Background:Cyclin-dependent kinase 4/6(CDK4/6)inhibitors have transformed the management of hormone receptor–positive/HER2–negative(HR+/HER2–)advanced breast cancer,yet evidence for elderly or poor-performance pati...Background:Cyclin-dependent kinase 4/6(CDK4/6)inhibitors have transformed the management of hormone receptor–positive/HER2–negative(HR+/HER2–)advanced breast cancer,yet evidence for elderly or poor-performance patients remains limited.This study examined real-world outcomes of palbociclib plus endocrine therapy in Asian patients,with additional subgroup analyses by age and performance status.Methods:We retrospectively analyzed 46 consecutive Asian patients with recurrent or de novo HR+/HER2−breast cancer treated with first-line palbociclib plus ET between April 2021 and March 2025.The primary endpoint was progression-free survival(PFS).Secondary endpoints included overall response rate(ORR),disease control rate(DCR),and safety.Subgroup analyses were performed by age(<70 vs.≥70 years)and performance status(PS;0–1 vs.2–3).Results:The median PFS was 26.6 months(range,1.4–69.5).Stratified by age,median PFS was 26.9 months in patients<70 years and 26.2 months in those≥70 years(p=0.760).By PS,PFS was 26.9 months for PS 0–1 and 17.8 months for PS 2–3(p=0.099).ORR was 60.9%and DCR 93.5%;notably,all PS 2–3 patients achieved disease control.Hematologic toxicities were common,with neutropenia(80.4%)and leukopenia(86.7%)predominating,but grade≥3 anemia was rare(2.2%).Elderly patients experienced anemia more frequently,while overall toxicity remained manageable.Dose reductions occurred in 47.8%without loss of efficacy.Conclusions:In routine Japanese practice,palbociclib plus ET provided prolonged PFS and high disease control consistent with pivotal trials and international real-world evidence.Importantly,elderly patients tolerated treatment well,and selected PS 2–3 patients also derived clinical benefit.These findings indicate that neither age nor PS alone should preclude the use of palbociclib in carefully monitored real-world patients.展开更多
To establish practical,evidence-based strategies for noninvasive assessment and referral of patients with metabolic dysfunction-associated steatotic liver disease(MASLD)in Japan,we must address the urgent clinical nee...To establish practical,evidence-based strategies for noninvasive assessment and referral of patients with metabolic dysfunction-associated steatotic liver disease(MASLD)in Japan,we must address the urgent clinical need for accurate risk stratification and timely specialist intervention.A panel of 11 Japanese hepatology experts conducted a modified Delphi process to evaluate consensus recommendations regarding the use of noninvasive tests(NITs),including the fibrosis-4 index,enhanced liver fibrosis test,Mac-2-binding protein glycosylation isomer,type IV collagen 7S,cytokeratin-18 fragments,and imaging modalities such as ultrasound elastography and magnetic resonance elastography,for MASLD assessment and clinical referral.Practical algorithms were developed based on current Japanese data and panel consensus.The expert panel validated the utility of NITs as reliable tools for identifying patients with MASLD at risk for advanced fibrosis.Sequential use of NITs improved diagnostic accuracy and referral appropriateness while minimizing unnecessary specialist consultations.The proposed algorithms offer stepwise guidance for primary care physicians,supporting efficient,evidence-based decisionmaking.However,prospective longitudinal studies remain necessary for full prognostic validation of NITs in MASLD management.Noninvasive testing algorithms enable effective risk stratification and referral for MASLD in real-world Japanese practice with anticipated benefit for patient outcomes and healthcare systems.Broader adoption and further validation are warranted.展开更多
Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predica...Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures,existing direct generation approaches exhibit severe performance degradation.This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement.The system implements a complete generate-verify-repair cycle through six core processing components:semantic comprehension extracts structural constraints,language planner generates text plans,symbol translator performs structure-preserving mapping,consistency checker conducts static screening,Stanford Research Institute Problem Solver(STRIPS)simulator executes step-by-step validation,and VAL(Validator)provides semantic verification.A repair controller orchestrates four targeted strategies addressing typical failure