This paper reports a laboratory investigation of the fuel injection process in a diesel engine.The atomization process of the considered fuel(a hydrocarbon liquid)and the ensuing mixing with air is studied experimenta...This paper reports a laboratory investigation of the fuel injection process in a diesel engine.The atomization process of the considered fuel(a hydrocarbon liquid)and the ensuing mixing with air is studied experimentally under high-pressure conditions.Different types of injector nozzles are examined,including(two)new configurations,which are compared in terms of performances to a standard injector manufactured by the Bosch company.For the two alternate configurations,the intake edges of one atomizing hole(hole No.1)are located in the sack volume while for the other(hole No.2)they are located on the locking cone of the needle valve.The injection process,the fuel atomization fineness and fuel supply speed characteristics are studied as functions of high-pressure fuel pump camshaft speed and rotation angle.The results obtained show that a decrease in the high-pressure fuel pump camshaft speed can produce fuel redistribution depending on the injector operation.In general,however,the hole No.1 can ensure fuel flow with higher speed with respect to the hole No.2 for all the operation modes of the injector.Based on such an analysis,we conclude that the use of certain injectors can enable a fine tuning of the propagation process of fuel sprays into various areas of the diesel engine combustion chamber.展开更多
The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of...The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.展开更多
This study is the investigation of the microstructure of different types of carbon fiber. They were compared with the carbonized and graphitized fibers. Results of structural researches have been presented. It was fou...This study is the investigation of the microstructure of different types of carbon fiber. They were compared with the carbonized and graphitized fibers. Results of structural researches have been presented. It was found that the damage varies from different pollution and the damage of the monofibers. The effect of the pollution of the monofiber was determined.展开更多
Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designi...Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance.However,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning.To alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural information.Considering that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its curvature.Our DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data.On several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.展开更多
The article is devoted to surface hardening of steels by alloying with the use of laser energy. Two combined technologies were proposed: first—laser alloying by nitride-forming elements followed by nitriding, and sec...The article is devoted to surface hardening of steels by alloying with the use of laser energy. Two combined technologies were proposed: first—laser alloying by nitride-forming elements followed by nitriding, and second—the local laser alloying followed by metallization in atmosphere of ammonia. It is shown that laser alloying in continuous radiation forms a layer with a homogeneous fine-grained structure with thickness of 600 microns. The subsequent nitriding increases the microhardness of the surface layer of low-carbon steels to 20,000 MPa, increases wear-resistance in a 3 - 15 times and crack resistance in a 1.5 times. Two-stage technology of metallization allows getting diffusion layer on the surface of steels with the thickness, which is 1.5 - 2 times higher than after traditional metallization. In addition, this method of surface modification can significantly reduce the temperature of diffusion metallization and reduce the processing time to 3 hours. The optimal regimes of both technologies, which provide homogeneous multiphase diffusion layers with high hardness and wear resistance, were determined.展开更多
Phenomenon of localized surface plasmon excitation at nanostructured materials has attracted much attention in recent decades for their wide applications in single molecule detection,surface-enhanced Raman spectroscop...Phenomenon of localized surface plasmon excitation at nanostructured materials has attracted much attention in recent decades for their wide applications in single molecule detection,surface-enhanced Raman spectroscopy and nano-plasmonics.In addition to the excitation by external light field,an electron beam can also induce the local surface plasmon excitation.Nowadays,electron energy loss spectroscopy(EELS)technique has been increasingly employed in experiment to investigate the surface excitation characteristics of metallic nanoparticles.However,a present theoretical analysis tool for electromagnetic analysis based on the discrete dipole approximation(DDA)method can only treat the case of excitation by light field.In this work we extend the DDA method for the calculation of EELS spectrum for arbitary nanostructured materials.We have simulated EELS spectra for different incident locations of an electron beam on a single silver nanoparticle,the simulated results agree with an experimental measurement very well.The present method then provides a computation tool for study of the local surface plasmon excitation of metallic nanoparticles induced by an electron beam.展开更多
BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a we...BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a well-established treatment for FI.Given the increased need of magnetic resonance imaging(MRI)for diagnostics,the In-terStim which was previously used in sacral nerve stimulation was limited by MRI incompatibility.Medtronic MRI-compatible InterStim was approved by the United States Food and Drug Administration in August 2020 and has been widely used.AIM To evaluate the efficacy,outcomes and complications of the MRI-compatible InterStim.METHODS Data of patients who underwent MRI-compatible Medtronic InterStim placement at UPMC Williamsport,University of Minnesota,Advocate Lutheran General Hospital,and University of Wisconsin-Madison was pooled and analyzed.Patient demographics,clinical features,surgical techniques,complications,and outcomes were analyzed.Strengthening the Reporting of Observational studies in Epidemiology(STROBE)cross-sectional reporting guidelines were used.RESULTS Seventy-three patients had the InterStim implanted.The mean age was 63.29±12.2 years.Fifty-seven(78.1%)patients were females and forty-two(57.5%)patients had diabetes.In addition to incontinence,overlapping symptoms included diarrhea(23.3%),fecal urgency(58.9%),and urinary incontinence(28.8%).Fifteen(20.5%)patients underwent Peripheral Nerve Evaluation before proceeding to definite implant placement.Thirty-two(43.8%)patients underwent rechargeable InterStim placement.Three(4.1%)patients needed removal of the implant.Migration of the external lead connection was observed in 7(9.6%)patients after the stage I procedure.The explanation for one patient was due to infection.Seven(9.6%)patients had other complications like nerve pain,hematoma,infection,lead fracture,and bleeding.The mean follow-up was 6.62±3.5 mo.Sixty-eight(93.2%)patients reported significant improvement of symptoms on follow-up evaluation.CONCLUSION This study shows promising results with significant symptom improvement,good efficacy and good patient outcomes with low complication rates while using MRI compatible InterStim for FI.Further long-term follow-up and future studies with a larger patient population is recommended.展开更多
The authors discuss contradictions between the principal branches of the modern physical picture of the universe. Space and time have been shown in the Unitary Quantum Theory (UQT) not to be connected one with the oth...The authors discuss contradictions between the principal branches of the modern physical picture of the universe. Space and time have been shown in the Unitary Quantum Theory (UQT) not to be connected one with the other, unlike in the Special Theory of Relativity. In UQT, time becomes Newtonian again, and the growth of the particle’s mass with growing speed proceeds from other considerations of physics. Unlike the quantum theory, the modern gravitation theory (the general theory of relativity) is not confirmed by experiments and needs to be considerably revised.展开更多
<span style="font-family:Verdana;">Epilepsy is a chronic and the fourth most common neurological disorder which affects people of all age groups. Recently research and awareness on epilepsy-related dea...<span style="font-family:Verdana;">Epilepsy is a chronic and the fourth most common neurological disorder which affects people of all age groups. Recently research and awareness on epilepsy-related deaths have rapidly grown over the past two decades. Many previous studies are attributed to the guidelines that apprise health care professionals in handling these deaths, but there is a relative scarcity of information accessible for clinicians and pharmacists who are responsible for manufacturing or preparing the extemporaneous anti-epileptic suspensions in the hospitals. Mostly in partial seizures, phenytoin is one of the first-choice drugs. In Saudi Arabian hospitals, the extemporaneous preparation of phenytoin suspension is common, but the hot climatic weather in Saudi Arabia possesses stability problems that should be tackled as the prepared suspension should pass all the stability tests to ensure uniform dosage of the extemporaneous formulation. In the current study, the commercial capsules were used to prepare the oral phenytoin sodium extemporaneous suspension. The physical, chemical and microbiological stability of phenytoin sodium suspension is analyzed at various temperatures.</span>展开更多
BACKGROUND Cardiac magnetic resonance(CMR)is a unique tool for non-invasive tissue characterization,especially for identifying fibrosis.AIM To present the existing data regarding the association of electrocardiographi...BACKGROUND Cardiac magnetic resonance(CMR)is a unique tool for non-invasive tissue characterization,especially for identifying fibrosis.AIM To present the existing data regarding the association of electrocardiographic(ECG)markers with myocardial fibrosis identified by CMR-late gadolinium enhancement(LGE).METHODS A systematic search was performed for identifying the relevant studies in Medline and Cochrane databases through February 2021.In addition,we conducted a relevant search by Reference Citation Analysis(RCA)(https://www.referencecitationanalysis.com).RESULTS A total of 32 studies were included.In hypertrophic cardiomyopathy(HCM),fragmented QRS(fQRS)is related to the presence and extent of myocardial fibrosis.fQRS and abnormal Q waves are associated with LGE in ischemic cardiomyopathy patients,while fQRS has also been related to fibrosis in myocarditis.Selvester score,abnormal Q waves,and notched QRS have also been associated with LGE.Repolarization abnormalities as reflected by increased Tp-Te,negative Twaves,and higher QT dispersion are related to myocardial fibrosis in HCM patients.In patients with Duchenne muscular dystrophy,a significant correlation between fQRS and the amount of myocardial fibrosis as assessed by LGE-CMR was observed.In atrial fibrillation patients,advanced inter-atrial block is defined as P-wave duration≥120 ms,and biphasic morphology in inferior leads is related to left atrial fibrosis.CONCLUSION Myocardial fibrosis,a reliable marker of prognosis in a broad spectrum of cardiovascular diseases,can be easily understood with an easily applicable ECG.However,more data is needed on a specific disease basis to study the association of ECG markers and myocardial fibrosis as depicted by CMR.展开更多
Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes training.Large Language Models(LLMs)provide new in...Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes training.Large Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like Chinese.We first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom.Second,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants.We found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and LlaMA2-7B.For the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B failed.ChatGPT-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.展开更多
A typed category theory is proposed for the abstract description of knowledge and knowledge processing. It differs from the traditional category theory in two directions： all morphisms have types and the composition ...A typed category theory is proposed for the abstract description of knowledge and knowledge processing. It differs from the traditional category theory in two directions： all morphisms have types and the composition of morphisms is not necessary a morphism. Two aspects of application of typed category theory are discussed： cones and limits of knowledge complexity classes and knowledge completion with pseudo-functors.展开更多
Various factors affect the interfacial thermal resistance(ITR)between two materials,making ITR prediction a high-dimensional mathematical problem.Machine learning is a cost-effective method to address this.Here,we rep...Various factors affect the interfacial thermal resistance(ITR)between two materials,making ITR prediction a high-dimensional mathematical problem.Machine learning is a cost-effective method to address this.Here,we report ITR predictive models based on experimental data.The physical,chemical,and material properties of ITR are categorized into three sets of descriptors,and three algorithms are used for the models.Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%.Over 80,000 material systems composed of 293 materials were inputs for predictions.Among the top-100 high-ITR predictions by the three different algorithms,25 material systems are repeatedly predicted by at least two algorithms.One of the 25 material systems,Bi/Si achieved the ultra-low thermal conductivity in our previous work.We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications.This study proposed a strategy for material exploration for thermal management by means of machine learning.展开更多
To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method ...To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method to present a new scoring function that converts each bid into a score.Twokinds of multi-attribute auction models are introduced in terms of scoring rules and bidding objectivefunctions.Equilibrium bidding strategies,procurer's revenue comparisons and optimal auction designare characterized in these two models.Finally,this article discusses some improvement of robustnessof our models,with respect to the assumptions.展开更多
The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,par...The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.展开更多
Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade.One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with...Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade.One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments,which has given rise to the need for accelerated and accurate automated estimation of the properties of materials.In this regard,spectroscopic data are widely used for materials discovery because these data include essential information about materials.An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure,or kernel function.The required measure should be robust in terms of peak shifting,peak broadening,and noise.However,the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved.We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra.The similarity measures show good correspondence with the materials parameter,that is,the crystal-field parameter,in all measures.The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening.We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra.The regression model enabled the materials parameter,that is,10 Dq,to be automatically estimated from the spectra.With regard to research progress in similarity measures,this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.展开更多
The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We...The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.展开更多
基金supported by the Russian Science Foundation[grant number 19-19-00598].
文摘This paper reports a laboratory investigation of the fuel injection process in a diesel engine.The atomization process of the considered fuel(a hydrocarbon liquid)and the ensuing mixing with air is studied experimentally under high-pressure conditions.Different types of injector nozzles are examined,including(two)new configurations,which are compared in terms of performances to a standard injector manufactured by the Bosch company.For the two alternate configurations,the intake edges of one atomizing hole(hole No.1)are located in the sack volume while for the other(hole No.2)they are located on the locking cone of the needle valve.The injection process,the fuel atomization fineness and fuel supply speed characteristics are studied as functions of high-pressure fuel pump camshaft speed and rotation angle.The results obtained show that a decrease in the high-pressure fuel pump camshaft speed can produce fuel redistribution depending on the injector operation.In general,however,the hole No.1 can ensure fuel flow with higher speed with respect to the hole No.2 for all the operation modes of the injector.Based on such an analysis,we conclude that the use of certain injectors can enable a fine tuning of the propagation process of fuel sprays into various areas of the diesel engine combustion chamber.
