在经典近场动力学基础上利用变形等价关系重新推导了非局部力密度,结合非局部微分算子理论降低边界处的计算误差,基于此构建了应力-应变求解模型,解决了传统理论在应力分析方面的局限性,同时引入应变能密度(strain energy density)刚度...在经典近场动力学基础上利用变形等价关系重新推导了非局部力密度,结合非局部微分算子理论降低边界处的计算误差,基于此构建了应力-应变求解模型,解决了传统理论在应力分析方面的局限性,同时引入应变能密度(strain energy density)刚度折减理论,建立裂纹处介质的力学参数与残余应变能之间的关联。本方法被用于模拟软弱层岩体裂隙间的应力分布情况,并通过与先前研究结果的对比验证了其有效性和适用性。此外,研究了完整层状岩体中软弱层条形间隔破裂的演化过程。结果表明,软弱层岩体裂缝间距与厚度比值对力学状态影响显著,随着比值不断增加,裂纹之间的应力值整体由压应力转变为拉应力。软弱层的等间距破裂现象包括微裂隙的扩展、间隔裂纹的形成以及裂隙逐渐饱和等过程。外荷载作用下引发软弱层与基层之间的损伤以及岩层整体破裂。本方法能够有效描述层状岩体的间隔破裂过程,显示出良好的应用前景。展开更多
This paper presents a high-fidelity lumpedparameter(LP)thermal model(HF-LPTM)for permanent magnet synchronous machines(PMSMs)in electric vehicle(EV)applications,where various cooling techniques are considered,includin...This paper presents a high-fidelity lumpedparameter(LP)thermal model(HF-LPTM)for permanent magnet synchronous machines(PMSMs)in electric vehicle(EV)applications,where various cooling techniques are considered,including frame forced air/liquid cooling,oil jet cooling for endwinding,and rotor shaft cooling.To address the temperature misestimation in the LP thermal modelling due to assumptions of concentrated loss input and uniform heat flows,the developed HF-LPTM introduces two compensation thermal resistances for the winding and PM components,which are analytically derived from the multi-dimensional heat transfer equations and are robust against different load/thermal conditions.As validated by the finite element analysis method and experiments,the conventional LPTMs exhibit significant winding temperature deviations,while the proposed HF-LPTM can accurately predict both the midpoint and average temperatures.The developed HFLPTM is further used to assess the effectiveness of various cooling techniques under different scenarios,i.e.,steady-state thermal states under the rated load condition,and transient temperature profiles under city,freeway,and hybrid(city+freeway)driving cycles.Results indicate that no single cooling technique can maintain both winding and PM temperatures within safety limits.The combination of frame liquid cooling and oil jet cooling for end winding can sufficiently mitigate PMSM thermal stress in EV applications.展开更多
Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether ...Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.展开更多
Type 2 diabetes mellitus(T2DM)significantly elevates the risk of colorectal cancer(CRC)and complicates its treatment by promoting chemoresistance.Poor glycemic control has been linked to exacerbated CRC progression an...Type 2 diabetes mellitus(T2DM)significantly elevates the risk of colorectal cancer(CRC)and complicates its treatment by promoting chemoresistance.Poor glycemic control has been linked to exacerbated CRC progression and diminished chemotherapy efficacy,impacting patient outcomes through various mechanisms such as oxidative stress,activation of metabolic pathways,and altered protein modifications that hinder apoptosis and enhance tumor survival.Clinical evidence shows that T2DM patients experience higher rates of chemoresistance and reduced disease-free survival and overall survival compared to non-diabetic patients.Specifically,those with poor glycemic control exhibit increased chemo-resistance and poorer survival metrics.Antidiabetic treatments,including metformin,acarbose,and gliclazide,show promise in improving chemotherapy response and glycemic management,potentially enhancing patient outcomes.Addressing this challenge requires a comprehensive,multidisciplinary approach involving oncologists,endocrino-logists,and surgeons to optimize patient care.Integrated strategies that prioritize glycemic control are essential for reducing chemoresistance and improving survival in CRC patients with T2DM.展开更多
Vagus nerve stimulation(VNS)and stroke:Stroke is the second leading cause of death and the third leading cause of disability worldwide(Baig et al.,2023).There have been significant paradigm shifts in the management of...Vagus nerve stimulation(VNS)and stroke:Stroke is the second leading cause of death and the third leading cause of disability worldwide(Baig et al.,2023).There have been significant paradigm shifts in the management of acute ischemic stroke through mechanical thrombectomy.In chronic ischemic stroke,invasive VNS paired with rehabilitation is associated with a significant increase in upper limb motor recovery and is FDA-approved(Baig et al.,2023).There are no treatments of similar efficacy in acute intracerebral hemorrhage(ICH)where several promising trials,e.g.,TICH-2,STOP-AUST,and TRAIGE did not show improvements in functional outcomes(Puy et al.,2023).展开更多
