Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer patients.We aim to develop a multi-task model using multiscale who...Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer patients.We aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more finely.Methods:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to NAC.Our approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention mechanism.Results:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing sets.For the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing set.In the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,respectively.DLMM significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the model.Furthermore,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis exploration.Conclusions:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.展开更多
With the depletion of high-quality iron ore resources,high-phosphorus oolitic hematite(HPOH)has attracted great attention due to its large reserve and relatively high iron content.However,HPOH is very difficult to be ...With the depletion of high-quality iron ore resources,high-phosphorus oolitic hematite(HPOH)has attracted great attention due to its large reserve and relatively high iron content.However,HPOH is very difficult to be used in ironmaking process due to its special structure.A two-step method of gas-based direct reduction and magnetic separation was thus proposed to recover iron and reduce phosphorus.The results showed that the powdery reduced iron produced contained 92.31%iron and 0.1%phosphorus,and the iron recovery was 92.65%under optimum reduction condition,which is suitable for following steelmaking.The apatite will be reduced under long reduction time and a large reducing gas flow rate,resulting in more phosphorus found in the metallic iron.Increasing the hydrogen–carbon ratio will inhibit the formation and growth of iron particles and prevent the breakage of oolitic structure.Careful adjustment of reduction temperature is recommended as it affects the oolitic structure and reduction.展开更多
The hydrogen-enriched direct reduction shaft furnace addresses the high CO_(2) emissions associated with the blast furnace process.A discrete element method(DEM)model was introduced to explore how the structure of the...The hydrogen-enriched direct reduction shaft furnace addresses the high CO_(2) emissions associated with the blast furnace process.A discrete element method(DEM)model was introduced to explore how the structure of the diversion cone affects particle descent behavior in a hydrogen-enriched shaft furnace.The results indicated that in the absence of a diversion cone,the descending velocity near the furnace wall zone is significantly lower than that at its center,resulting in a‘V’-shaped burden flow pattern.The discharge velocity has a minor impact on the flow pattern in the shaft furnace.Upon installation of a diversion cone,burden descending velocity becomes more uniform,leading to a‘-’-shaped burden flow pattern.As the bottom of the diversion cone ascends(i.e.,the lower end of the diversion cone is progressively closer to the top),there is an increase in the volume fraction of the dead zone within the shaft furnace.This is particularly evident in the formation of a triangular dead zone at the base of the diversion cone.It is suggested that the lower cone of the bi-conical diversion cone should maintain a sufficient height.展开更多
Laser frequency microcombs provide a series of equidistant,coherent frequency markers across a broad spectrum,enabling advancements in laser spectroscopy,dense optical communications,precision distance metrology,and a...Laser frequency microcombs provide a series of equidistant,coherent frequency markers across a broad spectrum,enabling advancements in laser spectroscopy,dense optical communications,precision distance metrology,and astronomy.Here,we design and fabricate silicon nitride,dispersion-managed microresonators that effectively suppress avoided-mode crossings and achieve close-to-zero averaged dispersion.Both the stochastic noise and mode-locking dynamics of the resonator are numerically and experimentally investigated.First,we experimentally demonstrate thermally stabilized microcomb formation in the microresonator across different mode-locked states,showing negligible center frequency shifts and a broad frequency bandwidth.Next,we characterize the femtosecond timing jitter of the microcombs,supported by precise metrology of the timing phase and relative intensity noise.For the single-soliton state,we report a relative intensity noise of−153.2 dB∕Hz,close to the shot-noise limit,and a quantum-noise–limited timing jitter power spectral density of 0.4 as 2∕Hz at a 100 kHz offset frequency,measured using a self-heterodyne linear interferometer.In addition,we achieve an integrated timing jitter of 1.7 fs±0.07 fs,measured from 10 kHz to 1 MHz.Measuring and understanding these fundamental noise parameters in high clock rate frequency microcombs is critical for advancing soliton physics and enabling new applications in precision metrology.展开更多
Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).C...Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.展开更多
基金supported by the National Natural Science Foundation of China(No.82371933)the National Natural Science Foundation of Shandong Province of China(No.ZR2021MH120)+1 种基金the Taishan Scholars Project(No.tsqn202211378)the Shandong Provincial Natural Science Foundation for Excellent Young Scholars(No.ZR2024YQ075).
