A dataset of low-carbon energy transition index for Chinese cities 2003–2019

Cities are at the heart of climate change mitigation as they account for over 70% of global carbon emissions. However, cities vary in their energy systems and socioeconomic capacities to transition to renewable energy. To address this heterogeneity, this study proposes an Energy Transition Index (ETI) specifically designed for cities, and applies it to track the progress of energy transition in Chinese cities. The city-level ETI framework is based on the national ETI developed by the World Economic Forum (WEF) and comprises two sub-indexes: the Energy System Performance sub-index, which evaluates the current status of cities’ energy systems in terms of energy transition, and the Transition Readiness sub-index, which assesses their socioeconomic capacity for future energy transition. The initial version of the dataset includes ETI and its sub-indexes for 282 Chinese cities from 2003 to 2019, with annual updates planned. The spatiotemporal data provided by the dataset facilitates research into the energy transition roadmap for different cities, which can help China achieve its energy transition goals

PACE Solver Description: Hust-Solver - A Heuristic Algorithm of Directed Feedback Vertex Set Problem

A directed graph is formed by vertices and arcs from one vertex to another. The feedback vertex set problem (FVSP) consists in making a given directed graph acyclic by removing as few vertices as possible. In this write-up, we outline the core techniques used in the heuristic feedback vertex set algorithm, submitted to the heuristic track of the 2022 PACE challenge

A packet-layer video quality assessment model with spatiotemporal complexity estimation

Abstract A packet-layer video quality assessment (VQA) model is a lightweight model that predicts the video quality impacted by network conditions and coding configuration for application scenarios such as video system planning and in-service video quality monitoring. It is under standardization in ITU-T Study Group (SG) 12. In this article, we first differentiate the requirements for VQA model from the two application scenarios, and state the argument that the dataset for evaluating the quality monitoring model should be more challenging than that for system planning model. Correspondingly, different criteria and approaches are used for constructing the test datasets, for system planning (dataset-1) and for video quality monitoring (dataset-2), respectively. Further, we propose a novel video quality monitoring model by estimating the spatiotemporal complexity of video content. The model takes into account the interactions among content features, the error concealment effectiveness, and error propagation effects. Experiment results demonstrate that the proposed model achieves robust performance improvement compared with the existing peer VQA metrics on both dataset-1 and dataset-2. It is noted that on the more challenging dataset-2 for video quality monitoring, we obtain a large increase in Pearson correlation from 0.75 to 0.92 and a decrease in the modified RMSE from 0.41 to 0.19.</p

3D-HEVC visual quality assessment: database and bitstream model

Visual Quality Assessment of 3D/stereoscopic video (3D VQA) is significant for both quality monitoring and optimization of the existing 3D video services. In this paper, we build a 3D video database based on the latest 3D-HEVC video coding standard, to investigate the relationship among video quality, depth quality, and overall quality of experience (QoE) of 3D/stereoscopic video. We also analyze the pivotal factors to the video and depth qualities. Moreover, we develop a No-Reference 3D-HEVC bitstream-level objective video quality assessment model, which utilizes the key features extracted from the 3D video bitstreams to assess the perceived quality of the stereoscopic video. The model is verified to be effective on our database as compared with widely used 2D Full-Reference quality metrics as well as a state-of-the-art 3D FR pixel-level video quality metric

Decohering tensor network quantum machine learning models

Tensor network quantum machine learning (QML) models are promising applications on near-term quantum hardware. While decoherence of qubits is expected to decrease the performance of QML models, it is unclear to what extent the diminished performance can be compensated for by adding ancillas to the models and accordingly increasing the virtual bond dimension of the models. We investigate here the competition between decoherence and adding ancillas on the classification performance of two models, with an analysis of the decoherence effect from the perspective of regression. We present numerical evidence that the fully decohered unitary tree tensor network (TTN) with two ancillas performs at least as well as the non-decohered unitary TTN, suggesting that it is beneficial to add at least two ancillas to the unitary TTN regardless of the amount of decoherence may be consequently introduced

L.: Packet-layer model for quality assessment of encrypted video

Abstract-In this paper, a packet-layer quality assessment model is proposed for predicting the subjective quality of encrypted video for IPTV services. The detected information from encrypted video-stream is parsed into frame layer, and a novel estimation of frame type and Group-Of-Picture (GOP) structures is proposed to assist the parameter extraction utilized in the model. An efficient loss-related parameter is developed to reveal the visible degradation by loss. The quality assessment model focuses on predicting the quality measurement caused by both coding and channel artifacts. The cross-validation results on numerous databases show that the proposed model is not only better than other compared ones in performance, but also more generalized and robust to various testing conditions

