Showing papers by "Nanjing University of Information Science and Technology published in 2018"

Novel MOF‐Derived Co@N‐C Bifunctional Catalysts for Highly Efficient Zn–Air Batteries and Water Splitting

Abstract: Metal-organic frameworks (MOFs) and MOF-derived materials have recently attracted considerable interest as alternatives to noble-metal electrocatalysts. Herein, the rational design and synthesis of a new class of Co@N-C materials (C-MOF-C2-T) from a pair of enantiotopic chiral 3D MOFs by pyrolysis at temperature T is reported. The newly developed C-MOF-C2-900 with a unique 3D hierarchical rodlike structure, consisting of homogeneously distributed cobalt nanoparticles encapsulated by partially graphitized N-doped carbon rings along the rod length, exhibits higher electrocatalytic activities for oxygen reduction and oxygen evolution reactions (ORR and OER) than that of commercial Pt/C and RuO2 , respectively. Primary Zn-air batteries based on C-MOF-900 for the oxygen reduction reaction (ORR) operated at a discharge potential of 1.30 V with a specific capacity of 741 mA h gZn-1 under 10 mA cm-2 . Rechargeable Zn-air batteries based on C-MOF-C2-900 as an ORR and OER bifunctional catalyst exhibit initial charge and discharge potentials at 1.81 and 1.28 V (2 mA cm-2 ), along with an excellent cycling stability with no increase in polarization even after 120 h - outperform their counterparts based on noble-metal-based air electrodes. The resultant rechargeable Zn-air batteries are used to efficiently power electrochemical water-splitting systems, demonstrating promising potential as integrated green energy systems for practical applications.

A self-adaptive artificial bee colony algorithm based on global best for global optimization

Abstract: Intelligent optimization algorithms based on evolutionary and swarm principles have been widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent swarm algorithm for global optimization problems. Previous studies have shown that the ABC algorithm is an efficient, effective, and robust optimization method. However, the solution search equation used in ABC is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although some complex strategies for generating candidate solutions have recently been developed, the universality and robustness of these new algorithms are still insufficient. This is mainly because only one strategy is adopted in the modified ABC algorithm. In this paper, we propose a self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization. Experiments are conducted on a set of 25 benchmark functions. To ensure a fair comparison with other algorithms, we employ the same initial population for all algorithms on each benchmark function. Besides, to validate the feasibility of SABC-GB in real-world application, we demonstrate its application to a real clustering problem based on the K-means technique. The results demonstrate that SABC-GB is superior to the other algorithms for solving complex optimization problems. It means that it is a new technique to improve the ABC by introducing self-adaptive mechanism.

Industrial structure, technical progress and carbon intensity in China's provinces

Abstract: This paper uses dynamic spatial panel models to analyze the effects of industrial structure and technical progress on carbon intensity in order to explore those factors that may lead to a reduction in carbon intensity in China. The results show that there is both significant positive spatial autocorrelation and spatial heterogeneity between provinces in carbon intensity. Although upgrading and optimization of industrial structure are conducive to reducing carbon intensity, in China it is technical progress that plays the most important role. Efficiency change is a leading factor in the reduction of carbon intensity by technical progress, but the effect of technical change is not significant because of the carbon emissions rebound effect. Although technical change itself cannot directly reduce carbon intensity, it can indirectly do so by promoting the upgrading and optimization of industrial structure. Both structure of urbanization and coal-dominated energy consumption increase carbon intensity but the relationship between foreign direct investment and carbon intensity is not significant. Based on these empirical findings, we put forward specific suggestions for the Chinese government in their quest to reduce carbon intensity.

Drought losses in China might double between the 1.5 °C and 2.0 °C warming.

