Atherosclerotic diseases is a diffuse process that involves the coronaries, carotids, renals and all other peripheral arteries owing to the systemic nature of atherosclerotic pathophysiology. This systemic precipitant...Atherosclerotic diseases is a diffuse process that involves the coronaries, carotids, renals and all other peripheral arteries owing to the systemic nature of atherosclerotic pathophysiology. This systemic precipitants that promote aggressive atherogenesis have been confirmed in multiple studies showing a relationship between atherosclerotic disease in one vascular bed with disease in another. However, the strength of this relationship varies from patient to patient. Thus, the practical utility of the diffuse nature of atheresclerosis is questionable. Ge and colleagues have proposed the use of left main (LM)coronary artery disease as a potential marker for left anterior descending (lAD) atherosclerotic disease. At first thought, this seems useless since the evaluation of the LM (by angiography or IVUS) can just as easily be performed in the LAD so why bother searching for such a surrogate? However, newer (non-invasive) imaging modalifies are making great gains and will be able to reliably image the LM sooner than the LAD (especially the distal LAD) so such a surrogate could have practical applications.展开更多
The Jain logic （Nay） addresses concerns elicited by sense experience of observable and measurable reality. Reality is what it is, and it exists independent of the observer. The last Jain Tirthankar Mahaveer suggeste...The Jain logic （Nay） addresses concerns elicited by sense experience of observable and measurable reality. Reality is what it is, and it exists independent of the observer. The last Jain Tirthankar Mahaveer suggested that organisms interact with such realities for survival needs and become concerned about the consequences. He suggested a code of conduct for reality-based behaviors to address concerns. Perceptions and impressions provide measures （praman） of information in sense experience, and with other evidence guide choices and decisions to act and bear consequences. Ethical behaviors rooted in reality have desirable consequences, and inconsistent and contradictory behaviors are undesirable consequences. Omniscience （God, Brahm） is discarded as a self-referential ad hoc construct inconsistent and contradictory to real world behaviors. This article is survey of assumptions and models to represent, interpret, and validate knowledge that begins with logical deduction for inference （anuman） based on evidence from sense experience （Jain 2011）. Secular and atheistic thrust of thought and practice encourages reasoning and open-ended search with affirmed assertions and independent evidence. Individual identity （atm） emerges with consistent behaviors to overcome fallibility and unreliability by minimizing doubt （Syad-Saptbhangi Nay）. The first Tirthankar Rishabh Nath （ca. 2700 BC） suggested that the content （sat） of real and abstract objects and concerns during a change is conserved as the net balance of the inputs and outputs （Tatia 1994）. Identity and content of assertions and evidence is also conserved during logical manipulations for reasoning. Each assertion and its negation are to be affirmed with independent evidence, and lack of evidence for presence is not necessarily the evidence for either non-absence or non-existence.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
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
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
Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genoty...Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[OR_(MEARDS)]=0.84,P=0.008;OR_(MESSI)=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10^(-4)),and improved overall survival in ARDS patients(hazard ratio[HR_(28-day)]=0.49,P=0.009)and sepsis patients(HR_(28-day)=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients.展开更多
Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at a vanishing chemical potential,we constructed a Bayesian holographic QCD model and systematically analyzed the th...Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at a vanishing chemical potential,we constructed a Bayesian holographic QCD model and systematically analyzed the thermodynamic properties of heavy quarkonium in QCD matter under varying temperatures and chemical potentials.We computed the quark-antiquark interquark distance,potential energy,entropy,binding energy,and internal energy.We present detailed posterior distribution results of the thermodynamic quantities of heavy quarkonium,including maximum a posteriori(MAP)value estimates and 95%confidence levels(CL).Through numerical simulations and theoretical analysis,we find that an increase in the temperature and chemical potential reduces the quark distance,thereby facilitating the dissociation of heavy quarkonium and leading to a suppressed potential energy.The increase in temperature and chemical potential also raises the entropy and entropy force,further accelerating the dissociation of heavy quarkonium.The calculated results of binding energy indicate that a higher temperature and chemical potential enhance the tendency of heavy quarkonium to dissociate into free quarks.The internal energy also increases with rising temperature and chemical potential.These findings provide significant theoretical insights into the properties of strongly interacting matter under extreme conditions and lay a solid foundation for the interpretation and validation of future experimental data.Finally,we also present the results for the free energy,entropy,and internal energy of a single quark.展开更多
In the words of the late Sir Colin Blakemore,neurologists have historically sought to infer brain functions in a manner akin to to king a hammer to a computeranalyzing localized anatomical lesions caused by trauma,tum...In the words of the late Sir Colin Blakemore,neurologists have historically sought to infer brain functions in a manner akin to to king a hammer to a computeranalyzing localized anatomical lesions caused by trauma,tumors,or strokes,noting deficits,and inferring what functions certain brain regions may be responsible for.This approach exemplifies a deletion heuristic,where the absence of a specific function reveals insights about the underlying structures or mechanisms responsible for it.By observing what is lost when a particular brain region is damaged,throughout the history of the field,neurologists have pieced together the intricate relationship between anatomy and function.展开更多