patterns including first-step precondition errors andmid-segment statemaintenance issues.Comprehensive evaluation on PlanBench Mystery Blocksworld demonstrates substantial improvements over baseline approaches across both language models and reasoning models.Ablation studies confirm that each architectural component contributes non-redundantly to overall effectiveness,with targeted repair providing the largest impact,followed by deep constraint extraction and stepwise validation,demonstrating that superior performance emerges from synergistic integration of these mechanisms rather than any single dominant factor.Analysis reveals distinct failure patterns betweenmodel types—languagemodels struggle with local precondition satisfaction while reasoning models face global goal achievement challenges—yet the validation-driven mechanism successfully addresses these diverse weaknesses.A particularly noteworthy finding is the convergence of final success rates across models with varying intrinsic capabilities,suggesting that systematic validation and repair mechanisms play a more decisive role than raw model capacity in lexical-prior-free scenarios.This work establishes a rigorous evaluation framework incorporating statistical significance testing and mechanistic failure analysis,providingmethodological contributions for fair assessment and practical insights into building reliable planning systems under extreme constraint conditions.展开更多
In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A ...In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A semi-analytical formulation based on the First-Order Shear Deformation Theory(FSDT)and the principle of virtual displacements is established,and closed-form solutions are derived via Navier’s method for simply supported boundary conditions.The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model.A comprehensive parametric study examines the influence of folding geometry,dispersion arrangement,reinforcement weight fraction,curvature parameters,and elastic foundation support on the critical buckling temperature(CBT).The results reveal that,under optimal folding geometry and reinforcement alignment with principal stress trajectories,the CBT can increase by more than 150%.Furthermore,the combined effect of G-Ori reinforcement and elastic foundation substantially enhances thermal buckling resistance.These findings establish design guidelines for architected composite shells in applications such as aerospace thermal skins,morphing structures,and thermally-responsive systems,and illustrate the potential of auxetic graphene metamaterials for multifunctional,lightweight,and thermally robust structural components.展开更多
Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudi...Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.展开更多
The strength and damping properties of Co-Ni-Cr-Mo-based alloys with 0.5wt%Nb addition after various plastic deformation and heat treatment processes were investigated.Through Vickers hardness tests,free resonance You...The strength and damping properties of Co-Ni-Cr-Mo-based alloys with 0.5wt%Nb addition after various plastic deformation and heat treatment processes were investigated.Through Vickers hardness tests,free resonance Young's modulus measurements,and microstructure analysis,the effects of dislocation density,vacancy formation,and recrystallization on the alloy performance were clarified.Results indicate that increasing the rolling reduction enhances damping property due to higher dislocation density,whereas aging below the recrystallization temperature reduces damping property via dislocation pinning by the Suzuki effect.Recrystallization heat treatment restores the original structure and damping level.This alloy possesses tensile strength of approximately 1500 MPa and logarithmic decrement valueδ^(-1) in the range of 2×10^(-4)–3×10^(-4),demonstrating superior mechanical properties compared with the Ti-based alloys,which makes it an excellent candidate material for ultrasonic tools and medical applications.展开更多
目的建立超高效液相色谱-串联质谱法快速测定自制草乌药酒中9种乌头类生物碱含量的方法。方法以云南野生的滇草乌为主要原料,将草乌制成5种不同形状(整块、去皮、片状、块状、粉末状),分别取250 g浸泡于900 mL 52度玉米酿制白酒中,浸泡...目的建立超高效液相色谱-串联质谱法快速测定自制草乌药酒中9种乌头类生物碱含量的方法。方法以云南野生的滇草乌为主要原料,将草乌制成5种不同形状(整块、去皮、片状、块状、粉末状),分别取250 g浸泡于900 mL 52度玉米酿制白酒中,浸泡时间约为1.5年。移取1 mL药酒样品,加入30%甲醇水溶液(3:7,V:V)至100 mL,经0.22μm滤膜过滤后备测。利用C18色谱柱和乙腈-0.1%甲酸水的流动相进行梯度洗脱分离,多反应监测模式检测,通过基质工作曲线外标法定量。结果9种乌头类生物碱在1~100μg/L范围内线性关系良好,相关系数均大于0.999,检出限为0.63~0.98μg/L,定量限为2.11~3.26μg/L,低、中、高3种浓度水平下加标回收率为81.14%~116.01%,相对标准偏差为0.97%~11.26%(n=6)。经实际样品测定分析,药酒中9种乌头类生物碱的双酯型生物碱含量居高,其中以乌头碱含量最多,毒性较强。结论该方法前处理简便,灵敏度高,准确性好,检测效率高,适用于卫生应急中毒事件中自制草乌药酒9种乌头类生物碱的快速筛查,为医疗救治的快速诊断和采取对应急救治疗措施提供强有力技术支撑。展开更多
