Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problemat...It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.展开更多
AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 201...AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)da...AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.展开更多
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h...Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.展开更多
This paper studies high order compact finite volume methods on non-uniform meshes for one-dimensional elliptic and parabolic differential equations with the Robin boundary conditions.An explicit scheme and an implicit...This paper studies high order compact finite volume methods on non-uniform meshes for one-dimensional elliptic and parabolic differential equations with the Robin boundary conditions.An explicit scheme and an implicit scheme are obtained by discretizing the equivalent integral form of the equation.For the explicit scheme with nodal values,the algebraic system can be solved by the Thomas method.For the implicit scheme with both nodal values and their derivatives,the system can be implemented by a prediction-correction procedure,where in the correction stage,an implicit formula for recovering the nodal derivatives is introduced.Taking two point boundary value problem as an example,we prove that both the explicit and implicit schemes are convergent with fourth order accuracy with respect to some standard discrete norms using the energy method.Two numerical examples demonstrate the correctness and effectiveness of the schemes,as well as the indispensability of using non-uniform meshes.展开更多
When a ceramic ionic-crystal nanocluster is group-substituted with polymer chain segments to form an ionomeric aggregate,is the ordered structure maintained within the sterically hindered nanocluster?We observed,for N...When a ceramic ionic-crystal nanocluster is group-substituted with polymer chain segments to form an ionomeric aggregate,is the ordered structure maintained within the sterically hindered nanocluster?We observed,for Na-salt sulfonated polystyrene ionomer,the electron-diffraction lattice fringes of the nanoclusters,which proved their internal crystalline ordering driven by electrostatic attractions overcoming steric hindrance.Kinetically,the nanoclusters'enhanced melting endotherm upon aging indicate their quasi-,slow-ordering character.Extended tight binding molecular dynamics simulations provide an insight into the mechanism underlying the ionic-group aggregation during nanoclustering.We hence proposed an uncommon state of order,polymer-bound ceramic quasicrystal,supplementary to the order phenomena in crystalline ceramics.展开更多
Molecular dynamics simulations were carried out to study the effect of chemical short-range order(CSRO)on the primary radiation damage in TiVTaNb high-entropy alloys(HEAs).We have performed displacement cascade simula...Molecular dynamics simulations were carried out to study the effect of chemical short-range order(CSRO)on the primary radiation damage in TiVTaNb high-entropy alloys(HEAs).We have performed displacement cascade simulations to explore the CSRO effect on the generation and evolution behaviors of irradiation defects.The results demonstrate that CSRO can suppress the formation of Frenkel pairs in TiVTaNb HEAs,with the suppression effect becoming more pronounced as the degree of CSRO increases.CSRO can change the types of interstitial defects generated during cascade collisions.Specifically,as the degree of CSRO increases,the proportion of Ti-related interstitials shows a marked enhancement,primarily evidenced by a significant rise in Ti–Ti dumbbells accompanied by a corresponding decrease in Ti–V dumbbells.CSRO exhibits negligible influence on defect clustering and the nucleation and evolution of dislocation loops.Regardless of CSRO conditions,TiVTaNb HEAs preserve exceptional radiation tolerance throughout the cascade damage process,suggesting that the intrinsic properties of this multi-principal element system dominate its radiation response.These findings provide fundamental insights into the CSRO effect on defect formation and evolution behaviors in HEAs,which may provide new design strategies for high-entropy alloys.展开更多
While 2D/3D heterostructures are widely employed to improve the stability of perovskite optoelectronic devices,their effectiveness is fundamentally governed by the crystallinity of the interfacial structure -a factor ...While 2D/3D heterostructures are widely employed to improve the stability of perovskite optoelectronic devices,their effectiveness is fundamentally governed by the crystallinity of the interfacial structure -a factor often overlooked.Disordered interfaces exhibit thermodynamic metastability,where ion diffusion induces sequential phase transitions from low-n to high-n phases.Here,we construct atomically ordered 2D/3D interfaces using phase-pure 2D perovskite capping layers,which reduce the interfacial phase transition rate by 95%and effectively suppress ion migration.As a result,devices exhibit outstanding operational stability,retaining over 99%of their initial power conversion efficiency after 1500 h of continuous operation,along with excellent thermal durability at 85℃.These findings identify interfacial order as a critical parameter for regulating ion dynamics and phase behavior,providing a robust design principle for achieving high-efficiency,long-lifetime perovskite technologies.展开更多
To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and...To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system.展开更多
Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims...Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims We aim to assess shared genetics and potential associations between ADHD and T2D.Methods We performed genetic correlation,two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D.The genome-wide association study(GWAS)summary results of T2D(80154 cases and 853816 controls),ADHD2019(20183 cases and 35191 controls from the 2019 GWAS ADHD dataset)and ADHD2022(38691 cases and 275986 controls from the 2022 GWAS ADHD dataset)were used for the analyses.The T2D dataset was obtained from the DIAGRAM Consortium.The ADHD datasets were obtained from the Psychiatric Genomics Consortium.We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset.Moreover,molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.Results T2D has positive genetic correlations with ADHD2019(rg=0.33)and ADHD2022(rg=0.31).Genetic liability to ADHD2019 was associated with an increased risk for T2D(odds ratio(OR):1.30,p<0.001),while genetic liability to ADHD2022 had a suggestive causal effect on T2D(OR:1.30,p=0.086).Genetic liability to T2D was associated with a higher risk for ADHD2019(OR:1.05,p=0.001)and ADHD2022(OR:1.03,p<0.001).The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022.T2D and ADHD2022 have three overlapping loci.Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.Conclusions Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.展开更多
The quantum effect plays an important role in quantum thermodynamics,and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention.Based on two trapped ions,we propo...The quantum effect plays an important role in quantum thermodynamics,and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention.Based on two trapped ions,we propose a scheme to add an indefinite causal order to the isochoric cooling stroke of an Otto engine through reservoir engineering.Then,we observe that the quasi-static efficiency of this heat engine is far beyond the efficiency of a normal Otto heat engine and may reach one.When the power is its maximum,the efficiency is also much higher than that of a normal Otto heat engine.This enhancement may originate from the nonequilibrium of the reservoir and the measurement on the control qubit.展开更多
Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ...Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.展开更多
Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying...Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.展开更多
The synchrotron radiation beamline BL17B of the National Facility for Protein Science(NFPS)in Shanghai,situated at the Shanghai Synchrotron Radiation Facility(SSRF),was originally designed for diffraction experiments ...The synchrotron radiation beamline BL17B of the National Facility for Protein Science(NFPS)in Shanghai,situated at the Shanghai Synchrotron Radiation Facility(SSRF),was originally designed for diffraction experiments and accommodates techniques including single-crystal diffraction,powder diffraction,and grazing-incidence wide-angle X-ray scattering(GIWAXS)to enable the characterization of long-range ordered atomic structures.The academic community associated with BL17B engages in research domains encompassing biology,environment,energy,and materials,and a pronounced demand for characterizing short-range ordered structures exists.To address these requirements,BL17B established an advanced X-ray absorption fine structure(XAFS)experimental platform that enabled it to address a wide range of systems,from crystalline to amorphous and from long-range order to short-range order.The XAFS platform allows simultaneous XAFS data acquisition for both the transmission and fluorescence modes within an energy range of 5-23 keV,encompassing the K-edges of titanium to ruthenium and the L3-edges of cesium to bismuth.The platform exemplifies high levels of automation achieved through automated sample assessment and data collection based on large-capacity sample wheels that facilitate remote sample loading.When integrated with a highly integrated control system that simplifies experimental preparation and data collection,the XAFS platform significantly bolsters experimental efficiency and enhances user experience.Notably,the platform boasts an impressively low extended X-ray absorption fine structure(EXAFS)detection limit of 0.04 wt%for dilute copper phthalocyanine(CuPc)samples and an even more remarkable X-ray absorption near edge structure(XANES)detection threshold of 0.01 wt%.These results demonstrate the methodology?s reliability in low-concentration sample analysis,confirming its capability to generate high-quality XAFS data.展开更多
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.展开更多
To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-posi...To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.展开更多
The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this stu...The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.展开更多
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
基金supported by University Grant Agency of Matej Bel University in Banská Bystrica project number UGA-14-PDS-2025.
文摘It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.
基金Supported by the National Natural Science Foundation of China(No.82371104)the Natural Science Foundation of Hunan Province(No.2023JJ30851).
文摘AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
文摘AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00518960)in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00563192).
文摘Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.
