Thermocells are garnering increasing attention as a promising thermoelectric technology for harvesting low-grade heat.However,their performance is often limited by the scarcity of high-performance redox couples that p...Thermocells are garnering increasing attention as a promising thermoelectric technology for harvesting low-grade heat.However,their performance is often limited by the scarcity of high-performance redox couples that possess both high thermopower and rapid redox kinetics.This work addresses this challenge by leveraging our recently developed copper(Ⅰ/Ⅱ)(Cu^(+)/Cu^(2+))redox couple.We significantly enhance the performance of Cu-based liquid thermocells by integrating a thermosensitive crystallization process with etched carbon cloth electrodes,achieving synergistic improvements in thermodynamic and kinetic performance.The thermosensitive crystallization process establishes a persistent Cu^(2+)concentration gradient,boosting the thermopower from 1.47 to 2.93 mV K^(-1).Moreover,the etched carbon cloth electrodes provide a larger electroactive surface area and demonstrate a higher current density.Consequently,the optimized Cu^(+)/Cu^(2+)system achieved an exceptional normalized power density P_(max)(ΔT)^(-2)of 3.97 mW m^(-2)K^(-2).A thermocell module comprised of 20 cells directly power various electronic devices at a temperature difference of 40 K.This work successfully exhibits potential of Cu^(+)/Cu^(2+)redox couple in thermoelectric conversion and introduces a valuable redox couple for highperformance thermocells.展开更多
Stars getting close enough to black holes(BHs)can be torn apart by strong tidal forces,producing electromagnetic flares.To date,more than 100 tidal disruption events(TDEs)have been observed,each involving invariably n...Stars getting close enough to black holes(BHs)can be torn apart by strong tidal forces,producing electromagnetic flares.To date,more than 100 tidal disruption events(TDEs)have been observed,each involving invariably normal gaseous stars whose debris falls onto the BH,sustaining the flares over years.White dwarfs(WDs),which are the most prevalent compact stars and a million times denser-and therefore tougher-than gaseous stars,can only be disrupted by intermediate-mass black holes(IMBHs)of 10^(2)–10^(5) solar masses.WD-TDEs are considered to generate more powerful and short-lived flares,but their evidence has been lacking.Here we report observations of a fast and luminous X-ray transient EP250702a detected by Einstein Probe.Its one-day-long X-ray peak as luminous as 10^(47−49) erg s^(−1) showed strong recurrent flares with hard spectra extending to several tens of MeV gamma-rays,as detected by Fermi/GBM and Konus-Wind,indicating relativistic jet emission.The jet's X-rays dropped sharply from 3×10^(49) erg s^(−1) to around 1044 erg s^(−1) within 20 days(10 days in the source rest frame).These characteristics are inconsistent with any previously known transient phenomena.We suggest that this fast-evolving event over the unprecedentedly short timescale arises likely from disruption of a WD by an IMBH.At late times,a soft component progressively dominates the X-ray spectrum,reaching a luminosity as high as 1044 erg s^(−1),which is consistent with being extreme super-Eddington emission from an accretion disk expected to form in an IMBH-WD TDE.WD-TDEs open a new window for investigating the elusive IMBHs and their surrounding stellar environments,and they are prime sources of gravitational waves in the band of space-based interferometers.展开更多
Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmen...Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.展开更多
Memory enables organisms to encode,store,and retrieve information essential for interacting with and adapting to a dynamic environment.As an internal representation of the external world,memory serves as a crucial bri...Memory enables organisms to encode,store,and retrieve information essential for interacting with and adapting to a dynamic environment.As an internal representation of the external world,memory serves as a crucial bridge between past experiences and future behaviors.However,the brain continuously forms new memories,raising the question of how new memories are integrated without disrupting previously formed ones.展开更多
This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation syste...This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems.The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters.When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems,this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses.The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus.The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources.A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbonneutral building operations.展开更多
Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adaptin...Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.展开更多
Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transformi...Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.展开更多