Cancer is the second leading cause of death globally.Its treatment remains a major challenge due to the disease's complexity,heterogeneity,and adaptive nature.Among the array of available treatments,targeted thera...Cancer is the second leading cause of death globally.Its treatment remains a major challenge due to the disease's complexity,heterogeneity,and adaptive nature.Among the array of available treatments,targeted therapy emerges as a paramount approach to address this substantial unmet clinical need,owing to its precise tumor targeting capabilities and potential for mitigating tumor progression risks.Drug conjugates are in high demand for targeted therapy due to their unique ligand specificity and potent cytotoxicity,thereby significantly enhancing therapeutic efficacy and reducing the incidence of adverse effects.Therefore,as a burgeoning field in biomedical research,it is timely to outline the latest advances in drug conjugates-driven cancer treatment.Herein,we aim to present the emerging breakthroughs in this exciting field at the intersection of target ligands,linkers,payloads,and cancer treatments.This review focuses on several drug conjugates-related strategies,including antibody-drug conjugates(ADCs),peptide-drug conjugates(PDCs),small molecule-drug conjugates(SMDCs),aptamer-drug conjugates(ApDCs)and radionuclide-drug conjugates(RDCs).Finally,we discuss the fundamentals behind drug conjugate-based anticancer therapeutics,along with their inherent advantages and associated challenges,as well as recent research advances.展开更多
Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiment...Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.展开更多
To investigate the influencesof non-plastic silt and soil aging on the re-liquefaction resistance of sands,a series of undrained triaxial tests was performed on sand-silt mixtures with finescontent ranging from 0%to 1...To investigate the influencesof non-plastic silt and soil aging on the re-liquefaction resistance of sands,a series of undrained triaxial tests was performed on sand-silt mixtures with finescontent ranging from 0%to 100%,as well as on undisturbed and reconstituted non-plastic sandy soils retrieved from earth structures with a history of earthquake-induced damage.The specimens on sand-silt mixtures were produced under an initial degree of compaction of 95%.In these tests,liquefaction histories were applied three times to a single specimen under the same cyclic stress ratio after the respective consolidation stages with the measurements of the shear wave velocities.The following conclusions can be obtained from the test results:(1)The liquefaction resistance obtained in the firstto third cyclicloading stages decreased initially with increasing finescontent up to about 45%,while it increased afterward.Therefore,the susceptibility of sands containing a relatively large amount of non-plastic silt to reliquefaction may be more significantthan that of clean sands;(2)The liquefaction resistance and the shear wave velocity decreased significantlyduring the second cyclic-loading stage and after the second consolidation,respectively,despite an increase in the specimen density caused by the first liquefaction history,while they increased in the third stage.The possible reason for this change would be the disturbance of soil structures due to liquefaction,which may be partially evaluated by the volumetric strain during the respective consolidation stages,and the stress-induced anisotropy formed in the previous liquefaction stage;and(3)The liquefaction resistance and the shear wave velocity of the undisturbed specimens,which were measured in the firstto third stages,were larger than those of the reconstituted ones due to the aging effects,respectively.That is,the aging effects may not necessarily be eliminated by the subsequent liquefaction history and may remain partially in some cases.展开更多
Low-dimensional(LD)halide perovskites have attracted considerable attention due to their distinctive structures and exceptional optoelectronic properties,including high absorption coefficients,extended charge carrier ...Low-dimensional(LD)halide perovskites have attracted considerable attention due to their distinctive structures and exceptional optoelectronic properties,including high absorption coefficients,extended charge carrier diffusion lengths,suppressed non-radiative recombination rates,and intense photoluminescence.A key advantage of LD perovskites is the tunability of their optical and electronic properties through the precise optimization of their structural arrangements and dimensionality.This review systematically examines recent progress in the synthesis and optoelectronic characterizations of LD perovskites,focusing on their structural,optical,and photophysical properties that underpin their versatility in diverse applications.The review further summarizes advancements in LD perovskite-based devices,including resistive memory,artificial synapses,photodetectors,light-emitting diodes,and solar cells.Finally,the challenges associated with stability,scalability,and integration,as well as future prospects,are discussed,emphasizing the potential of LD perovskites to drive breakthroughs in device efficiency and industrial applicability.