Abstract: We project drought losses in China under global temperature increase of 1.5 °C and 2.0 °C, based on the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI), a cluster analysis method, and "intensity-loss rate" function. In contrast to earlier studies, to project the drought losses, we predict the regional gross domestic product under shared socioeconomic pathways instead of using a static socioeconomic scenario. We identify increasing precipitation and evapotranspiration pattern for the 1.5 °C and 2.0 °C global warming above the preindustrial at 2020-2039 and 2040-2059, respectively. With increasing drought intensity and areal coverage across China, drought losses will soar. The estimated loss in a sustainable development pathway at the 1.5 °C warming level increases 10-fold in comparison with the reference period 1986-2005 and nearly threefold relative to the interval 2006-2015. However, limiting the temperature increase to 1.5 °C can reduce the annual drought losses in China by several tens of billions of US dollars, compared with the 2.0 °C warming.

Hierarchical Z-scheme photocatalyst of g-C3N4@Ag/BiVO4 (040) with enhanced visible-light-induced photocatalytic oxidation performance

Abstract: The preferred exposure of (040) crystal facet of BiVO 4 is conductive to optimizing its photocatalytic performance. And the great separation of photoinduced electron-hole pairs is also a critical factor for semiconductor photocatalyst. Herein we designed a hierarchical g-C 3 N 4 @Ag/BiVO 4 (040) hybrid photocatalyst, in which Ag was photodeposited on the (040) facets of BiVO 4 and subsequently g-C 3 N 4 was covered on the surface of Ag/BiVO 4 (040). The physical and chemical properties of the synthetic samples were analyzed by several characterization techniques. SEM spectrum clearly reveals the morphology and structure of g-C 3 N 4 @Ag/BiVO 4 (040), suggesting the existence of the hierarchical composite photocatalyst. The visible light absorption wavelength of the composite is increased due to the surface plasmon resonance (SPR) effect of metal Ag nanoparticles, displayed in UV–vis spectrum. The photogenerated electron-hole pairs are also greatly enhanced through the Z-scheme g-C 3 N 4 @Ag/BiVO 4 (040) system with the Ag nanoparticles as the electron mediator. The above synergistic effects of the hybrid photocatalyst result in higher photocatalytic oxidation performance not only for water splitting but also for NO oxidation in gas phase compared with pure BiVO 4 .

Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

Abstract: In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.

Emergency decision making for natural disasters: An overview

Abstract: With the increasing trend of global warming, the frequent occurrences of natural disasters have brought serious challenges to the sustainable development of the society. Therefore, emergency decision making (EDM) for natural disasters plays an increasingly significant role in improving the capability to respond disasters. In this paper, we first elaborate the concept and characteristics of EDM for natural disasters and briefly expound emergency decision contents in different stages of natural disasters. Then, an overview is provided for the EDM theory and methods of natural disasters from the methodological perspective. After that, we give a detailed illustration of the construction of emergency decision support system. Finally, we summarize the main conclusions of the paper and point out the prospect of future researches.

Polar amplification dominated by local forcing and feedbacks

Abstract: The surface temperature response to greenhouse gas forcing displays a characteristic pattern of polar-amplified warming1–5, particularly in the Northern Hemisphere. However, the causes of this polar amplification are still debated. Some studies highlight the importance of surface-albedo feedback6–8, while others find larger contributions from longwave feedbacks4,9,10, with changes in atmospheric and oceanic heat transport also thought to play a role11–16. Here, we determine the causes of polar amplification using climate model simulations in which CO2 forcing is prescribed in distinct geographical regions, with the linear sum of climate responses to regional forcings replicating the response to global forcing. The degree of polar amplification depends strongly on the location of CO2 forcing. In particular, polar amplification is found to be dominated by forcing in the polar regions, specifically through positive local lapse-rate feedback, with ice-albedo and Planck feedbacks playing subsidiary roles. Extra-polar forcing is further shown to be conducive to polar warming, but given that it induces a largely uniform warming pattern through enhanced poleward heat transport, it contributes little to polar amplification. Therefore, understanding polar amplification requires primarily a better insight into local forcing and feedbacks rather than extra-polar processes. Model simulations with CO2 forcing prescribed in discrete geographical regions reveal that polar amplification arises primarily due to local lapse-rate feedback, with ice-albedo and Planck feedbacks playing subsidiary roles.