Objective:To explore the impact of exogenous chitosan on the growth and metabolism of Glycyrrhiza uralensis Fisch.(G.uralensis)and to improve the quality of cultivated G.uralensis for both medicine and food and aid in...Objective:To explore the impact of exogenous chitosan on the growth and metabolism of Glycyrrhiza uralensis Fisch.(G.uralensis)and to improve the quality of cultivated G.uralensis for both medicine and food and aid in the increase in the content of effective components in G.uralensis.Methods:In this study,whole G.uralensis plants were treated with exogenous chitosan,and compre-hensive analyses of secondary metabolites and proteins were conducted using liquid chromatography with tandem mass spectrometry and isobaric tag for relative and absolute quantitation,respectively.Effects of chitosan induction on endogenous hormones of G.uralensis were analyzed using an enzyme-linked immunosorbent assay.Gene ontology function annotation and Kyoto Encyclopedia of Genes and Genomes pathway annotation were conducted to study the effect of chitosan induction on the proteome.Results:Chitosan induction significantly increased the levels of flavonoids in G.uralensis;however,the variation in triterpenoids was not substantial.Biological processes,including photosynthesis,secondary metabolism,and abiotic stress responses,were significantly enriched.Additionally,the photosynthetic pathway,photosynthesis-antenna protein pathway,and plant hormone signal transduction pathway were significantly enriched.In the flavonoid biosynthesis pathway,the upstream-related enzyme phenylalanine ammonia-lyase(PAL)and the downstream-related enzymes chalcone synthase(CHS),polyketide reductase(PKR),chalcone isomerase(CHI),and vestitone reductase(VR)were significantly upregulated.Conclusions:Our findings suggest that chitosan induction may promote the tricarboxylic acid(TCA)cycle,and the TCA cycle enhancement significantly upregulated PAL,CHS,PKR,CHI,and VR,the five key enzymes involved in flavonoid synthesis of G.uralensis,indicating that chitosan induction activated the entire metabolic pathway associated with flavonoids in G.uralensis.Our findings provide a reference for improving the quality of cultivated G.uralensis from the perspective of pharmacodynamic components.展开更多
Migration is a potential strategy to reduce poverty in the Global South.In China,the Poverty-alleviation Relocation(PAR)is a government-led,large-scale migration initiative aimed at eliminating poverty and promoting e...Migration is a potential strategy to reduce poverty in the Global South.In China,the Poverty-alleviation Relocation(PAR)is a government-led,large-scale migration initiative aimed at eliminating poverty and promoting environmental sustainability.To examine the ecological and socio-economic effects of the PAR,we quantified the changes in five types of ecosystem services(ES)as well as the subjective well-being of rural residents in Fuping county,Hebei province of China,by using ES mapping,household survey,and semi-structured interviews.We found that the PAR improves people's quality of life,with the well-being scores associated with transportation,communication,education,and healthcare increasing by 0.45–0.81.Additionally,the PAR enhances the supply of ES,evidenced by the increases in four types of ES in both in-migration and out-migration areas.The ES growth rates in in-migration areas ranged from 0.7%to 3.9%,while in out-migration areas,the rates ranged from 0.4%to 2.5%.However,the changes in income and food well-being are minimal,with scores at 0 and 0.32,respectively.More importantly,the elderly and low-educated residents experience minimal improvements in well-being after relocation.Our findings suggest that for other developing countries seeking to adopt PAR,it is crucial to provide targeted support for livelihood transitions,particularly for marginalized social groups,restore out-migration areas,and strengthen cross-regional cooperation to better address ecological constraints on livelihoods.展开更多
The aim of this work is to find an alternative lubricating grease formulation that can be produced from renewable and biodegradable sources with minimal risks to human health and the environment.We used a castor oil a...The aim of this work is to find an alternative lubricating grease formulation that can be produced from renewable and biodegradable sources with minimal risks to human health and the environment.We used a castor oil and electrospun cellulose acetate propionate(CAp)as raw materials.We hypothesized that the acetyl and propionyl groups could provide an adequate chemical compatibility with the castor oil and that the electrospun nanostructures could enable improved physical stability by creating a variety of morphologies allowing the tailoring of the rheological and tribological properties of the resulting greases.The experimental results show that the use of electrospun CAp nanostructures can indeed yield physically stable formulations,even when used at low concentrations(3 wt%).The resulting dispersions went through structural transitions due to changes in the thickener morphologies and/or concentration,as shown by oscillatory rheology,oil holding capacity,tackiness,and lubrication performance in metal–metal contact.We found that the formulations,containing smooth or porous CAp nanofibers,at 5 wt%as a thickener,possess suitable rheological and tribological properties with a performance comparable to that of traditional lithium lubricating greases.展开更多
A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solidstate ultrasonic spot welding(USW)to evaluate the microstructure,tensile lap shear strengt...A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solidstate ultrasonic spot welding(USW)to evaluate the microstructure,tensile lap shear strength,and fatigue properties.The tensile strength increased with increasing welding energy,peaked at a welding energy of 1000 J,and then decreased due to the formation of an increasingly thick diffusion layer mainly containing Al12Mg17intermetallic compound at higher energy levels.The peak tensile lap shear strength attained at 1000 J was attributed to the optimal inter-diffusion between the magnesium alloy and softer AA1230-clad Al layer along with the presence of‘fishhook'-like mechanical interlocks at the weld interface and the formation of an indistinguishable intermetallic layer.The dissimilar joints welded at 1000 J also exhibited a longer fatigue life than other Mg-Al dissimilar joints,suggesting the beneficial role of the softer clad layer with a better intermingling capacity during USW.While the transverse-through-thickness(TTT)failure mode prevailed at lower cyclic loading levels,interfacial failure was the predominant mode of fatigue failure at higher cyclic loads,where distinctive fatigue striations were also observed on the fracture surface of the softer clad Al layer.This was associated with the presence of opening stress and bending moment near the nugget edge despite the tension-tension lap shear cyclic loading applied.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
