We describe here ten years of development of a Chinese learning technology and five years of practical experience in integrating this technology in MIT classrooms for intermediate-high and advanced-low students.Key re...We describe here ten years of development of a Chinese learning technology and five years of practical experience in integrating this technology in MIT classrooms for intermediate-high and advanced-low students.Key results are as follows:There is no need to disrupt the classroom experience(both for the teacher and the students);Technology provides a sharp increase in learning efficiency and motivation,as confirmed by students;and This overall improvement in learning is achieved by focusing on the efficiency of personal study time.The most salient type of feedback from students falls into two categories:“I wouldn’t have been able to take a class at that level without FullChinese,”and“The use of technology allowed me to prepare for class two to three times faster.”Results were achieved through a slow iterative process during which our learning technology evolved to solve observed needs in acquiring complex new material.展开更多
The aim of this paper is to define(p,q)-analogue of Mittag-Leffler Function,by using(p,q)-Gamma function.Some transformation formulae are also derived by using the(p,q)-derivative.The(p,q)-analogue for this function p...The aim of this paper is to define(p,q)-analogue of Mittag-Leffler Function,by using(p,q)-Gamma function.Some transformation formulae are also derived by using the(p,q)-derivative.The(p,q)-analogue for this function provides elegant generalization of q-analogue of Mittag-Leffler function in connection with q-calculus.Moreover,the(p,q)-Laplace Transform of the Mittag-Leffler function has been obtained.Some special cases have also been discussed.展开更多
Cloud computing is a technology that allows the end-users to access the network through a shared area of resources.As the demand for the cloud computing increases,vulnerabilities in the service provision also increase...Cloud computing is a technology that allows the end-users to access the network through a shared area of resources.As the demand for the cloud computing increases,vulnerabilities in the service provision also increase.EDoS is one of the attacks that take over the provider,financially affecting the various organizations which use the cloud data.This paper utilizes fuzzy entropy and lion neural learner(FLNL)for the classification of cloud users to mitigate EDoS attacks in the cloud.This technique includes a training phase,which creates a log file using various parameters and then transforms the features into database considering certain key features.There are two important stages in this classification approach:feature selection and classification.Here,the fuzzy entropy function is utilized for feature selection which effectively selects useful features without information loss.The classification is performed using lion neural learner(LNL)which incorporates Lion algorithm(LA)into the neural network and uses Levenberg–Marquardt(LM)algorithm.The experimental results finalize that the proposed FLNL is effective with 89%precision,78%recall,and 83.13%of f-measure compared with the existing Na¨ıve Bayes(NB),Neural Network+Back Propagation(NN+BP),and Neural Network+Levenberg–Marquardt(NN+LM).展开更多
The genus Eois comprises an important part of megadiverse assemblages of geometrid moths in mountain rainforests of southern Ecuador. In this study we report： （i） on the construction of a DNA barcode library of Eoi...The genus Eois comprises an important part of megadiverse assemblages of geometrid moths in mountain rainforests of southern Ecuador. In this study we report： （i） on the construction of a DNA barcode library of Eois for identification purposes; and （ii） the exploration of species diversity through species delimitation by pair-wise distance thresholds. COl barcode sequences were generated from 408 individuals （at least 105 species） collected on a narrow geographic scale （-40 km2） in the Reserva Bioldgica San Francisco. Analyses of barcode sequence divergence showed that species delimitations based solely on external morphology result in broad overlap of intra- and interspecific distances. Species delimitation at a 2% pair-wise distance threshold reveals a clear barcoding gap. Fifty-two previously unrecognized species were identified, 31 of which could only be distinguished by an integrative taxonomy approach. Twelve additional putative species could only be recognized by threshold-based delimitation. Most splits resulted in two or three newly perceived cryptic taxa. The present study increased the number of Eois species recorded from that small area of Andean mountain forest from 102 to 154 （morphology- plus integrative taxonomy-based） or even 166 （sequence-based）, leaving the species accumulation curve still far from reaching an asymptote. Notably, in no case did two or more previously distinguished morphospecies have to be lumped. This barcode inventory can be used to match larvae to known adult samples without rearing, and will therefore be of vital help to extend the currently limited knowledge about food plant relationships and host specialization.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
