A new maximal function is introduced in the dual spaces of test function spaces on spaces of homogeneous type. Using this maximal function, we get new characterization of atomic H^p spaces.
This article discussed the benzoic acid activated carbons which have changed the types and content of acid oxygen-function groups on the surface of activated carbons and their effect on the adsorption for Hg^0 in simu...This article discussed the benzoic acid activated carbons which have changed the types and content of acid oxygen-function groups on the surface of activated carbons and their effect on the adsorption for Hg^0 in simulated flue gas at 140 ℃. These surface acid oxygen function groups were identified by Boehm titration, Fourier transformation infrared spectrum, temperature programmed desorption and X-ray photoelectron spectroscopy. It indicates that the carboxyl, lactone and phenolic were formed when the benzoic acid is loaded on the surface of activated carbons. Among the surface acid oxygen function groups, the carboxyl groups enhance the adsorption capacities of Hg^0 for activated carbons to a greater extent.展开更多
The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a c...The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.展开更多
The adsorption of cyanide on the top site of a series of transition metal M（100） （M = Cu, Ag, Au, Ni, Pd, Pt） surfaces via carbon and nitrogen atoms respectively, with the CN axis perpendicular to the surface, has...The adsorption of cyanide on the top site of a series of transition metal M（100） （M = Cu, Ag, Au, Ni, Pd, Pt） surfaces via carbon and nitrogen atoms respectively, with the CN axis perpendicular to the surface, has been studied by means of density functional theory and cluster model. Geometry, adsorption energy and vibrational frequencies have been determined, and the present calculations show that the adsorption of CN through C-end on metal surface is more favorable than that via N-end for the same surface. The vibrational frequencies of CN for C-down configuration on surface are blue-shifted with respect to the free CN, which is contrary to the change of vibrational frequencies when CN is adsorbed by N-down structure. Furthermore, the charge transfer from surface to CN causes the increase of surface work function.展开更多
The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the...The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet Engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).展开更多
文摘A new maximal function is introduced in the dual spaces of test function spaces on spaces of homogeneous type. Using this maximal function, we get new characterization of atomic H^p spaces.
文摘This article discussed the benzoic acid activated carbons which have changed the types and content of acid oxygen-function groups on the surface of activated carbons and their effect on the adsorption for Hg^0 in simulated flue gas at 140 ℃. These surface acid oxygen function groups were identified by Boehm titration, Fourier transformation infrared spectrum, temperature programmed desorption and X-ray photoelectron spectroscopy. It indicates that the carboxyl, lactone and phenolic were formed when the benzoic acid is loaded on the surface of activated carbons. Among the surface acid oxygen function groups, the carboxyl groups enhance the adsorption capacities of Hg^0 for activated carbons to a greater extent.
基金support of the National Key R&D Program of China(No.2022YFC2803903)the Key R&D Program of Zhejiang Province(No.2021C03013)the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F020003).
文摘The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.
基金the National Natural Science Foundation of China (20673019, 20773024)the Natural Science Foundation of Fujian Province (U0650012)the New Century Excellent Talents in University and the Initial Funding for Talents of Fuzhou University (2008-XQ-07, XRC-0732)
文摘The adsorption of cyanide on the top site of a series of transition metal M（100） （M = Cu, Ag, Au, Ni, Pd, Pt） surfaces via carbon and nitrogen atoms respectively, with the CN axis perpendicular to the surface, has been studied by means of density functional theory and cluster model. Geometry, adsorption energy and vibrational frequencies have been determined, and the present calculations show that the adsorption of CN through C-end on metal surface is more favorable than that via N-end for the same surface. The vibrational frequencies of CN for C-down configuration on surface are blue-shifted with respect to the free CN, which is contrary to the change of vibrational frequencies when CN is adsorbed by N-down structure. Furthermore, the charge transfer from surface to CN causes the increase of surface work function.
文摘The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet Engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.70471065)上海市高校选拔培养优秀青年教师科研专项基金(Shanghai Scientific Special Funds For Cultivation and Selection of Excellent Young Teaching Staffs of Higher Education under Grant No.21012)+1 种基金上海市教委科技发展基金(Science and Technology Development Funds of Shanghai Education Commission under Grant No.05EZ31)上海市重点学科建设项目(Shanghai Leading Academic Discipline Project under Grant No.T0502)