Experimental Study and Application of Controlled Low-Strength Materials in Trench Backfilling in Suqian City, China

When backfilling narrow spaces, controlled low-strength materials (CLSM) can be used to achieve an effective backfilling effect. The pipeline engineering in Yahnghe Avenue of Suqian, China, provides a favorable on-site condition for the use of CLSM. However, no guidance exists for the determination of the material mixture ratio of CLSM for this geological condition. Laboratory tests were performed to investigate the basic physical parameters of excavated soil and the optimal mixture ratio of CLSM. Results indicate that the sand and silt account for 29.76% and 57.23% of the weight of excavated soil, respectively. As the water content increases (from 40% to 50%), the flowability of the CLSM approximately shows a linear increase (slumps values from 154.3 mm to 269.75 mm for 9% cement content), while its compressive strength shows a linear decreasing trend (from 875.3 KPa to 468.3 KPa after curing for 28 days); as the cement content increases (from 6% to 12%), the flowability approximately shows a linear decreasing trend (from 238.8 mm to 178.5 mm for 45% water content), while the compressive strength shows a linear increasing trend (from 391.6 KPa to 987.6 KPa after curing for 28 days). By establishing the relationship between compressive strength/flowability and the water–cement ratio, the optimal material ratio is determined to be 9% cement content and 40–43% water content. The engineering application results indicate that the use of CLSM can achieve efficient and high-quality backfilling effects for pipeline trenches. The findings of this research may provide a reference for the application of CLSM in fields with similar geological conditions

Syntheses, Structures, and Gas Adsorption Properties of Two Novel Cadmium–Sodium Organic Frameworks with 1,3,5-Benzenetricarboxylate Ligands

Solvothermal reactions of Cd(CH3COO)(2)center dot 2H(2)O and NaOH with 1,3,5-benzenetricarboxylic acid (H3BTC) in a mixture of H2O/DMA (DMA = N,N&apos;-dimethylacetamide) gave rise to two novel 3D porous metal-organic frameworks (MOFs), [Cd3Na6(BTC)(4)(H2O)(12)]center dot H2O (I) and [Cd3Na2(BTC)(3)(H2O)(3)]center dot[H2N(CH3)(2)]center dot H2O (2), which were characterized by single crystal and variable temperature powder X-ray diffraction (VTPXRD), IR spectroscopy, elemental analyses, inductively coupled plasma measurements, and coupled thermogravimetric-mass spectrometric analyses. Compound 1 crystallizes in the tetragonal space group P4(2)/n and could be described as a 2-fold interpenetrated net having a boracite (bor) topology, bridged by {Na3O12} trirners. Compound 2 crystallizes in the monoclinic space group Cc with ID channels distributed in an unusual honeycomb fashion along the b-axis. {Na2O10} dimers, (CH3)(2)NH2+ ions, and guest water are located in these channels. TG-MS and VTPXRD studies revealed that 1 had a high thermal stability up to 340 degrees C under air, and 2 was stable up to 310 degrees C. The gas-adsorption investigation disclosed that compound 1 exhibited H-2 uptake of 1.11 wt % at 77 K and 1.0 bar, with a high adsorption heat (8.4 kJ mol(-1)).Chemistry, MultidisciplinaryCrystallographyMaterials Science, MultidisciplinarySCI(E)0ARTICLE83529-35351

Syntheses, Structures, and Structural Transformations of Mixed Na(I) and Zn(II) Metal–Organic Frameworks with 1,3,5-Benzenetricarboxylate Ligands

Three new compounds, [ZnNa(BTC)(H2O)(2)]center dot H2O (1), [Zn3Na2(BTC)(2)(HCOO)(2)(H2O)(8)] (2), and [Zn3Na(BTC)(2) (HCOO)(H2O)3]center dot H2O (3) (H3BTC = 1,3,5-benzenetricarboxylate acid), were synthesized under hydrothermal conditions and were characterized by the single crystal and powder X-ray diffraction, IR spectra, elemental analyses, ICP measurements, and coupled TG-MS analyses. 1, 2, and 3 crystallize in the space groups P (1) over bar, C2/c, and P1, respectively. They are three-dimensional (3D) frameworks, consisting of zigzag chains ((Na2Zn2O14)(n) for 1, (Na2Zn3O18)(n) for 2, or (NaZn3O14)(n) for 3) and bridging BTC ligands. Powder X-ray diffraction studies reveal that 1 and 2 exhibit reversible dehydration-rehydration behaviors with a crystal-to-crystal transformation observed for 2.Chemistry, MultidisciplinaryCrystallographyMaterials Science, MultidisciplinarySCI(E)9ARTICLE62243-22491