Valvular Heart Disease(VHD),including stenosis and regurgitation,is a significant contributor to global cardiovascular morbidity.Current prosthetic solutions mechanical and bioprosthetic heart valves each present majo...Valvular Heart Disease(VHD),including stenosis and regurgitation,is a significant contributor to global cardiovascular morbidity.Current prosthetic solutions mechanical and bioprosthetic heart valves each present major limitation.Mechanical valves require lifelong anticoagulation due to thrombogenicity,while bioprosthetic valves suffer from structural degeneration and limited durability.Polymeric Heart Valves(PHVs)have emerged as promising alternatives,aiming to integrate the mechanical resilience of synthetic materials with the biocompatibility and hemodynamic performance of natural valves.Recent studies have explored advanced polymers such as Polyhedral Oligomeric Silsesquioxane–Polycarbonate–Urea–Urethane(POSS-PCU),Silicone–Polyurethane Urea(SiPUU),and nanocomposites like Polyvinyl Alcohol(PVA)and SIBS for their enhanced thromboresistance,calcification resistance,and long-term mechanical durability.Complementary to material innovation,fabrication methods such as 3D printing,Melt Electrospinning Writing(MEW),and Focused Rotary Jet Spinning(FRJS)offer patient-specific designs and microstructural control.This review systematically compares traditional and next-generation prostheses,examines mechanical and biological performance,and discusses critical design challenges including porosity,thrombogenicity,and leaflet calcification.Finite Element Analysis(FEA)and Computational Fluid Dynamics(CFD)are highlighted for optimizing design and simulating physiological conditions.By presenting recent preclinical progress and manufacturing strategies,this review outlines a translational roadmap toward clinically viable,biomimetic polymeric heart valves capable of addressing the needs of both adult and pediatric patients.Compared to traditional bioprosthetic tissues,advanced polymers offer better resistance to calcification,reduced thrombogenicity,and tunable mechanical properties.展开更多
BACKGROUND Cellular therapies have started an important new therapeutic direction in autistic spectrum disorder(ASD),and the ample diversity of ASD pathophysiology and the different types of cell therapies prompt an e...BACKGROUND Cellular therapies have started an important new therapeutic direction in autistic spectrum disorder(ASD),and the ample diversity of ASD pathophysiology and the different types of cell therapies prompt an equally ample effort to employ clinical studies for studying the ASD causes and cell therapies.Stem cells have yielded so far mixed results in clinical trials,and at patient level the results varied from impressive to no improvement.In this context we have administered autologous cord blood(ACB)and a non-placebo,material intervention repre-sented by an individualized combination of supplements(ICS)to ASD children.METHODS CORDUS clinical study is a crossover study in which both oral ICS and intravenous ACB were sequentially administered to 56 children;ACB was infused as an inpatient procedure.Treatment efficacy was evaluated pre-treatment and post-treatment at 6 months by an independent psychotherapist with Autism Treatment Evaluation Checklist,Quantitative Checklist for Autism in Toddlers and a 16-item comparative table score,after interviewing the children’s parents and therapists.Before and after each intervention participants had a set of blood tests including inflammatory,metabolic and oxidative markers,and the neuronal specific enolase.RESULTS No serious adverse reactions were noted during and after cord blood or supplement administration.ACB improved evaluation scores in 78%of children with age 3–7-years(n=28),but was much less effective in kids older than 8 years or with body weight of more than 35 kg(n=28;only 11%of children improved scores).ICS yielded better results than ACB in 5 cases out of 28,while in 23 kids ACB brought more improvement than ICS(P<0.05);high initial levels of inflammation and ferritin were associated with no improvement.Ample individual differences were noted in children's progress,and statistically significant improvements were seen after ACB on areas such as verbalization and social interaction,but not on irritability or aggressive behavior.CONCLUSION ACB has superior efficacy to ICS in ASD;high inflammation,ferritin,age and body weight predict less improvement;more clinical studies are needed for studying ACB efficacy in ASD.展开更多
Mechanical loading constitutes a fundamental determinant in the process of bone remodeling.This modeling encompasses the incorporation of mechanical stimuli,the involvement of cellular and molecular constituents,as we...Mechanical loading constitutes a fundamental determinant in the process of bone remodeling.This modeling encompasses the incorporation of mechanical stimuli,the involvement of cellular and molecular constituents,as well as the utilization of sophisticated computational methodologies.Such an approach is imperative for forecasting bone behaviour across varying environmental conditions.In the present study,key findings from bone mechanobiology are reviewed,along with the possibility that Functionally Graded Materials(FGM)enhances osseointegration and lowers the stress-shielding effect during bone remodeling and compared to titanium,FGM improves periprosthetic bone remodeling.To summarise some of the most important findings from computational models of bone mechanobiology,explaining how modifications to the mechanical environment affect implant design,growth of bone,and bone response.The impact that changes related to the mechanical environment have on bone response is examined using computational models and methods such as surface microtopography to determine how an implant’s bone density has increased over time.This review focuses on the refinement of advanced simulation frameworks and their synergy with imaging technologies to strengthen model validation,ultimately resulting in better clinical outcomes in the context of bone health treatments.展开更多