Background:Cardiovascular disease remains the leading cause of mortality across the European region.Despite marked regional variations,cross-national differences in underlying risk factors have received comparatively ...Background:Cardiovascular disease remains the leading cause of mortality across the European region.Despite marked regional variations,cross-national differences in underlying risk factors have received comparatively little attention.Objective:To use European Social Survey,a unique cross-European dataset,to examine regional patterns in prevalence and lifestyle risks.Methods:This study employs clustering analysis and nested logistic modelling.Counterfactual analysis was conducted to illustrate how lifestyle modifications could reduce risk.Results:The prevalence of heart problems was highest in Latvia(25.6%,95%CI:23.0 to 28.2),Lithuania(17.6%,95%CI:15.5 to 19.7),and Bulgaria(14.9%,95%CI:13.4 to 19.4).Regionally,heart problems were higher in Northern and Eastern Europe(15%and 11.9%)than Western and Southern Europe(10.8%and 9.5%).Among the risk factors,modelling emphasised the importance of modifiable factors including education,body mass index and physical activity.Conclusion:The results underline that cardiovascular disease is influenced by interrelated socioeconomic,environmental and lifestyle determinants.Public policy interventions could be targeted at those countries where greatest reductions are obtainable and concentrate on interventions on those lifestyle traits identified.The study utilised a social science dataset,thereby illustrating how multidisciplinary resources can benefit epidemiological research.展开更多
Dissimilar joining of NiTi and stainless steel(SS)is important in biomedical applications but poses significant challenges due to brittle intermetallic compounds(IMCs)formation in the welds.Replacing harmful phases in...Dissimilar joining of NiTi and stainless steel(SS)is important in biomedical applications but poses significant challenges due to brittle intermetallic compounds(IMCs)formation in the welds.Replacing harmful phases in fusion welding cannot fully eliminate brittle IMCs and may introduce toxic elements,while the mixing restriction in solid-state welding increases the process complexity and results in large plastic deformation that degrades NiTi functional properties.In this work,we present a novel methodology that achieves a solid-state joined interface in NiTi-SS fusion welding(i.e.,resistance microwelding)through in-situ interfacial liquid control.By combining the advantages of both welding techniques,the current method produced NiTi-SS joints with superior strength,superelasticity and biocompatibility compared to NiTi joints or base metal.The ultrathin reaction layer at the solid-state joined interface contributed to a strong metallurgical bonding,while Joule heating effects and interfacial reactions enhanced superelasticity and biocompatibility of the joint.By demonstrating complete superelasticity on NiTi side,flexible deformation capacity on SS side,superior resistance to hydrogen embrittlement and electrochemical corrosion,and reduced Ni ion release and cytotoxicity,the welded joint shows great potential for the fabrication of multifunctional biomedical devices.Our work not only provides a comprehensive study of NiTi-SS joining under the biomedical background,but also introduces a new strategy for controlling material interface and dissimilar-metal welding process.展开更多
基金financially supported by research grants from Innovative Research Group Project of National Natural Science Foundation of China(52021004)the National Key Research and Development Program of China(2022YFB3803300)+1 种基金the National Natural Science Foundation of China(62474026,62205140,12204071)the China Postdoctoral Science Foundation(2022M710532)。
文摘Thermocells are garnering increasing attention as a promising thermoelectric technology for harvesting low-grade heat.However,their performance is often limited by the scarcity of high-performance redox couples that possess both high thermopower and rapid redox kinetics.This work addresses this challenge by leveraging our recently developed copper(Ⅰ/Ⅱ)(Cu^(+)/Cu^(2+))redox couple.We significantly enhance the performance of Cu-based liquid thermocells by integrating a thermosensitive crystallization process with etched carbon cloth electrodes,achieving synergistic improvements in thermodynamic and kinetic performance.The thermosensitive crystallization process establishes a persistent Cu^(2+)concentration gradient,boosting the thermopower from 1.47 to 2.93 mV K^(-1).Moreover,the etched carbon cloth electrodes provide a larger electroactive surface area and demonstrate a higher current density.Consequently,the optimized Cu^(+)/Cu^(2+)system achieved an exceptional normalized power density P_(max)(ΔT)^(-2)of 3.97 mW m^(-2)K^(-2).A thermocell module comprised of 20 cells directly power various electronic devices at a temperature difference of 40 K.This work successfully exhibits potential of Cu^(+)/Cu^(2+)redox couple in thermoelectric conversion and introduces a valuable redox couple for highperformance thermocells.
