Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
The development of electronic circuits designed to emulate the functionality of biological neural networks has increased significantly in recent years.Specifically,memristor-based neuromorphic operation has been demon...The development of electronic circuits designed to emulate the functionality of biological neural networks has increased significantly in recent years.Specifically,memristor-based neuromorphic operation has been demonstrated using various material combinations.One class of devices replicates the ion-concentrationgradient buildup that precedes neurotransmitter release in biological synapses.Some of these devices incorporate amino-acid-rich solutions as an active layer.This work presents a density functional theory study of such a device.The interaction between an Ag-filamentary memristor and different Hydrogen concentrations in a tyrosine-rich environment was evaluated.Two mutually exclusive structures were studied,and the resulting source-to-drain currents were compared with experimental observations.One structure was based on Tyrosine-H blocks linked to Ag atoms as a charge conduction path,while the other placed these blocks in parallel with Ag partial filaments between the source and drain.The results indicate that the second aligns with experiments and supports the hypothesis that tyrosine can act as an enabler for proton-mediated charge transport.Furthermore,the insights into the electronic transport properties of specific molecules can provide a theoretical background for designing advanced Hydrogen sensors and amino acid detectors.展开更多
This paper tries to summarize the attempts to apply artificial intelligence (AI) to power systems,particularly power system planning and operations which require significant computer analysis.Although the term AI was ...This paper tries to summarize the attempts to apply artificial intelligence (AI) to power systems,particularly power system planning and operations which require significant computer analysis.Although the term AI was coined earlier,this paper considers the beginning to be in the 1980s when the first expert systems were applied to power engineering.Of course,many of the analytical techniques applied can be traced to earlier statistical analysis and pattern recognition.The concept of expert systems was very much in line with the concept of AI.The various methods for applying AI to power systems are traced here.The historical journey in this paper closes with the great explosion of AI applications in the last decade when almost all power system analysis is trying to utilize AI techniques to help the transformation of the power system into a more efficient and carbon-free system.This proliferation of research in the application of AI is covered in the other papers in this series.展开更多
Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced ...Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power systems.New analysis and control methods are needed for power systems to cope with the ongoing transformation.In the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power systems.Specifically,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.展开更多
Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑...Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment protocol.Compared with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space complexity.To tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)length.In this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings.Compared with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes.Particularly,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×smaller.Our constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge assumptions.We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.展开更多
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
文摘The development of electronic circuits designed to emulate the functionality of biological neural networks has increased significantly in recent years.Specifically,memristor-based neuromorphic operation has been demonstrated using various material combinations.One class of devices replicates the ion-concentrationgradient buildup that precedes neurotransmitter release in biological synapses.Some of these devices incorporate amino-acid-rich solutions as an active layer.This work presents a density functional theory study of such a device.The interaction between an Ag-filamentary memristor and different Hydrogen concentrations in a tyrosine-rich environment was evaluated.Two mutually exclusive structures were studied,and the resulting source-to-drain currents were compared with experimental observations.One structure was based on Tyrosine-H blocks linked to Ag atoms as a charge conduction path,while the other placed these blocks in parallel with Ag partial filaments between the source and drain.The results indicate that the second aligns with experiments and supports the hypothesis that tyrosine can act as an enabler for proton-mediated charge transport.Furthermore,the insights into the electronic transport properties of specific molecules can provide a theoretical background for designing advanced Hydrogen sensors and amino acid detectors.
文摘This paper tries to summarize the attempts to apply artificial intelligence (AI) to power systems,particularly power system planning and operations which require significant computer analysis.Although the term AI was coined earlier,this paper considers the beginning to be in the 1980s when the first expert systems were applied to power engineering.Of course,many of the analytical techniques applied can be traced to earlier statistical analysis and pattern recognition.The concept of expert systems was very much in line with the concept of AI.The various methods for applying AI to power systems are traced here.The historical journey in this paper closes with the great explosion of AI applications in the last decade when almost all power system analysis is trying to utilize AI techniques to help the transformation of the power system into a more efficient and carbon-free system.This proliferation of research in the application of AI is covered in the other papers in this series.
文摘Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power systems.New analysis and control methods are needed for power systems to cope with the ongoing transformation.In the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power systems.Specifically,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.
基金supported by the National Key R&D Program of China under Grant 2020YFA0712303Zhedong Wang is supported by National Natural Science Foundation of China(Grant No.62202305)Shanghai Pujiang Program under Grant 22PJ1407700.
文摘Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment protocol.Compared with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space complexity.To tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)length.In this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings.Compared with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes.Particularly,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×smaller.Our constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge assumptions.We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.