This study presents a novel Li metal host material with a unique hollow nano-spherical structure that incorporates Ag nano-seeds into a graphitic carbon nitride(g-C_(3)N_(4))shell layer,referred to as g-C_(3)N_(4)@Ag ...This study presents a novel Li metal host material with a unique hollow nano-spherical structure that incorporates Ag nano-seeds into a graphitic carbon nitride(g-C_(3)N_(4))shell layer,referred to as g-C_(3)N_(4)@Ag hollow spheres.The g-C_(3)N_(4)@Ag spheres provide a managed internal site for Li metal encapsulation and promote stable Li plating.The g-C_(3)N_(4) spheres are uniformly coated using polydopamine,which has an adhesive nature,to enhance lithium plating/stripping stability.The strategic presence of Ag nano-seeds eliminates the nucleation barrier,properly directing Li growth within the hollow spheres.This design facilitates highly reversible and consistent lithium deposition,offering a promising direction for the production of high-performance lithium metal anodes.These well-designed g-C_(3)N_(4)@Ag hollow spheres ensure stable Li plating/stripping kinetics over more than 500 cycles with a high coulombic efficiency of over 97%.Furthermore,a full cell made using LiNi_(0.90)Co_(0.07)Mn_(0.03)O_(2) and Li-g-C_(3)N_(4)@Ag host electrodes demonstrated highly competitive performance over 200 cycles,providing a guide for the implementation of this technology in advanced lithium metal batteries.展开更多
Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role ...Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role of matrix-embedded osteocytes in OI pathogenesis remains largely unknown.Homozygous human SP7(c.946 C>T,R316C)mutation results in a recessive form of OI characterized by fragility fractures,low bone mineral density and osteocyte dendrite defects.To better understand how the OI-causing R316C mutation affects the function of SP7,we generated Sp7^(R342C)knock-in mice.Consistent with patient phenotypes,Sp7^(R342C/R342C)mice demonstrate increased cortical porosity and reduced cortical bone mineral density.Sp7^(R342C/R342C)mice show osteocyte dendrite defects,increased osteocyte apoptosis,and intracortical bone remodeling with ectopic intracortical osteoclasts and elevated osteocyte Tnfsf11 expression.展开更多
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
文摘We describe here ten years of development of a Chinese learning technology and five years of practical experience in integrating this technology in MIT classrooms for intermediate-high and advanced-low students.Key results are as follows:There is no need to disrupt the classroom experience(both for the teacher and the students);Technology provides a sharp increase in learning efficiency and motivation,as confirmed by students;and This overall improvement in learning is achieved by focusing on the efficiency of personal study time.The most salient type of feedback from students falls into two categories:“I wouldn’t have been able to take a class at that level without FullChinese,”and“The use of technology allowed me to prepare for class two to three times faster.”Results were achieved through a slow iterative process during which our learning technology evolved to solve observed needs in acquiring complex new material.
文摘The aim of this paper is to define(p,q)-analogue of Mittag-Leffler Function,by using(p,q)-Gamma function.Some transformation formulae are also derived by using the(p,q)-derivative.The(p,q)-analogue for this function provides elegant generalization of q-analogue of Mittag-Leffler function in connection with q-calculus.Moreover,the(p,q)-Laplace Transform of the Mittag-Leffler function has been obtained.Some special cases have also been discussed.
文摘Cloud computing is a technology that allows the end-users to access the network through a shared area of resources.As the demand for the cloud computing increases,vulnerabilities in the service provision also increase.EDoS is one of the attacks that take over the provider,financially affecting the various organizations which use the cloud data.This paper utilizes fuzzy entropy and lion neural learner(FLNL)for the classification of cloud users to mitigate EDoS attacks in the cloud.This technique includes a training phase,which creates a log file using various parameters and then transforms the features into database considering certain key features.There are two important stages in this classification approach:feature selection and classification.Here,the fuzzy entropy function is utilized for feature selection which effectively selects useful features without information loss.The classification is performed using lion neural learner(LNL)which incorporates Lion algorithm(LA)into the neural network and uses Levenberg–Marquardt(LM)algorithm.The experimental results finalize that the proposed FLNL is effective with 89%precision,78%recall,and 83.13%of f-measure compared with the existing Na¨ıve Bayes(NB),Neural Network+Back Propagation(NN+BP),and Neural Network+Levenberg–Marquardt(NN+LM).