文摘Stars getting close enough to black holes(BHs)can be torn apart by strong tidal forces,producing electromagnetic flares.To date,more than 100 tidal disruption events(TDEs)have been observed,each involving invariably normal gaseous stars whose debris falls onto the BH,sustaining the flares over years.White dwarfs(WDs),which are the most prevalent compact stars and a million times denser-and therefore tougher-than gaseous stars,can only be disrupted by intermediate-mass black holes(IMBHs)of 10^(2)–10^(5) solar masses.WD-TDEs are considered to generate more powerful and short-lived flares,but their evidence has been lacking.Here we report observations of a fast and luminous X-ray transient EP250702a detected by Einstein Probe.Its one-day-long X-ray peak as luminous as 10^(47−49) erg s^(−1) showed strong recurrent flares with hard spectra extending to several tens of MeV gamma-rays,as detected by Fermi/GBM and Konus-Wind,indicating relativistic jet emission.The jet's X-rays dropped sharply from 3×10^(49) erg s^(−1) to around 1044 erg s^(−1) within 20 days(10 days in the source rest frame).These characteristics are inconsistent with any previously known transient phenomena.We suggest that this fast-evolving event over the unprecedentedly short timescale arises likely from disruption of a WD by an IMBH.At late times,a soft component progressively dominates the X-ray spectrum,reaching a luminosity as high as 1044 erg s^(−1),which is consistent with being extreme super-Eddington emission from an accretion disk expected to form in an IMBH-WD TDE.WD-TDEs open a new window for investigating the elusive IMBHs and their surrounding stellar environments,and they are prime sources of gravitational waves in the band of space-based interferometers.
基金supported by the National Key Research and Development Program of China(2023YFF0805602)National Natural Science Foundation of China(32225032,32001192,32271597)+1 种基金the Innovation Base Project of Gansu Province(2021YFF0703904)the Science and Technology Program of Gansu Province(24JRRA515,22JR5RA525,23JRRA1157).
文摘Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.
文摘Memory enables organisms to encode,store,and retrieve information essential for interacting with and adapting to a dynamic environment.As an internal representation of the external world,memory serves as a crucial bridge between past experiences and future behaviors.However,the brain continuously forms new memories,raising the question of how new memories are integrated without disrupting previously formed ones.
文摘This work proposes an adaptive quantum approximate optimization-based model predictive control(MPC)strategy for energy management in buildings equipped with battery energy storage and renewable energy generation systems.The learning-based parameter transfer scheme to realize adaptive quantum optimization leverages Bayesian optimization to predict initial quantum circuit parameters.When applied to the MPC problems formulated as quadratic unconstrained binary optimization problems,this approach computes optimal controls to minimize the net energy consumption levels in buildings and promotes decarbonization while reducing the computational efforts required for the quantum approximate optimization algorithm as the building energy system trajectory progresses.The energy efficiency and the decarbonization benefits of the proposed quantum optimization-based MPC strategy are demonstrated on buildings at the Cornell University campus.The proposed quantum computing-based technique to address MPC problems in buildings demonstrates energy-efficient and low-carbon building operation with a 6.8% improvement over deterministic MPC and presents opportunities for scaling to larger control problems with a significant reduction in utilized quantum computing resources.A reduction of 41.2% in carbon emissions is also achieved with the proposed control strategy facilitated by efficiently managing battery energy storage and renewable generation sources to promote a push toward carbonneutral building operations.
文摘Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.