Text classification based on deep belief network and softmax regression

Abstract: In this paper, we propose a novel hybrid text classification model based on deep belief network and softmax regression. To solve the sparse high-dimensional matrix computation problem of texts data, a deep belief network is introduced. After the feature extraction with DBN, softmax regression is employed to classify the text in the learned feature space. In pre-training procedures, the deep belief network and softmax regression are first trained, respectively. Then, in the fine-tuning stage, they are transformed into a coherent whole and the system parameters are optimized with Limited-memory Broyden---Fletcher---Goldfarb---Shanno algorithm. The experimental results on Reuters-21,578 and 20-Newsgroup corpus show that the proposed model can converge at fine-tuning stage and perform significantly better than the classical algorithms, such as SVM and KNN.

Anonymous and Traceable Group Data Sharing in Cloud Computing

Abstract: Group data sharing in cloud environments has become a hot topic in recent decades. With the popularity of cloud computing, how to achieve secure and efficient data sharing in cloud environments is an urgent problem to be solved. In addition, how to achieve both anonymity and traceability is also a challenge in the cloud for data sharing. This paper focuses on enabling data sharing and storage for the same group in the cloud with high security and efficiency in an anonymous manner. By leveraging the key agreement and the group signature, a novel traceable group data sharing scheme is proposed to support anonymous multiple users in public clouds. On the one hand, group members can communicate anonymously with respect to the group signature, and the real identities of members can be traced if necessary. On the other hand, a common conference key is derived based on the key agreement to enable group members to share and store their data securely. Note that a symmetric balanced incomplete block design is utilized for key generation, which substantially reduces the burden on members to derive a common conference key. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.

Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations

Abstract: Large-scale group decision making (LSGDM) or complex group decision making (GDM) problems are very commonly encountered in actual life, especially in the era of data. At present, double hierarchy hesitant fuzzy linguistic term set is a reasonable linguistic expression when describing some complex linguistic preference information. In this paper, we develop a consensus reaching process for LSGDM with double hierarchy hesitant fuzzy linguistic preference relations. To ensure the implementation of consensus reaching process, we also propose the similarity degree-based clustering method, the double hierarchy information entropy-based weights-determining method and the consensus measures. Finally, we apply our model to deal with a practical problem that is to evaluate Sichuan water resource management and make some comparisons with the existing approaches.

Multi-attribute group decision-making under probabilistic uncertain linguistic environment

Abstract: Existing decision-making methods cannot work under the probabilistic uncertain linguistic environment where the decision makers give different uncertain linguistic terms as their assessments and the weights of assessments are different. In this paper, a novel concept called probabilistic uncertain linguistic term set is proposed, which is composed of some possible uncertain linguistic terms associated with the corresponding probabilities. Then, the normalization process, comparison method, basic operations, and aggregation operators are studied for probabilistic uncertain linguistic term sets. After that, an extended technique for order preference by similarity to an ideal solution method and an aggregation-based method are developed to rank the alternatives and then select the best one for multi-attribute group decision-making with probabilistic uncertain linguistic information. Finally, a practical case concerning the selection of Cloud storage services is shown to illustrate the applicability o...

An ID-Based Linearly Homomorphic Signature Scheme and Its Application in Blockchain

Abstract: Identity-based cryptosystems mean that public keys can be directly derived from user identifiers, such as telephone numbers, email addresses, and social insurance number, and so on. So they can simplify key management procedures of certificate-based public key infrastructures and can be used to realize authentication in blockchain. Linearly homomorphic signature schemes allow to perform linear computations on authenticated data. And the correctness of the computation can be publicly verified. Although a series of homomorphic signature schemes have been designed recently, there are few homomorphic signature schemes designed in identity-based cryptography. In this paper, we construct a new ID-based linear homomorphic signature scheme, which avoids the shortcomings of the use of public-key certificates. The scheme is proved secure against existential forgery on adaptively chosen message and ID attack under the random oracle model. The ID-based linearly homomorphic signature schemes can be applied in e-business and cloud computing. Finally, we show how to apply it to realize authentication in blockchain.