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
Objectives:Although immune checkpoint inhibitors(ICIs)and targeted therapies have reshaped treatment non-small cell lung cancer(NSCLC)paradigms,prognosis remains poor for many patients due to delayed diagnosis and res...Objectives:Although immune checkpoint inhibitors(ICIs)and targeted therapies have reshaped treatment non-small cell lung cancer(NSCLC)paradigms,prognosis remains poor for many patients due to delayed diagnosis and resistance mechanisms.Liquid biopsy offers a minimally invasive approach to monitoring tumor evolution.Among circulating biomarkers,circulating tumor cells(CTCs)and cancer-associated macrophage-like cells(CAM-Ls)may provide complementary prognostic insights.The study aimed to evaluate the prognostic role of CTC and CAM-Ls dynamic in metastatic NSCLC patients.Methods:We retrospectively analyzed 77 patients with metastatic NSCLC who underwent CTC and CAM-L evaluation via the CellSearch^(R)system at baseline(T0)and after three months of first-line treatment(T1)including chemotherapy,targeted therapy,or ICIs.Survival outcomes were analyzed using Kaplan-Meier and Cox regression analyses.Results:Conversion to CTC-negative status at T1 was associated with improved outcomes,with median overall survival(OS)and progression-free survival(PFS)of 33 and 18 months,respectively,vs.10 and 6 months in persistently positive patients(both p<0.001).CTC negativity at T1 remained an independent prognostic factor for OS(HR:6.68)and PFS(HR:5.91,both p<0.0001).CAM-L positivity at T1 also correlated with longer OS(30 vs.12 months)and PFS(13 vs.6 months,both p<0.0001),particularly among ICI-treated patients.Combined CTC and CAM-L assessment further refined risk stratification.Conclusions:Dynamic monitoring of CTCs and CAM-Ls provides actionable prognostic information in metastatic NSCLC.CTC-negative status predicted longer OS and PFS,while CAM-L positivity at T1 was associated with improved outcomes,particularly in ICI-treated patients.Combined assessment of both biomarkers may directly inform therapeutic decision-making,through early detection of outcomes.展开更多
文摘Background:Cyclin-dependent kinase 4/6(CDK4/6)inhibitors have transformed the management of hormone receptor–positive/HER2–negative(HR+/HER2–)advanced breast cancer,yet evidence for elderly or poor-performance patients remains limited.This study examined real-world outcomes of palbociclib plus endocrine therapy in Asian patients,with additional subgroup analyses by age and performance status.Methods:We retrospectively analyzed 46 consecutive Asian patients with recurrent or de novo HR+/HER2−breast cancer treated with first-line palbociclib plus ET between April 2021 and March 2025.The primary endpoint was progression-free survival(PFS).Secondary endpoints included overall response rate(ORR),disease control rate(DCR),and safety.Subgroup analyses were performed by age(<70 vs.≥70 years)and performance status(PS;0–1 vs.2–3).Results:The median PFS was 26.6 months(range,1.4–69.5).Stratified by age,median PFS was 26.9 months in patients<70 years and 26.2 months in those≥70 years(p=0.760).By PS,PFS was 26.9 months for PS 0–1 and 17.8 months for PS 2–3(p=0.099).ORR was 60.9%and DCR 93.5%;notably,all PS 2–3 patients achieved disease control.Hematologic toxicities were common,with neutropenia(80.4%)and leukopenia(86.7%)predominating,but grade≥3 anemia was rare(2.2%).Elderly patients experienced anemia more frequently,while overall toxicity remained manageable.Dose reductions occurred in 47.8%without loss of efficacy.Conclusions:In routine Japanese practice,palbociclib plus ET provided prolonged PFS and high disease control consistent with pivotal trials and international real-world evidence.Importantly,elderly patients tolerated treatment well,and selected PS 2–3 patients also derived clinical benefit.These findings indicate that neither age nor PS alone should preclude the use of palbociclib in carefully monitored real-world patients.