基金funding support from the US National Science Foundation(2229092)supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship,a program of Schmidt Sciences,LLC.
文摘Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.
基金supported by grants from the National Natural Science Foundation of China(81773838).
文摘Objective:To explore the impact of exogenous chitosan on the growth and metabolism of Glycyrrhiza uralensis Fisch.(G.uralensis)and to improve the quality of cultivated G.uralensis for both medicine and food and aid in the increase in the content of effective components in G.uralensis.Methods:In this study,whole G.uralensis plants were treated with exogenous chitosan,and compre-hensive analyses of secondary metabolites and proteins were conducted using liquid chromatography with tandem mass spectrometry and isobaric tag for relative and absolute quantitation,respectively.Effects of chitosan induction on endogenous hormones of G.uralensis were analyzed using an enzyme-linked immunosorbent assay.Gene ontology function annotation and Kyoto Encyclopedia of Genes and Genomes pathway annotation were conducted to study the effect of chitosan induction on the proteome.Results:Chitosan induction significantly increased the levels of flavonoids in G.uralensis;however,the variation in triterpenoids was not substantial.Biological processes,including photosynthesis,secondary metabolism,and abiotic stress responses,were significantly enriched.Additionally,the photosynthetic pathway,photosynthesis-antenna protein pathway,and plant hormone signal transduction pathway were significantly enriched.In the flavonoid biosynthesis pathway,the upstream-related enzyme phenylalanine ammonia-lyase(PAL)and the downstream-related enzymes chalcone synthase(CHS),polyketide reductase(PKR),chalcone isomerase(CHI),and vestitone reductase(VR)were significantly upregulated.Conclusions:Our findings suggest that chitosan induction may promote the tricarboxylic acid(TCA)cycle,and the TCA cycle enhancement significantly upregulated PAL,CHS,PKR,CHI,and VR,the five key enzymes involved in flavonoid synthesis of G.uralensis,indicating that chitosan induction activated the entire metabolic pathway associated with flavonoids in G.uralensis.Our findings provide a reference for improving the quality of cultivated G.uralensis from the perspective of pharmacodynamic components.
基金National Natural Science Foundation of China,No.42361144859Team Construction Project of Faculty of Geographical Science,BNU,No.2024-JXTD-03,No.2024-KYTD-09The Beijing Normal University Tang Scholar,No.2021。
文摘Migration is a potential strategy to reduce poverty in the Global South.In China,the Poverty-alleviation Relocation(PAR)is a government-led,large-scale migration initiative aimed at eliminating poverty and promoting environmental sustainability.To examine the ecological and socio-economic effects of the PAR,we quantified the changes in five types of ecosystem services(ES)as well as the subjective well-being of rural residents in Fuping county,Hebei province of China,by using ES mapping,household survey,and semi-structured interviews.We found that the PAR improves people's quality of life,with the well-being scores associated with transportation,communication,education,and healthcare increasing by 0.45–0.81.Additionally,the PAR enhances the supply of ES,evidenced by the increases in four types of ES in both in-migration and out-migration areas.The ES growth rates in in-migration areas ranged from 0.7%to 3.9%,while in out-migration areas,the rates ranged from 0.4%to 2.5%.However,the changes in income and food well-being are minimal,with scores at 0 and 0.32,respectively.More importantly,the elderly and low-educated residents experience minimal improvements in well-being after relocation.Our findings suggest that for other developing countries seeking to adopt PAR,it is crucial to provide targeted support for livelihood transitions,particularly for marginalized social groups,restore out-migration areas,and strengthen cross-regional cooperation to better address ecological constraints on livelihoods.
基金sponsored by MCIN/AEI/10.13039/501100011033“ERDF A way of making Europe”(grant PID2021-125637OB-I00)FEDER/Junta de Andalucía Programmes(grants PY20_00751 and UHU202029).
文摘The aim of this work is to find an alternative lubricating grease formulation that can be produced from renewable and biodegradable sources with minimal risks to human health and the environment.We used a castor oil and electrospun cellulose acetate propionate(CAp)as raw materials.We hypothesized that the acetyl and propionyl groups could provide an adequate chemical compatibility with the castor oil and that the electrospun nanostructures could enable improved physical stability by creating a variety of morphologies allowing the tailoring of the rheological and tribological properties of the resulting greases.The experimental results show that the use of electrospun CAp nanostructures can indeed yield physically stable formulations,even when used at low concentrations(3 wt%).The resulting dispersions went through structural transitions due to changes in the thickener morphologies and/or concentration,as shown by oscillatory rheology,oil holding capacity,tackiness,and lubrication performance in metal–metal contact.We found that the formulations,containing smooth or porous CAp nanofibers,at 5 wt%as a thickener,possess suitable rheological and tribological properties with a performance comparable to that of traditional lithium lubricating greases.