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,...A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.展开更多
Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the ...Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the scoring functions under high dimensional cases.We study the construction of confidence regions for the parameters in spatial autoregressive models with spatial autoregressive disturbances(SARAR models)with high dimension of parameters by using the NBEL method.It is shown that the NBEL ratio statistics are asymptoticallyχ^(2)-type distributed,which are used to obtain the NBEL based confidence regions for the parameters in SARAR models.A simulation study is conducted to compare the performances of the NBEL and the usual EL methods.展开更多
In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult...In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.展开更多
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h...1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.展开更多
The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arj...The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin,South India.Fluoride levels in the study area vary between 0.1 and 3.10 mg/L,with 32 samples exceeding the World Health Organization(WHO)standard of 1.5 mg/L.Hydrogeochemical analyses(Durov and Gibbs)clearly show that the overall water chemistry is primarily influenced by simple dissolution,mixing,and rock-water interactions,indicating that geogenic sources are the predominant contributors to fluoride in the study area.Around 446.5 km^(2)is considered at risk.In predictive analysis,five Machine Learning(ML)models were used,with the AdaBoost model performing better than the other models,achieving 96%accuracy and 4%error rate.The Traditional Health Risk Assessment(THRA)results indicate that 65%of samples pose highly susceptible for dental fluorosis,while 12%of samples pose highly susceptible for skeletal fluorosis in young age groups.The Fuzzy Inference System(FIS)model effectively manages ambiguity and linguistic factors,which are crucial when addressing health risks linked to groundwater fluoride contamination.In this model,input variables include fluoride concentration,individual age,and ingestion rate,while output variables consist of dental caries risk,dental fluorosis,and skeletal fluorosis.The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis,along with very young and young age groups.This study is an essential resource for local authorities,healthcare officials,and communities,aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment,aligning with Sustainable Development Goals(SDGs)3 and 6,thereby contributing to a cleaner and healthier society.展开更多
The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed confl...The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique.The results show that between 2000 and 2021,armed conflicts slowed overall SDG progress by 3.43%,equivalent to a setback of 18 years.The Middle East was the most affected region,with a 6.10%slowdown in progress.The impact of different types of conflict varies across specific goals:interstate conflicts primarily affect SDG 5(Gender Equality)and SDG 7(Affordable and Clean Energy),while intrastate conflicts have a larger impact on SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).Additionally,SDG 15(Life on Land)is severely affected by both types of conflict,with long-term consequences.As armed conflicts increase,the development progress would regress rapidly in a non-linear manner.To achieve the SDGs by 2030,it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.展开更多
Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overloo...Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overlooking instancewise complexity variations.Moreover,owing to pruning or decomposition techniques,the upper bound of network representation capabilities might be permanently diminished.In this work,an input-dependent multiscale dynamic inference method(MSDI)is developed to strike a better balance between model performance and inference acceleration.Specifically,we modify the main body of a convolutional network to obtain a series of parameter-sharing subnetworks with varying levels of complexity.A side branch structure is then introduced to assign an input instance to a suitable subnetwork as its inference route,and we expect to accelerate the inference by assigning the easy input to the subnetwork with low capacity.We further propose multiscale distillation training to optimize the training of the modified subnetworks.Additionally,we compare the entropy-based and learning-based grading approaches,aiming to obtain a more suitable route assignment method.Experiments show that MSDI can accelerate most existing convolutional models,achieving up to 74.7%computation savings across diverse datasets.展开更多
Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’...Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s edge.However,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models challenging.Additionally,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors.These factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these topics.Consequently,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using TinyML.It identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software frameworks.In the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning strategies.In the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major sectors.As a result,this article provides valuable insights for researchers,practitioners,and developers.It assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.展开更多