文摘Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer patients.We aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more finely.Methods:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to NAC.Our approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention mechanism.Results:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing sets.For the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing set.In the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,respectively.DLMM significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the model.Furthermore,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis exploration.Conclusions:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.
基金supported by the National Key R&D Program of China(Grant Nos.2021YFC2902400 and 2021YFC2902404)Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(FRF-IDRY-21-027 and FRF-IDRY-22-018).
文摘With the depletion of high-quality iron ore resources,high-phosphorus oolitic hematite(HPOH)has attracted great attention due to its large reserve and relatively high iron content.However,HPOH is very difficult to be used in ironmaking process due to its special structure.A two-step method of gas-based direct reduction and magnetic separation was thus proposed to recover iron and reduce phosphorus.The results showed that the powdery reduced iron produced contained 92.31%iron and 0.1%phosphorus,and the iron recovery was 92.65%under optimum reduction condition,which is suitable for following steelmaking.The apatite will be reduced under long reduction time and a large reducing gas flow rate,resulting in more phosphorus found in the metallic iron.Increasing the hydrogen–carbon ratio will inhibit the formation and growth of iron particles and prevent the breakage of oolitic structure.Careful adjustment of reduction temperature is recommended as it affects the oolitic structure and reduction.
基金the National Key R&D Program of China(Nos.2021YFC2902400 and 2021YFC2902401)the project of State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process(No.BGRIMM-KZSKL-2023-14).
文摘The hydrogen-enriched direct reduction shaft furnace addresses the high CO_(2) emissions associated with the blast furnace process.A discrete element method(DEM)model was introduced to explore how the structure of the diversion cone affects particle descent behavior in a hydrogen-enriched shaft furnace.The results indicated that in the absence of a diversion cone,the descending velocity near the furnace wall zone is significantly lower than that at its center,resulting in a‘V’-shaped burden flow pattern.The discharge velocity has a minor impact on the flow pattern in the shaft furnace.Upon installation of a diversion cone,burden descending velocity becomes more uniform,leading to a‘-’-shaped burden flow pattern.As the bottom of the diversion cone ascends(i.e.,the lower end of the diversion cone is progressively closer to the top),there is an increase in the volume fraction of the dead zone within the shaft furnace.This is particularly evident in the formation of a triangular dead zone at the base of the diversion cone.It is suggested that the lower cone of the bi-conical diversion cone should maintain a sufficient height.
基金support from the Lawrence Livermore National Laboratory(Grant No.B622827)the National Science Foundation(Grant Nos.1824568,1810506,1741707,and 1829071)the Office of Naval Research(Grant No.N00014-16-1-2094).
文摘Laser frequency microcombs provide a series of equidistant,coherent frequency markers across a broad spectrum,enabling advancements in laser spectroscopy,dense optical communications,precision distance metrology,and astronomy.Here,we design and fabricate silicon nitride,dispersion-managed microresonators that effectively suppress avoided-mode crossings and achieve close-to-zero averaged dispersion.Both the stochastic noise and mode-locking dynamics of the resonator are numerically and experimentally investigated.First,we experimentally demonstrate thermally stabilized microcomb formation in the microresonator across different mode-locked states,showing negligible center frequency shifts and a broad frequency bandwidth.Next,we characterize the femtosecond timing jitter of the microcombs,supported by precise metrology of the timing phase and relative intensity noise.For the single-soliton state,we report a relative intensity noise of−153.2 dB∕Hz,close to the shot-noise limit,and a quantum-noise–limited timing jitter power spectral density of 0.4 as 2∕Hz at a 100 kHz offset frequency,measured using a self-heterodyne linear interferometer.In addition,we achieve an integrated timing jitter of 1.7 fs±0.07 fs,measured from 10 kHz to 1 MHz.Measuring and understanding these fundamental noise parameters in high clock rate frequency microcombs is critical for advancing soliton physics and enabling new applications in precision metrology.
基金the financial supports of the National Natural Science Foundation of China(No.52372200)a project supported by the State Key Laboratory of Mechanics and Control for Aerospace Structures(No.MCAS-S-0324G01)。
文摘Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.