文摘The genus Eois comprises an important part of megadiverse assemblages of geometrid moths in mountain rainforests of southern Ecuador. In this study we report： （i） on the construction of a DNA barcode library of Eois for identification purposes; and （ii） the exploration of species diversity through species delimitation by pair-wise distance thresholds. COl barcode sequences were generated from 408 individuals （at least 105 species） collected on a narrow geographic scale （-40 km2） in the Reserva Bioldgica San Francisco. Analyses of barcode sequence divergence showed that species delimitations based solely on external morphology result in broad overlap of intra- and interspecific distances. Species delimitation at a 2% pair-wise distance threshold reveals a clear barcoding gap. Fifty-two previously unrecognized species were identified, 31 of which could only be distinguished by an integrative taxonomy approach. Twelve additional putative species could only be recognized by threshold-based delimitation. Most splits resulted in two or three newly perceived cryptic taxa. The present study increased the number of Eois species recorded from that small area of Andean mountain forest from 102 to 154 （morphology- plus integrative taxonomy-based） or even 166 （sequence-based）, leaving the species accumulation curve still far from reaching an asymptote. Notably, in no case did two or more previously distinguished morphospecies have to be lumped. This barcode inventory can be used to match larvae to known adult samples without rearing, and will therefore be of vital help to extend the currently limited knowledge about food plant relationships and host specialization.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
文摘Valvular Heart Disease(VHD),including stenosis and regurgitation,is a significant contributor to global cardiovascular morbidity.Current prosthetic solutions mechanical and bioprosthetic heart valves each present major limitation.Mechanical valves require lifelong anticoagulation due to thrombogenicity,while bioprosthetic valves suffer from structural degeneration and limited durability.Polymeric Heart Valves(PHVs)have emerged as promising alternatives,aiming to integrate the mechanical resilience of synthetic materials with the biocompatibility and hemodynamic performance of natural valves.Recent studies have explored advanced polymers such as Polyhedral Oligomeric Silsesquioxane–Polycarbonate–Urea–Urethane(POSS-PCU),Silicone–Polyurethane Urea(SiPUU),and nanocomposites like Polyvinyl Alcohol(PVA)and SIBS for their enhanced thromboresistance,calcification resistance,and long-term mechanical durability.Complementary to material innovation,fabrication methods such as 3D printing,Melt Electrospinning Writing(MEW),and Focused Rotary Jet Spinning(FRJS)offer patient-specific designs and microstructural control.This review systematically compares traditional and next-generation prostheses,examines mechanical and biological performance,and discusses critical design challenges including porosity,thrombogenicity,and leaflet calcification.Finite Element Analysis(FEA)and Computational Fluid Dynamics(CFD)are highlighted for optimizing design and simulating physiological conditions.By presenting recent preclinical progress and manufacturing strategies,this review outlines a translational roadmap toward clinically viable,biomimetic polymeric heart valves capable of addressing the needs of both adult and pediatric patients.Compared to traditional bioprosthetic tissues,advanced polymers offer better resistance to calcification,reduced thrombogenicity,and tunable mechanical properties.
文摘BACKGROUND Cellular therapies have started an important new therapeutic direction in autistic spectrum disorder(ASD),and the ample diversity of ASD pathophysiology and the different types of cell therapies prompt an equally ample effort to employ clinical studies for studying the ASD causes and cell therapies.Stem cells have yielded so far mixed results in clinical trials,and at patient level the results varied from impressive to no improvement.In this context we have administered autologous cord blood(ACB)and a non-placebo,material intervention repre-sented by an individualized combination of supplements(ICS)to ASD children.METHODS CORDUS clinical study is a crossover study in which both oral ICS and intravenous ACB were sequentially administered to 56 children;ACB was infused as an inpatient procedure.Treatment efficacy was evaluated pre-treatment and post-treatment at 6 months by an independent psychotherapist with Autism Treatment Evaluation Checklist,Quantitative Checklist for Autism in Toddlers and a 16-item comparative table score,after interviewing the children’s parents and therapists.Before and after each intervention participants had a set of blood tests including inflammatory,metabolic and oxidative markers,and the neuronal specific enolase.RESULTS No serious adverse reactions were noted during and after cord blood or supplement administration.ACB improved evaluation scores in 78%of children with age 3–7-years(n=28),but was much less effective in kids older than 8 years or with body weight of more than 35 kg(n=28;only 11%of children improved scores).ICS yielded better results than ACB in 5 cases out of 28,while in 23 kids ACB brought more improvement than ICS(P<0.05);high initial levels of inflammation and ferritin were associated with no improvement.Ample individual differences were noted in children's progress,and statistically significant improvements were seen after ACB on areas such as verbalization and social interaction,but not on irritability or aggressive behavior.CONCLUSION ACB has superior efficacy to ICS in ASD;high inflammation,ferritin,age and body weight predict less improvement;more clinical studies are needed for studying ACB efficacy in ASD.