This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively i...This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively impacts cryptocurrencies.Moreover,the results indicate rapid divergence from counterfactual predictions,and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse.Cryptocurrency is reacting strongly to the uncertainty caused by insolvency.In relative terms,the collapse of FTX has been highly detrimental to Solana and Ethereum.Furthermore,the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred.FTX collapsed owing to a mismatch between the assets and liabilities.The industry is still mostly unregulated,and regulators must act quickly,highlighting the need for outstanding innovation and decentralized and trustless technology adoption.展开更多
In order to solve the problems of high experimental cost of ammunition,lack of field test data,and the difficulty in applying the ammunition hit probability estimation method in classical statistics,this paper assumes...In order to solve the problems of high experimental cost of ammunition,lack of field test data,and the difficulty in applying the ammunition hit probability estimation method in classical statistics,this paper assumes that the projectile dispersion of ammunition is a two-dimensional joint normal distribution,and proposes a new Bayesian inference method of ammunition hit probability based on normal-inverse Wishart distribution.Firstly,the conjugate joint prior distribution of the projectile dispersion characteristic parameters is determined to be a normal inverse Wishart distribution,and the hyperparameters in the prior distribution are estimated by simulation experimental data and historical measured data.Secondly,the field test data is integrated with the Bayesian formula to obtain the joint posterior distribution of the projectile dispersion characteristic parameters,and then the hit probability of the ammunition is estimated.Finally,compared with the binomial distribution method,the method in this paper can consider the dispersion information of ammunition projectiles,and the hit probability information is more fully utilized.The hit probability results are closer to the field shooting test samples.This method has strong applicability and is conducive to obtaining more accurate hit probability estimation results.展开更多
Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed ...Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.展开更多
文摘Atherosclerotic diseases is a diffuse process that involves the coronaries, carotids, renals and all other peripheral arteries owing to the systemic nature of atherosclerotic pathophysiology. This systemic precipitants that promote aggressive atherogenesis have been confirmed in multiple studies showing a relationship between atherosclerotic disease in one vascular bed with disease in another. However, the strength of this relationship varies from patient to patient. Thus, the practical utility of the diffuse nature of atheresclerosis is questionable. Ge and colleagues have proposed the use of left main (LM)coronary artery disease as a potential marker for left anterior descending (lAD) atherosclerotic disease. At first thought, this seems useless since the evaluation of the LM (by angiography or IVUS) can just as easily be performed in the LAD so why bother searching for such a surrogate? However, newer (non-invasive) imaging modalifies are making great gains and will be able to reliably image the LM sooner than the LAD (especially the distal LAD) so such a surrogate could have practical applications.
文摘The Jain logic （Nay） addresses concerns elicited by sense experience of observable and measurable reality. Reality is what it is, and it exists independent of the observer. The last Jain Tirthankar Mahaveer suggested that organisms interact with such realities for survival needs and become concerned about the consequences. He suggested a code of conduct for reality-based behaviors to address concerns. Perceptions and impressions provide measures （praman） of information in sense experience, and with other evidence guide choices and decisions to act and bear consequences. Ethical behaviors rooted in reality have desirable consequences, and inconsistent and contradictory behaviors are undesirable consequences. Omniscience （God, Brahm） is discarded as a self-referential ad hoc construct inconsistent and contradictory to real world behaviors. This article is survey of assumptions and models to represent, interpret, and validate knowledge that begins with logical deduction for inference （anuman） based on evidence from sense experience （Jain 2011）. Secular and atheistic thrust of thought and practice encourages reasoning and open-ended search with affirmed assertions and independent evidence. Individual identity （atm） emerges with consistent behaviors to overcome fallibility and unreliability by minimizing doubt （Syad-Saptbhangi Nay）. The first Tirthankar Rishabh Nath （ca. 2700 BC） suggested that the content （sat） of real and abstract objects and concerns during a change is conserved as the net balance of the inputs and outputs （Tatia 1994）. Identity and content of assertions and evidence is also conserved during logical manipulations for reasoning. Each assertion and its negation are to be affirmed with independent evidence, and lack of evidence for presence is not necessarily the evidence for either non-absence or non-existence.
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 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.
基金supported by the National Natural Science Foundation of China(Grant No.82220108002 to F.C.and Grant No.82273737 to R.Z.)the U.S.National Institutes of Health(Grant Nos.CA209414,HL060710,and ES000002 to D.C.C.,Grant Nos.CA209414 and CA249096 to Y.L.)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young Level Academic Leadership Training Program of Nanjing Medical University.
文摘Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[OR_(MEARDS)]=0.84,P=0.008;OR_(MESSI)=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10^(-4)),and improved overall survival in ARDS patients(hazard ratio[HR_(28-day)]=0.49,P=0.009)and sepsis patients(HR_(28-day)=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients.