文摘在经典近场动力学基础上利用变形等价关系重新推导了非局部力密度,结合非局部微分算子理论降低边界处的计算误差,基于此构建了应力-应变求解模型,解决了传统理论在应力分析方面的局限性,同时引入应变能密度(strain energy density)刚度折减理论,建立裂纹处介质的力学参数与残余应变能之间的关联。本方法被用于模拟软弱层岩体裂隙间的应力分布情况,并通过与先前研究结果的对比验证了其有效性和适用性。此外,研究了完整层状岩体中软弱层条形间隔破裂的演化过程。结果表明,软弱层岩体裂缝间距与厚度比值对力学状态影响显著,随着比值不断增加,裂纹之间的应力值整体由压应力转变为拉应力。软弱层的等间距破裂现象包括微裂隙的扩展、间隔裂纹的形成以及裂隙逐渐饱和等过程。外荷载作用下引发软弱层与基层之间的损伤以及岩层整体破裂。本方法能够有效描述层状岩体的间隔破裂过程,显示出良好的应用前景。
文摘This paper presents a high-fidelity lumpedparameter(LP)thermal model(HF-LPTM)for permanent magnet synchronous machines(PMSMs)in electric vehicle(EV)applications,where various cooling techniques are considered,including frame forced air/liquid cooling,oil jet cooling for endwinding,and rotor shaft cooling.To address the temperature misestimation in the LP thermal modelling due to assumptions of concentrated loss input and uniform heat flows,the developed HF-LPTM introduces two compensation thermal resistances for the winding and PM components,which are analytically derived from the multi-dimensional heat transfer equations and are robust against different load/thermal conditions.As validated by the finite element analysis method and experiments,the conventional LPTMs exhibit significant winding temperature deviations,while the proposed HF-LPTM can accurately predict both the midpoint and average temperatures.The developed HFLPTM is further used to assess the effectiveness of various cooling techniques under different scenarios,i.e.,steady-state thermal states under the rated load condition,and transient temperature profiles under city,freeway,and hybrid(city+freeway)driving cycles.Results indicate that no single cooling technique can maintain both winding and PM temperatures within safety limits.The combination of frame liquid cooling and oil jet cooling for end winding can sufficiently mitigate PMSM thermal stress in EV applications.
文摘Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.
文摘Type 2 diabetes mellitus(T2DM)significantly elevates the risk of colorectal cancer(CRC)and complicates its treatment by promoting chemoresistance.Poor glycemic control has been linked to exacerbated CRC progression and diminished chemotherapy efficacy,impacting patient outcomes through various mechanisms such as oxidative stress,activation of metabolic pathways,and altered protein modifications that hinder apoptosis and enhance tumor survival.Clinical evidence shows that T2DM patients experience higher rates of chemoresistance and reduced disease-free survival and overall survival compared to non-diabetic patients.Specifically,those with poor glycemic control exhibit increased chemo-resistance and poorer survival metrics.Antidiabetic treatments,including metformin,acarbose,and gliclazide,show promise in improving chemotherapy response and glycemic management,potentially enhancing patient outcomes.Addressing this challenge requires a comprehensive,multidisciplinary approach involving oncologists,endocrino-logists,and surgeons to optimize patient care.Integrated strategies that prioritize glycemic control are essential for reducing chemoresistance and improving survival in CRC patients with T2DM.
基金supported by on Association of British Neurologists Fellowship(Stroke Association/Berkeley Foundation)supported by the NIHR Sheffield Biomedical Research Centre。
文摘Vagus nerve stimulation(VNS)and stroke:Stroke is the second leading cause of death and the third leading cause of disability worldwide(Baig et al.,2023).There have been significant paradigm shifts in the management of acute ischemic stroke through mechanical thrombectomy.In chronic ischemic stroke,invasive VNS paired with rehabilitation is associated with a significant increase in upper limb motor recovery and is FDA-approved(Baig et al.,2023).There are no treatments of similar efficacy in acute intracerebral hemorrhage(ICH)where several promising trials,e.g.,TICH-2,STOP-AUST,and TRAIGE did not show improvements in functional outcomes(Puy et al.,2023).