基金the National Natural Science Foundation of China(Grant No.51971183)supported by OU(Osaka University,Japan)program for multilateral international collaboration research in joining and welding。
文摘A low rare-earth containing ZEK100-O magnesium alloy was welded to AA1230-clad high-strength AA2024-T3 aluminum alloy via solidstate ultrasonic spot welding(USW)to evaluate the microstructure,tensile lap shear strength,and fatigue properties.The tensile strength increased with increasing welding energy,peaked at a welding energy of 1000 J,and then decreased due to the formation of an increasingly thick diffusion layer mainly containing Al12Mg17intermetallic compound at higher energy levels.The peak tensile lap shear strength attained at 1000 J was attributed to the optimal inter-diffusion between the magnesium alloy and softer AA1230-clad Al layer along with the presence of‘fishhook'-like mechanical interlocks at the weld interface and the formation of an indistinguishable intermetallic layer.The dissimilar joints welded at 1000 J also exhibited a longer fatigue life than other Mg-Al dissimilar joints,suggesting the beneficial role of the softer clad layer with a better intermingling capacity during USW.While the transverse-through-thickness(TTT)failure mode prevailed at lower cyclic loading levels,interfacial failure was the predominant mode of fatigue failure at higher cyclic loads,where distinctive fatigue striations were also observed on the fracture surface of the softer clad Al layer.This was associated with the presence of opening stress and bending moment near the nugget edge despite the tension-tension lap shear cyclic loading applied.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
文摘Background:Cardiovascular disease remains the leading cause of mortality across the European region.Despite marked regional variations,cross-national differences in underlying risk factors have received comparatively little attention.Objective:To use European Social Survey,a unique cross-European dataset,to examine regional patterns in prevalence and lifestyle risks.Methods:This study employs clustering analysis and nested logistic modelling.Counterfactual analysis was conducted to illustrate how lifestyle modifications could reduce risk.Results:The prevalence of heart problems was highest in Latvia(25.6%,95%CI:23.0 to 28.2),Lithuania(17.6%,95%CI:15.5 to 19.7),and Bulgaria(14.9%,95%CI:13.4 to 19.4).Regionally,heart problems were higher in Northern and Eastern Europe(15%and 11.9%)than Western and Southern Europe(10.8%and 9.5%).Among the risk factors,modelling emphasised the importance of modifiable factors including education,body mass index and physical activity.Conclusion:The results underline that cardiovascular disease is influenced by interrelated socioeconomic,environmental and lifestyle determinants.Public policy interventions could be targeted at those countries where greatest reductions are obtainable and concentrate on interventions on those lifestyle traits identified.The study utilised a social science dataset,thereby illustrating how multidisciplinary resources can benefit epidemiological research.
基金Coherent Cu-rich nanoprecipitates:Achieving both high strength and superior magnetic properties in non-oriented silicon steels。
文摘Dissimilar joining of NiTi and stainless steel(SS)is important in biomedical applications but poses significant challenges due to brittle intermetallic compounds(IMCs)formation in the welds.Replacing harmful phases in fusion welding cannot fully eliminate brittle IMCs and may introduce toxic elements,while the mixing restriction in solid-state welding increases the process complexity and results in large plastic deformation that degrades NiTi functional properties.In this work,we present a novel methodology that achieves a solid-state joined interface in NiTi-SS fusion welding(i.e.,resistance microwelding)through in-situ interfacial liquid control.By combining the advantages of both welding techniques,the current method produced NiTi-SS joints with superior strength,superelasticity and biocompatibility compared to NiTi joints or base metal.The ultrathin reaction layer at the solid-state joined interface contributed to a strong metallurgical bonding,while Joule heating effects and interfacial reactions enhanced superelasticity and biocompatibility of the joint.By demonstrating complete superelasticity on NiTi side,flexible deformation capacity on SS side,superior resistance to hydrogen embrittlement and electrochemical corrosion,and reduced Ni ion release and cytotoxicity,the welded joint shows great potential for the fabrication of multifunctional biomedical devices.Our work not only provides a comprehensive study of NiTi-SS joining under the biomedical background,but also introduces a new strategy for controlling material interface and dissimilar-metal welding process.