文摘This paper studies high order compact finite volume methods on non-uniform meshes for one-dimensional elliptic and parabolic differential equations with the Robin boundary conditions.An explicit scheme and an implicit scheme are obtained by discretizing the equivalent integral form of the equation.For the explicit scheme with nodal values,the algebraic system can be solved by the Thomas method.For the implicit scheme with both nodal values and their derivatives,the system can be implemented by a prediction-correction procedure,where in the correction stage,an implicit formula for recovering the nodal derivatives is introduced.Taking two point boundary value problem as an example,we prove that both the explicit and implicit schemes are convergent with fourth order accuracy with respect to some standard discrete norms using the energy method.Two numerical examples demonstrate the correctness and effectiveness of the schemes,as well as the indispensability of using non-uniform meshes.
基金Funded by the Hubei Province Key Research Foundation for Water Resources,China(No.HBSLKY2023035)as well as by the Technology Foundation for Selected Overseas Scholars,Ministry of Human Resources and Social Security,China(No.[2013]277)+2 种基金the Natural Science Foundation of the Hubei Province of China(No.2014CFA094)the Overseas High-level Talents Scientific-research Starting Fund of Hubei University of Technology,China(HBUTscience-[2005]2)the National Natural Science Foundation of China(No.51703053)。
文摘When a ceramic ionic-crystal nanocluster is group-substituted with polymer chain segments to form an ionomeric aggregate,is the ordered structure maintained within the sterically hindered nanocluster?We observed,for Na-salt sulfonated polystyrene ionomer,the electron-diffraction lattice fringes of the nanoclusters,which proved their internal crystalline ordering driven by electrostatic attractions overcoming steric hindrance.Kinetically,the nanoclusters'enhanced melting endotherm upon aging indicate their quasi-,slow-ordering character.Extended tight binding molecular dynamics simulations provide an insight into the mechanism underlying the ionic-group aggregation during nanoclustering.We hence proposed an uncommon state of order,polymer-bound ceramic quasicrystal,supplementary to the order phenomena in crystalline ceramics.
基金Project supported by the Youth Program of the National Natural Science Foundation of China(Grant No.12405324)the CNNC Science Fund for Talented Young Scholars(Grant No.24940)the CNNC Basic Science Fund(Grant No.24851)。
文摘Molecular dynamics simulations were carried out to study the effect of chemical short-range order(CSRO)on the primary radiation damage in TiVTaNb high-entropy alloys(HEAs).We have performed displacement cascade simulations to explore the CSRO effect on the generation and evolution behaviors of irradiation defects.The results demonstrate that CSRO can suppress the formation of Frenkel pairs in TiVTaNb HEAs,with the suppression effect becoming more pronounced as the degree of CSRO increases.CSRO can change the types of interstitial defects generated during cascade collisions.Specifically,as the degree of CSRO increases,the proportion of Ti-related interstitials shows a marked enhancement,primarily evidenced by a significant rise in Ti–Ti dumbbells accompanied by a corresponding decrease in Ti–V dumbbells.CSRO exhibits negligible influence on defect clustering and the nucleation and evolution of dislocation loops.Regardless of CSRO conditions,TiVTaNb HEAs preserve exceptional radiation tolerance throughout the cascade damage process,suggesting that the intrinsic properties of this multi-principal element system dominate its radiation response.These findings provide fundamental insights into the CSRO effect on defect formation and evolution behaviors in HEAs,which may provide new design strategies for high-entropy alloys.
基金funding supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB1140000)National Natural Science Foundation of China(22379156,U23A20141)+1 种基金Qingdao New Energy Shandong Laboratory(QIBEBT/SEI/QNESL S202305)Key R&D Program of Shandong Province,China(2024SFGC0102)。
文摘While 2D/3D heterostructures are widely employed to improve the stability of perovskite optoelectronic devices,their effectiveness is fundamentally governed by the crystallinity of the interfacial structure -a factor often overlooked.Disordered interfaces exhibit thermodynamic metastability,where ion diffusion induces sequential phase transitions from low-n to high-n phases.Here,we construct atomically ordered 2D/3D interfaces using phase-pure 2D perovskite capping layers,which reduce the interfacial phase transition rate by 95%and effectively suppress ion migration.As a result,devices exhibit outstanding operational stability,retaining over 99%of their initial power conversion efficiency after 1500 h of continuous operation,along with excellent thermal durability at 85℃.These findings identify interfacial order as a critical parameter for regulating ion dynamics and phase behavior,providing a robust design principle for achieving high-efficiency,long-lifetime perovskite technologies.