基金Supported by Japan Society for the Promotion of Science KAKENHI,No.25K11274.
文摘To establish practical,evidence-based strategies for noninvasive assessment and referral of patients with metabolic dysfunction-associated steatotic liver disease(MASLD)in Japan,we must address the urgent clinical need for accurate risk stratification and timely specialist intervention.A panel of 11 Japanese hepatology experts conducted a modified Delphi process to evaluate consensus recommendations regarding the use of noninvasive tests(NITs),including the fibrosis-4 index,enhanced liver fibrosis test,Mac-2-binding protein glycosylation isomer,type IV collagen 7S,cytokeratin-18 fragments,and imaging modalities such as ultrasound elastography and magnetic resonance elastography,for MASLD assessment and clinical referral.Practical algorithms were developed based on current Japanese data and panel consensus.The expert panel validated the utility of NITs as reliable tools for identifying patients with MASLD at risk for advanced fibrosis.Sequential use of NITs improved diagnostic accuracy and referral appropriateness while minimizing unnecessary specialist consultations.The proposed algorithms offer stepwise guidance for primary care physicians,supporting efficient,evidence-based decisionmaking.However,prospective longitudinal studies remain necessary for full prognostic validation of NITs in MASLD management.Noninvasive testing algorithms enable effective risk stratification and referral for MASLD in real-world Japanese practice with anticipated benefit for patient outcomes and healthcare systems.Broader adoption and further validation are warranted.
基金supported by the Information,Production and Systems Research Center,Waseda University,and partly supported by the Future Robotics Organization,Waseda Universitythe Humanoid Robotics Institute,Waseda University,under the Humanoid Project+1 种基金the Waseda University Grant for Special Research Projects(grant numbers 2024C-518 and 2025E-027)was partly executed under the cooperation of organization between Kioxia Corporation andWaseda University.
文摘Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures,existing direct generation approaches exhibit severe performance degradation.This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement.The system implements a complete generate-verify-repair cycle through six core processing components:semantic comprehension extracts structural constraints,language planner generates text plans,symbol translator performs structure-preserving mapping,consistency checker conducts static screening,Stanford Research Institute Problem Solver(STRIPS)simulator executes step-by-step validation,and VAL(Validator)provides semantic verification.A repair controller orchestrates four targeted strategies addressing typical failure patterns including first-step precondition errors andmid-segment statemaintenance issues.Comprehensive evaluation on PlanBench Mystery Blocksworld demonstrates substantial improvements over baseline approaches across both language models and reasoning models.Ablation studies confirm that each architectural component contributes non-redundantly to overall effectiveness,with targeted repair providing the largest impact,followed by deep constraint extraction and stepwise validation,demonstrating that superior performance emerges from synergistic integration of these mechanisms rather than any single dominant factor.Analysis reveals distinct failure patterns betweenmodel types—languagemodels struggle with local precondition satisfaction while reasoning models face global goal achievement challenges—yet the validation-driven mechanism successfully addresses these diverse weaknesses.A particularly noteworthy finding is the convergence of final success rates across models with varying intrinsic capabilities,suggesting that systematic validation and repair mechanisms play a more decisive role than raw model capacity in lexical-prior-free scenarios.This work establishes a rigorous evaluation framework incorporating statistical significance testing and mechanistic failure analysis,providingmethodological contributions for fair assessment and practical insights into building reliable planning systems under extreme constraint conditions.
