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
Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods i...Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods is relatively rare.Because most terrestrial arthropods possess hardened sclerites and appendages,it is possible that snakes that feed on arthropods would show specialized prey-handling behavior.In this study,we describe prey-handling behavior of a snake feeding on terrestrial arthropods,which hitherto has not been well documented.We focused on Rhabdophis chiwen,which mainly feeds on earthworms,but also consumes lampyrine firefly larvae,sequestering cardiotonic steroids from them in its defensive organs,called nucho-dorsal glands.When feeding on earthworms,snakes showed size-dependent selection of swallowing direction,but this tendency was not observed when feeding on firefly larvae.Manipulation of firefly larvae did not seem to be efficient,probably because they possess sclerites and appendages such as legs that impede smooth handling.Although fireflies are an essential food for R.chiwen as a toxin source,our results showed that the snake is not adept at handling firefly larvae compared to earthworms,implying that dietary specialization does not necessarily accompany behavioral specialization.We discuss possible reasons for this inconsistency.展开更多
The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation ...The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.展开更多
Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, whic...Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, which is the first case from Diaphanes, was identified and sequenced. The luciferase gene from D. pectinealis spans 1958 base pairs （bp） from the start to the stop codon, including seven exons separated by six introns, and encoding a 547-residuelong polypeptide. Its deduced amino acid sequence showed high protein similarity to those of the Lampyrini tribe （93 - 94% ） and the Cratomorphini tribe （92%）, while low similarity was found with the North American firefly Photinus pyralis （83%） of the Photinini tribe within the same subfamily Lampyrinae. The phylogenetic analysis performed with the deduced amino acid sequences of the luciferase gene further confirms that D. pectinealis, Pyrocoelia, Lampyris, Cratomorphus, and Photinus belong to the same subfamily Lampyrinae, and Diaphanes is closely related to Pyrocoelia, Lampyris, and Cratomorphus. Furthemore, the phylogenetic analysis based on the nucleotide sequences of the luciferase gene indicates Diaphanes is a sister to Lampyris. The phylogenetic analyses are partly consistent with morphological （Branham ＆ Wenzel, 2003） and mitochondrial DNA analyses （Li et al, 2006）.展开更多
To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is ex...To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.展开更多
Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity pro...Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity profile and stratigraphic information from Rayleigh waves. We choose the Firefly algorithm for inversion of surface waves. The Firefly algorithm, a new type of particle swarm optimization, has the advantages of being robust, highly effective, and allows global searching. This algorithm is feasible and has advantages for use in Rayleigh wave inversion with both synthetic models and field data. The results show that the Firefly algorithm, which is a robust and practical method, can achieve nonlinear inversion of surface waves with high resolution.展开更多
The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 bas...The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 base pairs in length, coding a protein of 548 amino acid residues. Sequence analysis of the deduced amino acid sequence showed that this luciferase had 97.8% resemblance to luciferases from the fireflies Lampyris noctiluca, Lampyris turkestanicus and Nyctophila cf. caucasica. Phylogenetic analysis using deduced amino acid sequence showed that P pygidialis located at the base of Lampyris＋Nyctophila clade with robust support （BP=97%）; but did not show a monophyletic relationship with its congeneric species P pectoralis, P tufa and P miyako, all three are strong luminous and nocturnal species. The expression worked in recombinant Escherichia coli. Expression product had a 70kDa band and emitted yellow-green luminescence in the presence of luciferin. Five loops in the P pygidialis luciferase, L1 （NI98-G208）, L2 （T240-G247）, L3 （G317-K322）, L4 （L343-I350） and L5 （G522-D532）, were found from the structure modeling analysis in the cleft, where it was considered the active site for the substrate compound entering and binding. Different amino acid residues between the luciferases of P. pygidialis and the three other known strong luminous species can not explain the situation of weak or strong luminescence. Future study of these loops, residues or crystal structure analysis may be helpful in understanding the real differences between the luciferases between diurnal and nocturnal species.展开更多
Fifty-five-year-old Shi Yizhong never imagined fireflies could become the foundation of a serious business.In 2022,Longshan Village in Huzhou’s Wuxing District collaborated with a young entrepreneurial team to create...Fifty-five-year-old Shi Yizhong never imagined fireflies could become the foundation of a serious business.In 2022,Longshan Village in Huzhou’s Wuxing District collaborated with a young entrepreneurial team to create a firefly campsite-a natural attraction where visitors can observe these glowing insects in a preserved habitat.The site quickly drew waves of tourists,who shared their experience online,recouping its initial investment in just two years.展开更多
Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mo...Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.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.
