The multifaceted switches are part of our everyday life from the macroscopic to the molecular world.A molecular switch operating in the solution and in the crystalline state is very different.In this review,we summari...The multifaceted switches are part of our everyday life from the macroscopic to the molecular world.A molecular switch operating in the solution and in the crystalline state is very different.In this review,we summarize the state-of-the-art of smart molecular crystal switches based on molecular martensites.These crystal switches respond to external stimuli and reversibly change between states,retaining their macroscopic integrity.The operation of the switches predominantly relies on temperature alterations or mechanical stress,with emerging methods based on photothermal effects,photoisomerization,and host-vip chemistry.The capability of changing the molecular orientation and interaction in smart molecular crystal switches offers opportunities in several applications,including actuators,reversibly shaping structural materials,optoelectronic and magnetic materials,as well as switchable porous materials.Smart molecular crystal switches have vast potential in modern scientific and technological progress.The ongoing research shapes a rich landscape for innovation and future scientific exploration across diverse disciplines.展开更多
The increasing integration of small-scale structures in engineering,particularly in Micro-Electro-Mechanical Systems(MEMS),necessitates advanced modeling approaches to accurately capture their complex mechanical behav...The increasing integration of small-scale structures in engineering,particularly in Micro-Electro-Mechanical Systems(MEMS),necessitates advanced modeling approaches to accurately capture their complex mechanical behavior.Classical continuum theories are inadequate at micro-and nanoscales,particularly concerning size effects,singularities,and phenomena like strain softening or phase transitions.This limitation follows from their lack of intrinsic length scale parameters,crucial for representingmicrostructural features.Theoretical and experimental findings emphasize the critical role of these parameters on small scales.This review thoroughly examines various strain gradient elasticity(SGE)theories commonly employed in literature to capture these size-dependent effects on the elastic response.Given the complexity arising from numerous SGE frameworks available in the literature,including first-and second-order gradient theories,we conduct a comprehensive and comparative analysis of common SGE models.This analysis highlights their unique physical interpretations and compares their effectiveness in modeling the size-dependent behavior of low-dimensional structures.A brief discussion on estimating additional material constants,such as intrinsic length scales,is also included to improve the practical relevance of SGE.Following this theoretical treatment,the review covers analytical and numerical methods for solving the associated higher-order governing differential equations.Finally,we present a detailed overview of strain gradient applications in multiscale andmultiphysics response of solids.Interesting research on exploring the relevance of SGE for reduced-order modeling of complex macrostructures,a universal multiphysics coupling in low-dimensional structures without being restricted to limited material symmetries(as in the case of microstructures),is also presented here for interested readers.Finally,we briefly discuss alternative nonlocal elasticity approaches(integral and integro-differential)for incorporating size effects,and conclude with some potential areas for future research on strain gradients.This review aims to provide a clear understanding of strain gradient theories and their broad applicability beyond classical elasticity.展开更多
Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when impl...Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.展开更多
The CIM （common information model） is an abstract information model that can be used to model an electrical network and various equipments used on the network. By using a common model, utilities and vendors can redu...The CIM （common information model） is an abstract information model that can be used to model an electrical network and various equipments used on the network. By using a common model, utilities and vendors can reduce their integration costs, which should allow more resources to be applied toward increased functionality for managing and optimizing the electrical system. As a part of smart grid, the SPG （smart power grid） was built on Jeju Island. The SPG consists of IDAS （intelligent distribution automation system）, substation automation system, intelligent transmission system, and active telemetrics system. To integrate these systems which have different operating systems and platforms CIM standard was used. But IDAS has many functions and advanced algorithms not defined in CIM. In this paper, the authors introduce how to develop and extend the CIM model for managing the IDAS.展开更多
Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,w...Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.展开更多
基金supported by the National Natural Science Foundation of China(51773077 and 52173164 H.Z)the Natural Science Foundation of Jilin Province(20230101038JC,H.Z)+1 种基金a fund from New York University Abu Dhabi(P.N)This material is based upon works supported by Tamkeen under NYUAD RRC Grant No.CG011.