基金supported in part by the National Key Research and Development Program of China(No.2022YFA1604900)the National Natural Science Foundation of China(NSFC)(Nos.12405154,12235016,12221005,12435009,12275104,92570117)+7 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB34030000)the Fundamental Research Funds for the Central UniversitiesOpen fund for Key Laboratories of the Ministry of Education(No.QLPL2024P01)CUHK-Shenzhen University Development Fund(Nos.UDF01003041 and UDF03003041)Shenzhen Peacock Fund(No.2023TC0007)Ministry of Science and Technology of China(No.2024YFA1611004)the European Union–Next Generation EU through the research(No.P2022Z4P4B)“SOPHYA-Sustainable Optimized PHYsics Algorithms:fundamental physics to build an advanced society”under the program PRIN 2022 PNRR of the Italian Ministero dell’Universitàe Ricerca(MUR)。
文摘Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at a vanishing chemical potential,we constructed a Bayesian holographic QCD model and systematically analyzed the thermodynamic properties of heavy quarkonium in QCD matter under varying temperatures and chemical potentials.We computed the quark-antiquark interquark distance,potential energy,entropy,binding energy,and internal energy.We present detailed posterior distribution results of the thermodynamic quantities of heavy quarkonium,including maximum a posteriori(MAP)value estimates and 95%confidence levels(CL).Through numerical simulations and theoretical analysis,we find that an increase in the temperature and chemical potential reduces the quark distance,thereby facilitating the dissociation of heavy quarkonium and leading to a suppressed potential energy.The increase in temperature and chemical potential also raises the entropy and entropy force,further accelerating the dissociation of heavy quarkonium.The calculated results of binding energy indicate that a higher temperature and chemical potential enhance the tendency of heavy quarkonium to dissociate into free quarks.The internal energy also increases with rising temperature and chemical potential.These findings provide significant theoretical insights into the properties of strongly interacting matter under extreme conditions and lay a solid foundation for the interpretation and validation of future experimental data.Finally,we also present the results for the free energy,entropy,and internal energy of a single quark.
文摘In the words of the late Sir Colin Blakemore,neurologists have historically sought to infer brain functions in a manner akin to to king a hammer to a computeranalyzing localized anatomical lesions caused by trauma,tumors,or strokes,noting deficits,and inferring what functions certain brain regions may be responsible for.This approach exemplifies a deletion heuristic,where the absence of a specific function reveals insights about the underlying structures or mechanisms responsible for it.By observing what is lost when a particular brain region is damaged,throughout the history of the field,neurologists have pieced together the intricate relationship between anatomy and function.
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
文摘A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.
基金Supported by the National Natural Science Foundation of China(12061017,12361055)the Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(22-A-01-01)。
文摘Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the scoring functions under high dimensional cases.We study the construction of confidence regions for the parameters in spatial autoregressive models with spatial autoregressive disturbances(SARAR models)with high dimension of parameters by using the NBEL method.It is shown that the NBEL ratio statistics are asymptoticallyχ^(2)-type distributed,which are used to obtain the NBEL based confidence regions for the parameters in SARAR models.A simulation study is conducted to compare the performances of the NBEL and the usual EL methods.
基金supported by the National Natural Science Foundation of China(No.62201079)the Beijing Natural Science Foundation(No.L232051).
文摘In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.
基金supported by the National Key Research and Development Program of China(2022YFB2703503)the National Natural Science Foundation of China(62293501,62525210,and 62293502)the China Scholarship Council(202306280318).
文摘1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.
基金the Anusandhan National Research Foundation(ANRF),New Delhi[Erstwhile,Science and Engineering Research Board(SERB)]Department of Science and Technology(DST)(Government of India)(File No.:CRG/2022/002618 Dated:22.08.2023)for providing the grant and support to carry out this work effectively.