基金The paper received financial support from the National Natural Science Foundation of China(Nos.71422015,71871213)the National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences.
文摘The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.
文摘This study is the investigation of the microstructure of different types of carbon fiber. They were compared with the carbonized and graphitized fibers. Results of structural researches have been presented. It was found that the damage varies from different pollution and the damage of the monofibers. The effect of the pollution of the monofiber was determined.
基金supported in part by the Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)the National Natural Science Foundation of China(61621003,62101136)+2 种基金Natural Science Foundation of Shanghai(21ZR1403600)Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)ZJLab,and Shanghai Municipal of Science and Technology Project(20JC1419500)。
文摘Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance.However,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning.To alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural information.Considering that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its curvature.Our DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data.On several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.
文摘The article is devoted to surface hardening of steels by alloying with the use of laser energy. Two combined technologies were proposed: first—laser alloying by nitride-forming elements followed by nitriding, and second—the local laser alloying followed by metallization in atmosphere of ammonia. It is shown that laser alloying in continuous radiation forms a layer with a homogeneous fine-grained structure with thickness of 600 microns. The subsequent nitriding increases the microhardness of the surface layer of low-carbon steels to 20,000 MPa, increases wear-resistance in a 3 - 15 times and crack resistance in a 1.5 times. Two-stage technology of metallization allows getting diffusion layer on the surface of steels with the thickness, which is 1.5 - 2 times higher than after traditional metallization. In addition, this method of surface modification can significantly reduce the temperature of diffusion metallization and reduce the processing time to 3 hours. The optimal regimes of both technologies, which provide homogeneous multiphase diffusion layers with high hardness and wear resistance, were determined.
基金supported by the National Natural Science Foundation of China (No.11574289)Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund(2nd phase) (No.U1501501)+1 种基金"111" Project by Education Ministry of China"Materials research by Information Integration" Initiative (MI2I) Project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST)
文摘Phenomenon of localized surface plasmon excitation at nanostructured materials has attracted much attention in recent decades for their wide applications in single molecule detection,surface-enhanced Raman spectroscopy and nano-plasmonics.In addition to the excitation by external light field,an electron beam can also induce the local surface plasmon excitation.Nowadays,electron energy loss spectroscopy(EELS)technique has been increasingly employed in experiment to investigate the surface excitation characteristics of metallic nanoparticles.However,a present theoretical analysis tool for electromagnetic analysis based on the discrete dipole approximation(DDA)method can only treat the case of excitation by light field.In this work we extend the DDA method for the calculation of EELS spectrum for arbitary nanostructured materials.We have simulated EELS spectra for different incident locations of an electron beam on a single silver nanoparticle,the simulated results agree with an experimental measurement very well.The present method then provides a computation tool for study of the local surface plasmon excitation of metallic nanoparticles induced by an electron beam.
文摘BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a well-established treatment for FI.Given the increased need of magnetic resonance imaging(MRI)for diagnostics,the In-terStim which was previously used in sacral nerve stimulation was limited by MRI incompatibility.Medtronic MRI-compatible InterStim was approved by the United States Food and Drug Administration in August 2020 and has been widely used.AIM To evaluate the efficacy,outcomes and complications of the MRI-compatible InterStim.METHODS Data of patients who underwent MRI-compatible Medtronic InterStim placement at UPMC Williamsport,University of Minnesota,Advocate Lutheran General Hospital,and University of Wisconsin-Madison was pooled and analyzed.Patient demographics,clinical features,surgical techniques,complications,and outcomes were analyzed.Strengthening the Reporting of Observational studies in Epidemiology(STROBE)cross-sectional reporting guidelines were used.RESULTS Seventy-three patients had the InterStim implanted.The mean age was 63.29±12.2 years.Fifty-seven(78.1%)patients were females and forty-two(57.5%)patients had diabetes.In addition to incontinence,overlapping symptoms included diarrhea(23.3%),fecal urgency(58.9%),and urinary incontinence(28.8%).Fifteen(20.5%)patients underwent Peripheral Nerve Evaluation before proceeding to definite implant placement.Thirty-two(43.8%)patients underwent rechargeable InterStim placement.Three(4.1%)patients needed removal of the implant.Migration of the external lead connection was observed in 7(9.6%)patients after the stage I procedure.The explanation for one patient was due to infection.Seven(9.6%)patients had other complications like nerve pain,hematoma,infection,lead fracture,and bleeding.The mean follow-up was 6.62±3.5 mo.Sixty-eight(93.2%)patients reported significant improvement of symptoms on follow-up evaluation.CONCLUSION This study shows promising results with significant symptom improvement,good efficacy and good patient outcomes with low complication rates while using MRI compatible InterStim for FI.Further long-term follow-up and future studies with a larger patient population is recommended.