基金supported in part by JST SPRING(JPMJSP2110)by Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research(18KK0205,21H02551)Shosuke Takeuchi for personal funding.
文摘Dietary specialists consume specific prey items,and they are often morphologically and behaviorally specialized to feed efficiently on those prey animals.Among specialist snakes,consumption of terrestrial arthropods is relatively rare.Because most terrestrial arthropods possess hardened sclerites and appendages,it is possible that snakes that feed on arthropods would show specialized prey-handling behavior.In this study,we describe prey-handling behavior of a snake feeding on terrestrial arthropods,which hitherto has not been well documented.We focused on Rhabdophis chiwen,which mainly feeds on earthworms,but also consumes lampyrine firefly larvae,sequestering cardiotonic steroids from them in its defensive organs,called nucho-dorsal glands.When feeding on earthworms,snakes showed size-dependent selection of swallowing direction,but this tendency was not observed when feeding on firefly larvae.Manipulation of firefly larvae did not seem to be efficient,probably because they possess sclerites and appendages such as legs that impede smooth handling.Although fireflies are an essential food for R.chiwen as a toxin source,our results showed that the snake is not adept at handling firefly larvae compared to earthworms,implying that dietary specialization does not necessarily accompany behavioral specialization.We discuss possible reasons for this inconsistency.
基金the Deanship of Graduate Studies and Scientific Research at Najran University for funding this work under the Easy Funding Program grant code(NU/EFP/SERC/13/166).
文摘The Internet of Things(IoT)has emerged as an important future technology.IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data.In IoT-Fog computing,resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers.The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem.This study proposes an Adaptive Firefly Algorithm(AFA)for dependent task scheduling in IoT-Fog computing.The proposed AFA is a modified version of the standard Firefly Algorithm(FA),considering the execution times of the submitted tasks,the impact of synchronization requirements,and the communication time between dependent tasks.As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner,tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem.The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments.The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations.The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm(FA),Puma Optimizer(PO),Genetic Algorithm(GA),and Ant Colony Optimization(ACO)through simulations under light,typical,and heavy workload scenarios.In heavy workloads,the proposed AFA mechanism obtained the shortest average execution time,968.98 ms compared to 970.96,1352.87,1247.28,and 1773.62 of FA,PO,GA,and ACO,respectively.The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions,emphasizing its adaptability and efficiency in typical and heavy workloads.
文摘Diaphanes is the fourth largest genus in Lampyridae, but no luciferase gene from this genus has been reported. In this paper, by PCR amplification of the genomic DNA, the luciferase gene of Diaphanes pectinealis, which is the first case from Diaphanes, was identified and sequenced. The luciferase gene from D. pectinealis spans 1958 base pairs （bp） from the start to the stop codon, including seven exons separated by six introns, and encoding a 547-residuelong polypeptide. Its deduced amino acid sequence showed high protein similarity to those of the Lampyrini tribe （93 - 94% ） and the Cratomorphini tribe （92%）, while low similarity was found with the North American firefly Photinus pyralis （83%） of the Photinini tribe within the same subfamily Lampyrinae. The phylogenetic analysis performed with the deduced amino acid sequences of the luciferase gene further confirms that D. pectinealis, Pyrocoelia, Lampyris, Cratomorphus, and Photinus belong to the same subfamily Lampyrinae, and Diaphanes is closely related to Pyrocoelia, Lampyris, and Cratomorphus. Furthemore, the phylogenetic analysis based on the nucleotide sequences of the luciferase gene indicates Diaphanes is a sister to Lampyris. The phylogenetic analyses are partly consistent with morphological （Branham ＆ Wenzel, 2003） and mitochondrial DNA analyses （Li et al, 2006）.