文摘The multifaceted switches are part of our everyday life from the macroscopic to the molecular world.A molecular switch operating in the solution and in the crystalline state is very different.In this review,we summarize the state-of-the-art of smart molecular crystal switches based on molecular martensites.These crystal switches respond to external stimuli and reversibly change between states,retaining their macroscopic integrity.The operation of the switches predominantly relies on temperature alterations or mechanical stress,with emerging methods based on photothermal effects,photoisomerization,and host-vip chemistry.The capability of changing the molecular orientation and interaction in smart molecular crystal switches offers opportunities in several applications,including actuators,reversibly shaping structural materials,optoelectronic and magnetic materials,as well as switchable porous materials.Smart molecular crystal switches have vast potential in modern scientific and technological progress.The ongoing research shapes a rich landscape for innovation and future scientific exploration across diverse disciplines.
基金support from the Anusandhan National Research Foundation(ANRF),erstwhile Science and Engineering Research Board(SERB),India,under the startup research grant program(SRG/2022/000566).
文摘The increasing integration of small-scale structures in engineering,particularly in Micro-Electro-Mechanical Systems(MEMS),necessitates advanced modeling approaches to accurately capture their complex mechanical behavior.Classical continuum theories are inadequate at micro-and nanoscales,particularly concerning size effects,singularities,and phenomena like strain softening or phase transitions.This limitation follows from their lack of intrinsic length scale parameters,crucial for representingmicrostructural features.Theoretical and experimental findings emphasize the critical role of these parameters on small scales.This review thoroughly examines various strain gradient elasticity(SGE)theories commonly employed in literature to capture these size-dependent effects on the elastic response.Given the complexity arising from numerous SGE frameworks available in the literature,including first-and second-order gradient theories,we conduct a comprehensive and comparative analysis of common SGE models.This analysis highlights their unique physical interpretations and compares their effectiveness in modeling the size-dependent behavior of low-dimensional structures.A brief discussion on estimating additional material constants,such as intrinsic length scales,is also included to improve the practical relevance of SGE.Following this theoretical treatment,the review covers analytical and numerical methods for solving the associated higher-order governing differential equations.Finally,we present a detailed overview of strain gradient applications in multiscale andmultiphysics response of solids.Interesting research on exploring the relevance of SGE for reduced-order modeling of complex macrostructures,a universal multiphysics coupling in low-dimensional structures without being restricted to limited material symmetries(as in the case of microstructures),is also presented here for interested readers.Finally,we briefly discuss alternative nonlocal elasticity approaches(integral and integro-differential)for incorporating size effects,and conclude with some potential areas for future research on strain gradients.This review aims to provide a clear understanding of strain gradient theories and their broad applicability beyond classical elasticity.
基金funded by the McGill University Graduate Excellence Fellowship Award(00157)the Mitacs Accelerate Program(IT13369)the McGill Engineering Doctoral Award(MEDA).
文摘Machine learning(ML)has recently enabled many modeling tasks in design,manufacturing,and condition monitoring due to its unparalleled learning ability using existing data.Data have become the limiting factor when implementing ML in industry.However,there is no systematic investigation on how data quality can be assessed and improved for ML-based design and manufacturing.The aim of this survey is to uncover the data challenges in this domain and review the techniques used to resolve them.To establish the background for the subsequent analysis,crucial data terminologies in ML-based modeling are reviewed and categorized into data acquisition,management,analysis,and utilization.Thereafter,the concepts and frameworks established to evaluate data quality and imbalance,including data quality assessment,data readiness,information quality,data biases,fairness,and diversity,are further investigated.The root causes and types of data challenges,including human factors,complex systems,complicated relationships,lack of data quality,data heterogeneity,data imbalance,and data scarcity,are identified and summarized.Methods to improve data quality and mitigate data imbalance and their applications in this domain are reviewed.This literature review focuses on two promising methods:data augmentation and active learning.The strengths,limitations,and applicability of the surveyed techniques are illustrated.The trends of data augmentation and active learning are discussed with respect to their applications,data types,and approaches.Based on this discussion,future directions for data quality improvement and data imbalance mitigation in this domain are identified.
文摘The CIM （common information model） is an abstract information model that can be used to model an electrical network and various equipments used on the network. By using a common model, utilities and vendors can reduce their integration costs, which should allow more resources to be applied toward increased functionality for managing and optimizing the electrical system. As a part of smart grid, the SPG （smart power grid） was built on Jeju Island. The SPG consists of IDAS （intelligent distribution automation system）, substation automation system, intelligent transmission system, and active telemetrics system. To integrate these systems which have different operating systems and platforms CIM standard was used. But IDAS has many functions and advanced algorithms not defined in CIM. In this paper, the authors introduce how to develop and extend the CIM model for managing the IDAS.
基金via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.
