Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

Optimal relaying in heterogeneous delay tolerant networks

In Delay Tolerant Networks (DTNs), there exists only intermittent connectivity between communication sources and destinations. In order to provide successful communication services for these challenged networks, a variety of relaying and routing algorithms have been proposed with the assumption that nodes are homogeneous in terms of contact rates and delivery costs. However, various applications of DTN have shown that mobile nodes should be divided into different classes in terms of their energy requirements and communication ability, and real application data have revealed the heterogeneous contact rates between node pairs. In this paper, we design an optimal relaying scheme for DTNs, which takes into account nodes’ heterogeneous contact rates and delivery costs when selecting relays to minimise the delivery cost while satisfying the required message delivery probability. Extensive results based on real traces demonstrate that our relaying scheme requires the least delivery cost and achieves the largest maximum delivery probability, compared with the schemes that neglect nodes’ heterogeneity

Vertical vs. Adiabatic Ionization Energies in Solution and Gas-Phase: Probing Ionization-Induced Reorganization in Conformationally-Mobile Bichromophoric Actuators Using Photoelectron Spectroscopy, Electrochemistry and Theory

Ionization-induced structural and conformational reorganization in various π-stacked dimers and covalently linked bichromophores is relevant to many processes in biological systems and functional materials. In this work, we examine the role of structural, conformational, and solvent reorganization in a set of conformationally mobile bichromophoric donors, using a combination of gas-phase photoelectron spectroscopy, solution-phase electrochemistry, and density functional theory (DFT) calculations. Photoelectron spectral analysis yields both adiabatic and vertical ionization energies (AIE/VIE), which are compared with measured (adiabatic) solution-phase oxidation potentials (Eox). Importantly, we find a strong correlation of Eox with AIE, but not VIE, reflecting variations in the attendant structural/conformational reorganization upon ionization. A careful comparison of the experimental data with the DFT calculations allowed us to probe the extent of charge stabilization in the gas phase and solution and to parse the reorganizational energy into its various components. This study highlights the importance of a synergistic approach of experiment and theory to study ionization-induced structural and conformational reorganization

Fabrication of a NiFe Alloy Oxide Catalyst via Surface Reconstruction for Selective Hydrodeoxygenation of Fatty Acid to Fatty Alcohol

Traditional NiFe alloy catalyst (NiFe AC) possesses low alcohol selectivity for the hydrodeoxygenation (HDO) of fatty acid due to its excessive deoxygenation into alkane. Herein, we innovatively provide the NiFe alloy oxide catalyst (NiFe AOC) to suppress the adsorption of aldehyde, which is the crucial intermediate of objective product alcohol converting into a side product, via the steric hindrance of lattice oxygen to inhibit the further conversion of alcohol. NiFe AOC reaches 100% conversion of lauric acid with 90% selectivity to lauryl alcohol. Kinetic analysis indicated that the apparent activation energy of side reaction increases by 71.1 kJ/mol for NiFe AOC relative to NiFe AC, evidencing the inhibition for the conversion of objective product alcohol into alkane for NiFe AOC. Furthermore, DFT calculation also suggests that the activation energy of the side reaction increases by 0.33 eV on NiFe AOC compared to NiFe AC. In addition, used NiFe AOC can be totally regenerated via surface reconstruction during the reduction-reoxidation treatment. However, overoxidation inducing NiFe surface phase separation weakened the synergistic interaction of Ni-Fe bimetallic sites and further decreased the catalytic activity

Stabilization of Fast Pyrolysis Liquids from Biomass by Mild Catalytic Hydrotreatment:Model Compound Study

Repolymerization is a huge problem in the storage and processing of biomass pyrolysis liquid (PL). Herein, to solve the problem of repolymerization, mild catalytic hydrotreatment of PL was conducted to convert unstable PL model compounds (hydroxyacetone, furfural, and phenol) into stable alcohols. An Ni/SiO2 catalyst was synthesized by the deposition-precipitation method and used in a mild hydrotreatment process. The mild hydrotreatment of the single model compound was studied to determine the reaction pathways, which provided guidance for improving the selectivity of stable intermediate alcohols through the control of reaction conditions. More importantly, the mild hydrotreatment of mixed model compounds was evaluated to simulate the PL more factually. In addition, the effect of the interaction between hydroxyacetone, furfural, and phenol during the catalytic hydrotreatment was also explored. There was a strange phenomenon observed in that phenol was not converted in the initial stage of the hydrotreatment of mixed model compounds. Thermogravimetric analysis (TGA), Ultraviolet-Raman (UV-Raman), and Brunauer−Emmett−Teller (BET) characterization of catalysts used in the hydrotreatment of single and mixed model compounds demonstrated that this phenomenon did not mainly arise from the irreversible deactivation of catalysts caused by carbon deposition, but the competitive adsorption among hydroxyacetone, furfural, and phenol during the mild hydrotreatment of mixed model compounds

Collaborative data dissemination in opportunistic vehicular networks

Future opportunistic vehicular networks offers viable means for collaborative data dissemination by high-capacity device-to-device communication. This is a highly challenging problem because a) mobile data items are heterogeneous in size and lifetime; b) mobile users have different interests to different data; and c) dissemination participants have limited storages. We study collaborative data dissemination under these realistic opportunistic vehicular network conditions and formulate the optimal data dissemination as a submodular function maximisation problem with multiple linear storage constraints. We then propose a heuristic algorithm to solve this challenging problem, and provide its theoretical performance bound. The effectiveness of our approach is demonstrated through simulation using real vehicular traces

Prompt-based All-in-One Image Restoration using CNNs and Transformer

Image restoration aims to recover the high-quality images from their degraded observations. Since most existing methods have been dedicated into single degradation removal, they may not yield optimal results on other types of degradations, which do not satisfy the applications in real world scenarios. In this paper, we propose a novel data ingredient-oriented approach that leverages prompt-based learning to enable a single model to efficiently tackle multiple image degradation tasks. Specifically, we utilize a encoder to capture features and introduce prompts with degradation-specific information to guide the decoder in adaptively recovering images affected by various degradations. In order to model the local invariant properties and non-local information for high-quality image restoration, we combined CNNs operations and Transformers. Simultaneously, we made several key designs in the Transformer blocks (multi-head rearranged attention with prompts and simple-gate feed-forward network) to reduce computational requirements and selectively determines what information should be persevered to facilitate efficient recovery of potentially sharp images. Furthermore, we incorporate a feature fusion mechanism further explores the multi-scale information to improve the aggregated features. The resulting tightly interlinked hierarchy architecture, named as CAPTNet, despite being designed to handle different types of degradations, extensive experiments demonstrate that our method performs competitively to the task-specific algorithms