文摘The authors discuss contradictions between the principal branches of the modern physical picture of the universe. Space and time have been shown in the Unitary Quantum Theory (UQT) not to be connected one with the other, unlike in the Special Theory of Relativity. In UQT, time becomes Newtonian again, and the growth of the particle’s mass with growing speed proceeds from other considerations of physics. Unlike the quantum theory, the modern gravitation theory (the general theory of relativity) is not confirmed by experiments and needs to be considerably revised.
文摘<span style="font-family:Verdana;">Epilepsy is a chronic and the fourth most common neurological disorder which affects people of all age groups. Recently research and awareness on epilepsy-related deaths have rapidly grown over the past two decades. Many previous studies are attributed to the guidelines that apprise health care professionals in handling these deaths, but there is a relative scarcity of information accessible for clinicians and pharmacists who are responsible for manufacturing or preparing the extemporaneous anti-epileptic suspensions in the hospitals. Mostly in partial seizures, phenytoin is one of the first-choice drugs. In Saudi Arabian hospitals, the extemporaneous preparation of phenytoin suspension is common, but the hot climatic weather in Saudi Arabia possesses stability problems that should be tackled as the prepared suspension should pass all the stability tests to ensure uniform dosage of the extemporaneous formulation. In the current study, the commercial capsules were used to prepare the oral phenytoin sodium extemporaneous suspension. The physical, chemical and microbiological stability of phenytoin sodium suspension is analyzed at various temperatures.</span>
文摘BACKGROUND Cardiac magnetic resonance(CMR)is a unique tool for non-invasive tissue characterization,especially for identifying fibrosis.AIM To present the existing data regarding the association of electrocardiographic(ECG)markers with myocardial fibrosis identified by CMR-late gadolinium enhancement(LGE).METHODS A systematic search was performed for identifying the relevant studies in Medline and Cochrane databases through February 2021.In addition,we conducted a relevant search by Reference Citation Analysis(RCA)(https://www.referencecitationanalysis.com).RESULTS A total of 32 studies were included.In hypertrophic cardiomyopathy(HCM),fragmented QRS(fQRS)is related to the presence and extent of myocardial fibrosis.fQRS and abnormal Q waves are associated with LGE in ischemic cardiomyopathy patients,while fQRS has also been related to fibrosis in myocarditis.Selvester score,abnormal Q waves,and notched QRS have also been associated with LGE.Repolarization abnormalities as reflected by increased Tp-Te,negative Twaves,and higher QT dispersion are related to myocardial fibrosis in HCM patients.In patients with Duchenne muscular dystrophy,a significant correlation between fQRS and the amount of myocardial fibrosis as assessed by LGE-CMR was observed.In atrial fibrillation patients,advanced inter-atrial block is defined as P-wave duration≥120 ms,and biphasic morphology in inferior leads is related to left atrial fibrosis.CONCLUSION Myocardial fibrosis,a reliable marker of prognosis in a broad spectrum of cardiovascular diseases,can be easily understood with an easily applicable ECG.However,more data is needed on a specific disease basis to study the association of ECG markers and myocardial fibrosis as depicted by CMR.
基金supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project(2023ZD0509202 and 2023ZD0509201)National Natural Science Foundation of China(62077037,8238810007,82022012,81870598,62272298 and 82388101)+4 种基金the National Key Research and Development Program of China(2022YFC2502800 and 2022YFC2407000)the Shanghai Municipal Key Clinical Specialty,Shanghai Research Center for Endocrine and Metabolic Diseases(2022ZZ01002)the Chinese Academy of Engineering(2022-XY-08)the Innovative Research Team of High-level Local Universities in Shanghai(SHSMUZDCX20212700)Beijing Natural Science Foundation(IS23096).
文摘Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes training.Large Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like Chinese.We first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom.Second,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants.We found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and LlaMA2-7B.For the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B failed.ChatGPT-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.
文摘A typed category theory is proposed for the abstract description of knowledge and knowledge processing. It differs from the traditional category theory in two directions： all morphisms have types and the composition of morphisms is not necessary a morphism. Two aspects of application of typed category theory are discussed： cones and limits of knowledge complexity classes and knowledge completion with pseudo-functors.