Robust color image watermarking technique in the spatial domain

Abstract: This paper proposes a new blind watermarking algorithm, which embedding the binary watermark into the blue component of a RGB image in the spatial domain, to resolve the problem of protecting copyright. For embedding watermark, the generation principle and distribution features of direct current (DC) coefficient are used to directly modify the pixel values in the spatial domain, and then four different sub-watermarks are embedded into the different areas of the host image for four times, respectively. When watermark extraction, the sub-watermark is extracted with blind manner according to DC coefficients of watermarked image and the key-based quantization step, and then the statistical rule and the method of “first to select, second to combine” are proposed to form the final watermark. Hence, the proposed algorithm is executed in the spatial domain rather than in discrete cosine transform (DCT) domain, which not only has simple and quick performance of the spatial domain but also has high robustness feature of DCT domain. The experimental results show that the proposed watermarking algorithm can obtain better invisibility of watermark and stronger robustness for common attacks, e.g., JPEG compression, cropping, and adding noise. Comparison results also show the advantages of the proposed method.

User Collusion Avoidance CP-ABE With Efficient Attribute Revocation for Cloud Storage

Abstract: Attribute-based encryption (ABE) can guarantee confidentiality and achieve fine-grained data access control in a cloud storage system. Due to the fact that every attribute in ABE may be shared by multiple users and each user holds multiple attributes, any single-attribute revocation for some user may affect the other users with the same attribute in the system. Therefore, how to revoke attribute efficiently is an important and challenging problem in ABE schemes. In order to solve above problems, we first give a concrete attack to the existing ABE scheme with attribute revocation. Then, we formalize the definition and security model, which model collusion attack executed by the existing users cooperating with the revoked users. Finally, we present a user collusion avoidance ciphertext-policy ABE scheme with efficient attribute revocation for the cloud storage system. The problem of attribute revocation is solved efficiently by exploiting the concept of an attribute group. When an attribute is revoked from a user, the group manager updates other users’ secret keys. Furthermore, we prove that the proposed scheme is secure against collusion attack launched by the existing users and the revoked users. The security of the proposed scheme is reduced to the computational Diffie–Hellman assumption.

Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution

Abstract: An improvement in industrial carbon emission efficiency is crucial for achieving both reductions in carbon emissions and sustainable economic growth. In this paper, we use an improved non-radial directional distance function (NDDF) to construct a new meta-frontier total-factor carbon emission efficiency index (TCEI) with which we estimate the meta-frontier TCEI of China's 30 provincial industrial sectors in 2005–2015 and analyze their dynamic evolution. The results show that compared to traditional NDDF, the improved NDDF has more advantages in measuring both carbon emission efficiency and the technology gap ratio. For the study period, China's industrial meta-frontier TCEI is low, indicating that the industrial TCEI of many provinces still has much room for improvement. The meta-frontier TCEI has significant inter-group heterogeneity, with Eastern China having the largest carbon emission efficiency, followed by Central China, and Western China having the lowest. China's industrial meta-frontier TCEI increased significantly during the study period with technical progress playing a major role in promoting it. Over time, however, the meta-frontier TCEI growth rate decreased significantly as the deterioration in technological efficiency and the expansion of the technology gap have jointly inhibited the growth of carbon emissions efficiency. Carbon emission performance in various regions over different periods exhibit differing characteristics, that is, the carbon emission performance has significant spatial heterogeneity and period heterogeneity.

Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate

Abstract: Lake evaporation is a sensitive indicator of the hydrological response to climate change. Variability in annual lake evaporation has been assumed to be controlled primarily by the incoming surface solar radiation. Here we report simulations with a numerical model of lake surface fluxes, with input data based on a high-emissions climate change scenario (Representative Concentration Pathway 8.5). In our simulations, the global annual lake evaporation increases by 16% by the end of the century, despite little change in incoming solar radiation at the surface. We attribute about half of this projected increase to two effects: periods of ice cover are shorter in a warmer climate and the ratio of sensible to latent heat flux decreases, thus channelling more energy into evaporation. At low latitudes, annual lake evaporation is further enhanced because the lake surface warms more slowly than the air, leading to more long-wave radiation energy available for evaporation. We suggest that an analogous change in the ratio of sensible to latent heat fluxes in the open ocean can help to explain some of the spread among climate models in terms of their sensitivity of precipitation to warming. We conclude that an accurate prediction of the energy balance at the Earth’s surface is crucial for evaluating the hydrological response to climate change.

Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification

Abstract: In this paper, we propose a multiscale deep feature learning method for high-resolution satellite image scene classification. Specifically, we first warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multiscale satellite images are fed into their corresponding SPP-nets, respectively, to extract multiscale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult data sets show that the proposed method achieves favorable performance compared with other state-of-the-art methods.

Dynamic Resource Allocation for Load Balancing in Fog Environment

Abstract: Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

Towards Privacy-Preserving Content-Based Image Retrieval in Cloud Computing

Abstract: Content-based image retrieval (CBIR) applications have been rapidly developed along with the increase in the quantity, availability and importance of images in our daily life. However, the wide deployment of CBIR scheme has been limited by its the severe computation and storage requirement. In this paper, we propose a privacy-preserving content-based image retrieval scheme, which allows the data owner to outsource the image database and CBIR service to the cloud, without revealing the actual content of the database to the cloud server. Local features are utilized to represent the images, and earth mover's distance (EMD) is employed to evaluate the similarity of images. The EMD computation is essentially a linear programming (LP) problem. The proposed scheme transforms the EMD problem in such a way that the cloud server can solve it without learning the sensitive information. In addition, local sensitive hash (LSH) is utilized to improve the search efficiency. The security analysis and experiments show the security and efficiency of the proposed scheme.

Urban soil and human health: a review

Abstract: Rapid industrialization and urbanization during recent decades are having dramatic effects on urban soil properties and lead to large discharges of pollutants, which inevitably affect the health of the soil, ecosystems and human populations. This paper provides a systematic review of the relations between urban soil and human health. First, it summarizes the organic and inorganic pollutants in urban soil and their potential risks to human health. Second, the relations between urban greenbelt land, soil microbial diversity and human health are also explored. Third, we propose that future research should focus on the integration of assessments of health risks with exposure pathways and site characteristics. Bioavailability-based risk assessment frameworks for pollutants in urban soil can elucidate the complicated relations between urban soil, pollutant exposure and human health in cities. Finally, management of urban soil and policy should be strengthened in the future to maintain its sustainable development and utilization. More effort should be directed to understanding the relations between soil microbial diversity, green space and human health in cities.

CO2 reforming with methane over small-sized Ni@SiO2 catalysts with unique features of sintering-free and low carbon

Abstract: Sintering-free and carbon-free Ni catalysts developments are hot topics for high temperature hydrocarbons catalytic reactions. Core-shell is a promising structure to limit sintering, but ineffective towards carbon deposition if big sized Ni nanoparticles are present. In this work, core-shell structured Ni@SiO2 catalysts with small-sized Ni nanoparticles (about 5 nm) were synthesized by microemulsion method, which are featured by both sintering-free and low carbon deposits for high temperature CO2 reforming with methane reaction. The advantages were originated from the silica shell overlay confined moving space of Ni nanoparticles and the small size of Ni nanoparticles guaranteed low carbon diffusion in Ni crystals. The work provides a simple approach to synthesize small-sized Ni nanoparticles in core-shell catalysts for stable performance of CO2 reforming with methane reaction. It is supposed that this type of catalyst could also be applied in many other hydrocarbon catalytic reactions involving sintering and carbon problems.