基金funded by National Key Research and Development Program Projects of China under Grant No.2020YFB1713500.
文摘To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system.
文摘Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims We aim to assess shared genetics and potential associations between ADHD and T2D.Methods We performed genetic correlation,two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D.The genome-wide association study(GWAS)summary results of T2D(80154 cases and 853816 controls),ADHD2019(20183 cases and 35191 controls from the 2019 GWAS ADHD dataset)and ADHD2022(38691 cases and 275986 controls from the 2022 GWAS ADHD dataset)were used for the analyses.The T2D dataset was obtained from the DIAGRAM Consortium.The ADHD datasets were obtained from the Psychiatric Genomics Consortium.We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset.Moreover,molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.Results T2D has positive genetic correlations with ADHD2019(rg=0.33)and ADHD2022(rg=0.31).Genetic liability to ADHD2019 was associated with an increased risk for T2D(odds ratio(OR):1.30,p<0.001),while genetic liability to ADHD2022 had a suggestive causal effect on T2D(OR:1.30,p=0.086).Genetic liability to T2D was associated with a higher risk for ADHD2019(OR:1.05,p=0.001)and ADHD2022(OR:1.03,p<0.001).The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022.T2D and ADHD2022 have three overlapping loci.Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.Conclusions Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.
基金supported by National Natural Science Foundation of China under Grant No.11965012Yunan Province’s Hi-tech Talents Recruitment Plan No.YNWR-QNBJ-2019-245。
文摘The quantum effect plays an important role in quantum thermodynamics,and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention.Based on two trapped ions,we propose a scheme to add an indefinite causal order to the isochoric cooling stroke of an Otto engine through reservoir engineering.Then,we observe that the quasi-static efficiency of this heat engine is far beyond the efficiency of a normal Otto heat engine and may reach one.When the power is its maximum,the efficiency is also much higher than that of a normal Otto heat engine.This enhancement may originate from the nonequilibrium of the reservoir and the measurement on the control qubit.
基金supported by National Natural Science Foundation of China Joint Fund for Enterprise Innovation Development(U23B2029)National Natural Science Foundation of China(62076167,61772020)+1 种基金Key Scientific Research Project of Higher Education Institutions in Henan Province(24A520058,24A520060,23A520022)Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2024AL053).
文摘Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.
文摘Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.
基金supported by the Chinese Academy of Science(CAS)Key Technology Talent Program(No.2021000022)。
文摘The synchrotron radiation beamline BL17B of the National Facility for Protein Science(NFPS)in Shanghai,situated at the Shanghai Synchrotron Radiation Facility(SSRF),was originally designed for diffraction experiments and accommodates techniques including single-crystal diffraction,powder diffraction,and grazing-incidence wide-angle X-ray scattering(GIWAXS)to enable the characterization of long-range ordered atomic structures.The academic community associated with BL17B engages in research domains encompassing biology,environment,energy,and materials,and a pronounced demand for characterizing short-range ordered structures exists.To address these requirements,BL17B established an advanced X-ray absorption fine structure(XAFS)experimental platform that enabled it to address a wide range of systems,from crystalline to amorphous and from long-range order to short-range order.The XAFS platform allows simultaneous XAFS data acquisition for both the transmission and fluorescence modes within an energy range of 5-23 keV,encompassing the K-edges of titanium to ruthenium and the L3-edges of cesium to bismuth.The platform exemplifies high levels of automation achieved through automated sample assessment and data collection based on large-capacity sample wheels that facilitate remote sample loading.When integrated with a highly integrated control system that simplifies experimental preparation and data collection,the XAFS platform significantly bolsters experimental efficiency and enhances user experience.Notably,the platform boasts an impressively low extended X-ray absorption fine structure(EXAFS)detection limit of 0.04 wt%for dilute copper phthalocyanine(CuPc)samples and an even more remarkable X-ray absorption near edge structure(XANES)detection threshold of 0.01 wt%.These results demonstrate the methodology?s reliability in low-concentration sample analysis,confirming its capability to generate high-quality XAFS data.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
基金The National Natural Science Foundation of China (No. 51908107)。
文摘To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.
基金supported by the National Natural Science Foundation of China(Grant Nos.12022508,12074394,and 22125604)Shanghai Supercomputer Center of ChinaShanghai Snowlake Technology Co.Ltd.
文摘The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.