文摘In this work,a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami(G-Ori)auxetic metamaterials.A semi-analytical formulation based on the First-Order Shear Deformation Theory(FSDT)and the principle of virtual displacements is established,and closed-form solutions are derived via Navier’s method for simply supported boundary conditions.The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model.A comprehensive parametric study examines the influence of folding geometry,dispersion arrangement,reinforcement weight fraction,curvature parameters,and elastic foundation support on the critical buckling temperature(CBT).The results reveal that,under optimal folding geometry and reinforcement alignment with principal stress trajectories,the CBT can increase by more than 150%.Furthermore,the combined effect of G-Ori reinforcement and elastic foundation substantially enhances thermal buckling resistance.These findings establish design guidelines for architected composite shells in applications such as aerospace thermal skins,morphing structures,and thermally-responsive systems,and illustrate the potential of auxetic graphene metamaterials for multifunctional,lightweight,and thermally robust structural components.
文摘Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.
文摘The strength and damping properties of Co-Ni-Cr-Mo-based alloys with 0.5wt%Nb addition after various plastic deformation and heat treatment processes were investigated.Through Vickers hardness tests,free resonance Young's modulus measurements,and microstructure analysis,the effects of dislocation density,vacancy formation,and recrystallization on the alloy performance were clarified.Results indicate that increasing the rolling reduction enhances damping property due to higher dislocation density,whereas aging below the recrystallization temperature reduces damping property via dislocation pinning by the Suzuki effect.Recrystallization heat treatment restores the original structure and damping level.This alloy possesses tensile strength of approximately 1500 MPa and logarithmic decrement valueδ^(-1) in the range of 2×10^(-4)–3×10^(-4),demonstrating superior mechanical properties compared with the Ti-based alloys,which makes it an excellent candidate material for ultrasonic tools and medical applications.
基金the Project of China-Japan Joint International Laboratory of Advanced Drug Delivery System Research and Translation of Liaoning Province(No.2024JH2/102100007)the open fund of National Key Laboratory of Advanced DrugFormulations for Overcoming Delivery Barriers(No.2024-KFB-003)+1 种基金the National Natural Science Foundation of China(No.82104109)Scientific Research Project of Liaoning Department of Education(No.LJ212410163045).
文摘Cancer is the second leading cause of death globally.Its treatment remains a major challenge due to the disease's complexity,heterogeneity,and adaptive nature.Among the array of available treatments,targeted therapy emerges as a paramount approach to address this substantial unmet clinical need,owing to its precise tumor targeting capabilities and potential for mitigating tumor progression risks.Drug conjugates are in high demand for targeted therapy due to their unique ligand specificity and potent cytotoxicity,thereby significantly enhancing therapeutic efficacy and reducing the incidence of adverse effects.Therefore,as a burgeoning field in biomedical research,it is timely to outline the latest advances in drug conjugates-driven cancer treatment.Herein,we aim to present the emerging breakthroughs in this exciting field at the intersection of target ligands,linkers,payloads,and cancer treatments.This review focuses on several drug conjugates-related strategies,including antibody-drug conjugates(ADCs),peptide-drug conjugates(PDCs),small molecule-drug conjugates(SMDCs),aptamer-drug conjugates(ApDCs)and radionuclide-drug conjugates(RDCs).Finally,we discuss the fundamentals behind drug conjugate-based anticancer therapeutics,along with their inherent advantages and associated challenges,as well as recent research advances.
文摘Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.
基金supported by JSPS KAKENHI(Grant Nos.JP22K04305 and JP19K15083).
文摘To investigate the influencesof non-plastic silt and soil aging on the re-liquefaction resistance of sands,a series of undrained triaxial tests was performed on sand-silt mixtures with finescontent ranging from 0%to 100%,as well as on undisturbed and reconstituted non-plastic sandy soils retrieved from earth structures with a history of earthquake-induced damage.The specimens on sand-silt mixtures were produced under an initial degree of compaction of 95%.In these tests,liquefaction histories were applied three times to a single specimen under the same cyclic stress ratio after the respective consolidation stages with the measurements of the shear wave velocities.The following conclusions can be obtained from the test results:(1)The liquefaction resistance obtained in the firstto third cyclicloading stages decreased initially with increasing finescontent up to about 45%,while it increased afterward.Therefore,the susceptibility of sands containing a relatively large amount of non-plastic silt to reliquefaction may be more significantthan that of clean sands;(2)The liquefaction resistance and the shear wave velocity decreased significantlyduring the second cyclic-loading stage and after the second consolidation,respectively,despite an increase in the specimen density caused by the first liquefaction history,while they increased in the third stage.The possible reason for this change would be the disturbance of soil structures due to liquefaction,which may be partially evaluated by the volumetric strain during the respective consolidation stages,and the stress-induced anisotropy formed in the previous liquefaction stage;and(3)The liquefaction resistance and the shear wave velocity of the undisturbed specimens,which were measured in the firstto third stages,were larger than those of the reconstituted ones due to the aging effects,respectively.That is,the aging effects may not necessarily be eliminated by the subsequent liquefaction history and may remain partially in some cases.