文摘Mechanical loading constitutes a fundamental determinant in the process of bone remodeling.This modeling encompasses the incorporation of mechanical stimuli,the involvement of cellular and molecular constituents,as well as the utilization of sophisticated computational methodologies.Such an approach is imperative for forecasting bone behaviour across varying environmental conditions.In the present study,key findings from bone mechanobiology are reviewed,along with the possibility that Functionally Graded Materials(FGM)enhances osseointegration and lowers the stress-shielding effect during bone remodeling and compared to titanium,FGM improves periprosthetic bone remodeling.To summarise some of the most important findings from computational models of bone mechanobiology,explaining how modifications to the mechanical environment affect implant design,growth of bone,and bone response.The impact that changes related to the mechanical environment have on bone response is examined using computational models and methods such as surface microtopography to determine how an implant’s bone density has increased over time.This review focuses on the refinement of advanced simulation frameworks and their synergy with imaging technologies to strengthen model validation,ultimately resulting in better clinical outcomes in the context of bone health treatments.
基金supported by the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(No.GTL24011-000)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00272863)supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012748,HRD Program for Industrial Innovation)。
文摘This study presents a novel Li metal host material with a unique hollow nano-spherical structure that incorporates Ag nano-seeds into a graphitic carbon nitride(g-C_(3)N_(4))shell layer,referred to as g-C_(3)N_(4)@Ag hollow spheres.The g-C_(3)N_(4)@Ag spheres provide a managed internal site for Li metal encapsulation and promote stable Li plating.The g-C_(3)N_(4) spheres are uniformly coated using polydopamine,which has an adhesive nature,to enhance lithium plating/stripping stability.The strategic presence of Ag nano-seeds eliminates the nucleation barrier,properly directing Li growth within the hollow spheres.This design facilitates highly reversible and consistent lithium deposition,offering a promising direction for the production of high-performance lithium metal anodes.These well-designed g-C_(3)N_(4)@Ag hollow spheres ensure stable Li plating/stripping kinetics over more than 500 cycles with a high coulombic efficiency of over 97%.Furthermore,a full cell made using LiNi_(0.90)Co_(0.07)Mn_(0.03)O_(2) and Li-g-C_(3)N_(4)@Ag host electrodes demonstrated highly competitive performance over 200 cycles,providing a guide for the implementation of this technology in advanced lithium metal batteries.
基金support from the National Institute of Health(K99AR081897,R00AR081897)M.N.W.acknowledges funding support from the National Institute of Health(P01DK011794,R01DK116716)+1 种基金the Smith Family Foundation Odyssey Award,and the Chen Institute Massachusetts General Hospital Research Scholar(2024-2029)awardμCT and bone histomorphometry were performed by the Center for Skeletal Research at Massachusetts General Hospital,a NIH-funded program(P30AR066261 and AR075042)led by Mary Bouxsein and Marie Demay.
文摘Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role of matrix-embedded osteocytes in OI pathogenesis remains largely unknown.Homozygous human SP7(c.946 C>T,R316C)mutation results in a recessive form of OI characterized by fragility fractures,low bone mineral density and osteocyte dendrite defects.To better understand how the OI-causing R316C mutation affects the function of SP7,we generated Sp7^(R342C)knock-in mice.Consistent with patient phenotypes,Sp7^(R342C/R342C)mice demonstrate increased cortical porosity and reduced cortical bone mineral density.Sp7^(R342C/R342C)mice show osteocyte dendrite defects,increased osteocyte apoptosis,and intracortical bone remodeling with ectopic intracortical osteoclasts and elevated osteocyte Tnfsf11 expression.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