文摘The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin,South India.Fluoride levels in the study area vary between 0.1 and 3.10 mg/L,with 32 samples exceeding the World Health Organization(WHO)standard of 1.5 mg/L.Hydrogeochemical analyses(Durov and Gibbs)clearly show that the overall water chemistry is primarily influenced by simple dissolution,mixing,and rock-water interactions,indicating that geogenic sources are the predominant contributors to fluoride in the study area.Around 446.5 km^(2)is considered at risk.In predictive analysis,five Machine Learning(ML)models were used,with the AdaBoost model performing better than the other models,achieving 96%accuracy and 4%error rate.The Traditional Health Risk Assessment(THRA)results indicate that 65%of samples pose highly susceptible for dental fluorosis,while 12%of samples pose highly susceptible for skeletal fluorosis in young age groups.The Fuzzy Inference System(FIS)model effectively manages ambiguity and linguistic factors,which are crucial when addressing health risks linked to groundwater fluoride contamination.In this model,input variables include fluoride concentration,individual age,and ingestion rate,while output variables consist of dental caries risk,dental fluorosis,and skeletal fluorosis.The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis,along with very young and young age groups.This study is an essential resource for local authorities,healthcare officials,and communities,aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment,aligning with Sustainable Development Goals(SDGs)3 and 6,thereby contributing to a cleaner and healthier society.
基金supported and funded by the Special Research Assistant Program of Chinese Academy of Sciences(Grant No.E3S30015Y5)Maria de Maeztu excellence accreditation 2023–2026(Ref.CEX2021-001201-M),funded by MCIN/AEI/10.13039/501100011033。
文摘The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique.The results show that between 2000 and 2021,armed conflicts slowed overall SDG progress by 3.43%,equivalent to a setback of 18 years.The Middle East was the most affected region,with a 6.10%slowdown in progress.The impact of different types of conflict varies across specific goals:interstate conflicts primarily affect SDG 5(Gender Equality)and SDG 7(Affordable and Clean Energy),while intrastate conflicts have a larger impact on SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).Additionally,SDG 15(Life on Land)is severely affected by both types of conflict,with long-term consequences.As armed conflicts increase,the development progress would regress rapidly in a non-linear manner.To achieve the SDGs by 2030,it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.
文摘Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overlooking instancewise complexity variations.Moreover,owing to pruning or decomposition techniques,the upper bound of network representation capabilities might be permanently diminished.In this work,an input-dependent multiscale dynamic inference method(MSDI)is developed to strike a better balance between model performance and inference acceleration.Specifically,we modify the main body of a convolutional network to obtain a series of parameter-sharing subnetworks with varying levels of complexity.A side branch structure is then introduced to assign an input instance to a suitable subnetwork as its inference route,and we expect to accelerate the inference by assigning the easy input to the subnetwork with low capacity.We further propose multiscale distillation training to optimize the training of the modified subnetworks.Additionally,we compare the entropy-based and learning-based grading approaches,aiming to obtain a more suitable route assignment method.Experiments show that MSDI can accelerate most existing convolutional models,achieving up to 74.7%computation savings across diverse datasets.
文摘Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s edge.However,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models challenging.Additionally,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors.These factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these topics.Consequently,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using TinyML.It identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software frameworks.In the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning strategies.In the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major sectors.As a result,this article provides valuable insights for researchers,practitioners,and developers.It assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.
文摘This study uses the Bayesian structural model to assess the causal effect of the futures exchange(FTX)insolvency on cryptocurrencies from October 2022 to December 14,2022.Findings show that FTX insolvency negatively impacts cryptocurrencies.Moreover,the results indicate rapid divergence from counterfactual predictions,and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse.Cryptocurrency is reacting strongly to the uncertainty caused by insolvency.In relative terms,the collapse of FTX has been highly detrimental to Solana and Ethereum.Furthermore,the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred.FTX collapsed owing to a mismatch between the assets and liabilities.The industry is still mostly unregulated,and regulators must act quickly,highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
基金supported by the National Natural Science Foundation of China(No.71501183).
文摘In order to solve the problems of high experimental cost of ammunition,lack of field test data,and the difficulty in applying the ammunition hit probability estimation method in classical statistics,this paper assumes that the projectile dispersion of ammunition is a two-dimensional joint normal distribution,and proposes a new Bayesian inference method of ammunition hit probability based on normal-inverse Wishart distribution.Firstly,the conjugate joint prior distribution of the projectile dispersion characteristic parameters is determined to be a normal inverse Wishart distribution,and the hyperparameters in the prior distribution are estimated by simulation experimental data and historical measured data.Secondly,the field test data is integrated with the Bayesian formula to obtain the joint posterior distribution of the projectile dispersion characteristic parameters,and then the hit probability of the ammunition is estimated.Finally,compared with the binomial distribution method,the method in this paper can consider the dispersion information of ammunition projectiles,and the hit probability information is more fully utilized.The hit probability results are closer to the field shooting test samples.This method has strong applicability and is conducive to obtaining more accurate hit probability estimation results.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204396,82304491,and 82400511).
文摘Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.