基金The National Natural Science Foundation of China(No.50805023)the Science and Technology Support Program of Jiangsu Province(No.BE2008081)+1 种基金the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2010093)the Program for Special Talent in Six Fields of Jiangsu Province(No.2008144)
文摘To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.
基金supported by the National Basic Research Program of China(No.2013CB228602)the National Science and Technology Major Project of China(No.2011ZX05004-003)the National High Technology Research Program of China(No.2013AA064202)
文摘Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity profile and stratigraphic information from Rayleigh waves. We choose the Firefly algorithm for inversion of surface waves. The Firefly algorithm, a new type of particle swarm optimization, has the advantages of being robust, highly effective, and allows global searching. This algorithm is feasible and has advantages for use in Rayleigh wave inversion with both synthetic models and field data. The results show that the Firefly algorithm, which is a robust and practical method, can achieve nonlinear inversion of surface waves with high resolution.
基金the Natural Foundation of Sciences of Yunnan Province (2006C0046Q)Partly by the Chinese Academy of Sciences (O706551141)
文摘The cDNA encoding the luciferase from lantern mRNA of one diurnal firefly Pyrocoelia pygidialis Pic, 1926 has been cloned, sequenced and functionally expressed. The cDNA sequence of P pygidialis luciferase is 1647 base pairs in length, coding a protein of 548 amino acid residues. Sequence analysis of the deduced amino acid sequence showed that this luciferase had 97.8% resemblance to luciferases from the fireflies Lampyris noctiluca, Lampyris turkestanicus and Nyctophila cf. caucasica. Phylogenetic analysis using deduced amino acid sequence showed that P pygidialis located at the base of Lampyris＋Nyctophila clade with robust support （BP=97%）; but did not show a monophyletic relationship with its congeneric species P pectoralis, P tufa and P miyako, all three are strong luminous and nocturnal species. The expression worked in recombinant Escherichia coli. Expression product had a 70kDa band and emitted yellow-green luminescence in the presence of luciferin. Five loops in the P pygidialis luciferase, L1 （NI98-G208）, L2 （T240-G247）, L3 （G317-K322）, L4 （L343-I350） and L5 （G522-D532）, were found from the structure modeling analysis in the cleft, where it was considered the active site for the substrate compound entering and binding. Different amino acid residues between the luciferases of P. pygidialis and the three other known strong luminous species can not explain the situation of weak or strong luminescence. Future study of these loops, residues or crystal structure analysis may be helpful in understanding the real differences between the luciferases between diurnal and nocturnal species.
文摘Fifty-five-year-old Shi Yizhong never imagined fireflies could become the foundation of a serious business.In 2022,Longshan Village in Huzhou’s Wuxing District collaborated with a young entrepreneurial team to create a firefly campsite-a natural attraction where visitors can observe these glowing insects in a preserved habitat.The site quickly drew waves of tourists,who shared their experience online,recouping its initial investment in just two years.
文摘Autonomous mobile robot navigation is one of the most emerging areas of research by using swarm intelligence. Path planning and obstacle avoidance are most researched current topics like navigational challenges for mobile robot. The paper presents application and implementation of Firefly Algorithm(FA)for Mobile Robot Navigation(MRN) in uncertain environment. The uncertainty is defined over the changing environmental condition from static to dynamic. The attraction of one firefly towards the other firefly due to variation of their brightness is the key concept of the proposed study. The proposed controller efficiently explores the environment and improves the global search in less number of iterations and hence it can be easily implemented for real time obstacle avoidance especially for dynamic environment. It solves the challenges of navigation, minimizes the computational calculations, and avoids random moving of fireflies. The performance of proposed controller is better in terms of path optimality when compared to other intelligent navigational approaches.