基金This work was supported by“the Materials research by Information Integration”Initiative(MI2I)project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency(JST).
文摘Various factors affect the interfacial thermal resistance(ITR)between two materials,making ITR prediction a high-dimensional mathematical problem.Machine learning is a cost-effective method to address this.Here,we report ITR predictive models based on experimental data.The physical,chemical,and material properties of ITR are categorized into three sets of descriptors,and three algorithms are used for the models.Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%.Over 80,000 material systems composed of 293 materials were inputs for predictions.Among the top-100 high-ITR predictions by the three different algorithms,25 material systems are repeatedly predicted by at least two algorithms.One of the 25 material systems,Bi/Si achieved the ultra-low thermal conductivity in our previous work.We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications.This study proposed a strategy for material exploration for thermal management by means of machine learning.
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant No. 200159National Natural Science Foundation of China under Grant No. 70571014
文摘To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method to present a new scoring function that converts each bid into a score.Twokinds of multi-attribute auction models are introduced in terms of scoring rules and bidding objectivefunctions.Equilibrium bidding strategies,procurer's revenue comparisons and optimal auction designare characterized in these two models.Finally,this article discusses some improvement of robustnessof our models,with respect to the assumptions.
基金This work was supported in part by the“Materials Research by Information Integration”Initiative(MI2I)project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency(JST)and a Grant-in-Aid for Scientific Research(B)15H02672 from the Japan Society for the Promotion of Science(JSPS)S.W.gratefully acknowledges financial support from JSPS KAKENHI Grant Number JP18K18017+3 种基金K.H.gratefully acknowledges financial support from JSPS KAKENHI Grant Number JP17K17762a Grant-in-Aid for Scientific Research on Innovative Areas(16H06439)and PRESTO(JPMJPR16NA)C.S.gratefully acknowledges financial support from the Ministry of Education and Science of the Russian Federation(Grant 14.Y26.31.0019)J.M.acknowledges partial financial support by JSPS KAKENHI Grant Number JP16K06768.
文摘The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.
基金This work is partly supported by the Elements Strategy Initiative Centre for Magnetic Materials(ESICMM)under the outsourcing project of the Ministry of Education,Culture,Sports,Science,Technology(MEXT)This work is partly supported in part by‘Materials Research by Information Integration’Initiative(MI2I)project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency(JST)+1 种基金H.H.is partly supported by JST CREST grant number JPMJCR1761.Y.S.is supported by JST,ACT-I,grant Number JPMJPR18UEK.O.gratefully acknowledges the financial support by Toyota Motor Corporation.
文摘Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade.One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments,which has given rise to the need for accelerated and accurate automated estimation of the properties of materials.In this regard,spectroscopic data are widely used for materials discovery because these data include essential information about materials.An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure,or kernel function.The required measure should be robust in terms of peak shifting,peak broadening,and noise.However,the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved.We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra.The similarity measures show good correspondence with the materials parameter,that is,the crystal-field parameter,in all measures.The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening.We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra.The regression model enabled the materials parameter,that is,10 Dq,to be automatically estimated from the spectra.With regard to research progress in similarity measures,this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.
基金The numerical calculations were conducted on the five supercomputer systems,Fugaku at the RIKEN Center for Computational Science,Kobe,Japanthe supercomputer at the Research Center for Computational Science,Okazaki,Japan(Project:21-IMS-C126,22-IMS-C125)+7 种基金the supercomputer Ohtaka at the Supercomputer Center,the Institute for Solid State Physics,the University of Tokyo,Tokyo,Japanthe supercomputer TSUBAME3.0 at the Tokyo Institute of Technology,Tokyo,Japanthe supercomputer ABCI at the National Institute of Advanced Industrial Science and Technology,Tsukuba,JapanThis work was supported by the following five grants:a JST CREST(Grant Number JPMJCR19I3 to J.M.and R.Y.)the MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(Project ID:hp210264 to R.Y.)the Grant-in-Aid for Scientific Research(A)from the Japan Society for the Promotion of Science(19H01132 to R.Y.)the Grant-in-Aid for Scientific Research(C)from the Japan Society for the Promotion of Science(22K11949 to Y.H.)the HPCI System Research Project(Project ID:hp210213 to Y.H.).
文摘The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.