基金funding from FCT(Fundagao para a Ciencia e Tecnologia,I.P.)under the projects LA/P/0037/2020,UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures,Nanomodelling and Nanofabrication-i3Nby the projects FlexSolar(PTDC/CTM-REF/1008/2020),and SpaceFlex(2022.01610.PTDC,DOI:10.54499/2022.01610.PTDC)+1 种基金supported by the project M-ECO2-Industrial Cluster for advanced biofuel production,Ref.C644930471-00000041,R2U Technologies and Befunding from the European Union via the project X-STREAM(Horizon EU,ERC CoG,No 101124803)the support of a fellowship from the"la Caixa"Foundation(ID 100010434)。
文摘Low-dimensional(LD)halide perovskites have attracted considerable attention due to their distinctive structures and exceptional optoelectronic properties,including high absorption coefficients,extended charge carrier diffusion lengths,suppressed non-radiative recombination rates,and intense photoluminescence.A key advantage of LD perovskites is the tunability of their optical and electronic properties through the precise optimization of their structural arrangements and dimensionality.This review systematically examines recent progress in the synthesis and optoelectronic characterizations of LD perovskites,focusing on their structural,optical,and photophysical properties that underpin their versatility in diverse applications.The review further summarizes advancements in LD perovskite-based devices,including resistive memory,artificial synapses,photodetectors,light-emitting diodes,and solar cells.Finally,the challenges associated with stability,scalability,and integration,as well as future prospects,are discussed,emphasizing the potential of LD perovskites to drive breakthroughs in device efficiency and industrial applicability.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
基金funded by Sapienza University PNRR-RT_SPOKE_1—ROME TECHNOPOLE—Spoke 1—B83C22002820006—ECS00000024 and FO R.O.onlus.
文摘Objectives:Although immune checkpoint inhibitors(ICIs)and targeted therapies have reshaped treatment non-small cell lung cancer(NSCLC)paradigms,prognosis remains poor for many patients due to delayed diagnosis and resistance mechanisms.Liquid biopsy offers a minimally invasive approach to monitoring tumor evolution.Among circulating biomarkers,circulating tumor cells(CTCs)and cancer-associated macrophage-like cells(CAM-Ls)may provide complementary prognostic insights.The study aimed to evaluate the prognostic role of CTC and CAM-Ls dynamic in metastatic NSCLC patients.Methods:We retrospectively analyzed 77 patients with metastatic NSCLC who underwent CTC and CAM-L evaluation via the CellSearch^(R)system at baseline(T0)and after three months of first-line treatment(T1)including chemotherapy,targeted therapy,or ICIs.Survival outcomes were analyzed using Kaplan-Meier and Cox regression analyses.Results:Conversion to CTC-negative status at T1 was associated with improved outcomes,with median overall survival(OS)and progression-free survival(PFS)of 33 and 18 months,respectively,vs.10 and 6 months in persistently positive patients(both p<0.001).CTC negativity at T1 remained an independent prognostic factor for OS(HR:6.68)and PFS(HR:5.91,both p<0.0001).CAM-L positivity at T1 also correlated with longer OS(30 vs.12 months)and PFS(13 vs.6 months,both p<0.0001),particularly among ICI-treated patients.Combined CTC and CAM-L assessment further refined risk stratification.Conclusions:Dynamic monitoring of CTCs and CAM-Ls provides actionable prognostic information in metastatic NSCLC.CTC-negative status predicted longer OS and PFS,while CAM-L positivity at T1 was associated with improved outcomes,particularly in ICI-treated patients.Combined assessment of both biomarkers may directly inform therapeutic decision-making,through early detection of outcomes.
