Showing papers by "Dalian University of Technology published in 2021"

Transformer Tracking

Abstract: Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT.

Recent progress in photosensitizers for overcoming the challenges of photodynamic therapy: from molecular design to application.

Abstract: Photodynamic therapy (PDT), a therapeutic mode involving light triggering, has been recognized as an attractive oncotherapy treatment. However, nonnegligible challenges remain for its further clinical use, including finite tumor suppression, poor tumor targeting, and limited therapeutic depth. The photosensitizer (PS), being the most important element of PDT, plays a decisive role in PDT treatment. This review summarizes recent progress made in the development of PSs for overcoming the above challenges. This progress has included PSs developed to display enhanced tolerance of the tumor microenvironment, improved tumor-specific selectivity, and feasibility of use in deep tissue. Based on their molecular photophysical properties and design directions, the PSs are classified by parent structures, which are discussed in detail from the molecular design to application. Finally, a brief summary of current strategies for designing PSs and future perspectives are also presented. We expect the information provided in this review to spur the further design of PSs and the clinical development of PDT-mediated cancer treatments.

Engineering single-atomic ruthenium catalytic sites on defective nickel-iron layered double hydroxide for overall water splitting.

Abstract: Rational design of single atom catalyst is critical for efficient sustainable energy conversion. However, the atomic-level control of active sites is essential for electrocatalytic materials in alkaline electrolyte. Moreover, well-defined surface structures lead to in-depth understanding of catalytic mechanisms. Herein, we report a single-atomic-site ruthenium stabilized on defective nickel-iron layered double hydroxide nanosheets (Ru1/D-NiFe LDH). Under precise regulation of local coordination environments of catalytically active sites and the existence of the defects, Ru1/D-NiFe LDH delivers an ultralow overpotential of 18 mV at 10 mA cm−2 for hydrogen evolution reaction, surpassing the commercial Pt/C catalyst. Density functional theory calculations reveal that Ru1/D-NiFe LDH optimizes the adsorption energies of intermediates for hydrogen evolution reaction and promotes the O–O coupling at a Ru–O active site for oxygen evolution reaction. The Ru1/D-NiFe LDH as an ideal model reveals superior water splitting performance with potential for the development of promising water-alkali electrocatalysts. Rational design of single atom catalyst is critical for efficient sustainable energy conversion. Single-atomic-site ruthenium stabilized on defective nickel-iron layered double hydroxide nanosheets achieve superior HER and OER performance in alkaline media.

Metal–organic frameworks and their derivatives as electrocatalysts for the oxygen evolution reaction

Abstract: Electrochemical water splitting is an appealing and promising approach for energy conversion and storage. As a key half-reaction of electricity-driven water splitting, the oxygen evolution reaction (OER) is a sluggish process due to the transfer of four protons and four electrons. Therefore, development of low-cost and robust OER electrocatalysts is of great importance for improving the efficiency of water splitting. Based on the merits of high surface area, rich pore structure, diverse composition and well-defined metal centers, metal-organic frameworks (MOFs) and their derivatives have been widely exploited as OER electrocatalysts. Herein, the current progress on MOFs and their derivatives for OER electrolysis is summarized, highlighting the design principle, synthetic methods and performance for MOF-based materials. In addition, the structure-performance relationships of MOFs and their derivatives toward the OER are discussed, providing valuable insights into rationally developing OER catalysts with high efficiency. The current scientific and technological challenges and future perspectives towards the purpose of sustainable industrial applications are addressed at the end.

Heavy-Atom-Free Photosensitizers: From Molecular Design to Applications in the Photodynamic Therapy of Cancer

Abstract: Photodynamic therapy (PDT) is a clinically approved therapeutic modality that has shown great potential for the treatment of cancers owing to its excellent spatiotemporal selectivity and inherently noninvasive nature. However, PDT has not reached its full potential, partly due to the lack of ideal photosensitizers. A common molecular design strategy for effective photosensitizers is to incorporate heavy atoms into photosensitizer structures, causing concerns about elevated dark toxicity, short triplet-state lifetimes, poor photostability, and the potentially high cost of heavy metals. To address these drawbacks, a significant advance has been devoted to developing advanced smart photosensitizers without the use of heavy atoms to better fit the clinical requirements of PDT. Over the past few years, heavy-atom-free nonporphyrinoid photosensitizers have emerged as an innovative alternative class of PSs due to their superior photophysical and photochemical properties and lower expense. Heavy-atom-free nonporphyrinoid photosensitizers have been widely explored for PDT purposes and have shown great potential for clinical oncologic applications. Although many review articles about heavy-atom-free photosensitizers based on porphyrinoid structure have been published, no specific review articles have yet focused on the heavy-atom-free nonporphyrinoid photosensitizers.In this account, the specific concept related to heavy-atom-free photosensitizers and the advantageous properties of heavy-atom-free photosensitizers for cancer theranostics will be briefly introduced. In addition, recent progress in the development of heavy-atom-free photosensitizers, ranging from molecular design approaches to recent innovative types of heavy-atom-free nonporphyrinoid photosensitizers, emphasizing our own research, will be presented. The main molecular design approaches to efficient heavy-atom-free PSs can be divided into six groups: (1) the approach based on traditional tetrapyrrole structures, (2) spin-orbit charge-transfer intersystem crossing (SOCT-ISC), (3) reducing the singlet-triplet energy gap (ΔEST), (4) the thionation of carbonyl groups of conventional fluorophores, (5) twisted π-conjugation system-induced intersystem crossing, and (6) radical-enhanced intersystem crossing. The innovative types of heavy-atom-free nonporphyrinoid photosensitizers and their applications in cancer diagnostics and therapeutics will be discussed in detail in the third section. Finally, the challenges that need to be addressed to develop optimal heavy-atom-free photosensitizers for oncologic photodynamic therapy and a perspective in this research field will be provided. We believe that this review will provide general guidance for the future design of innovative photosensitizers and spur preclinical and clinical studies for PDT-mediated cancer treatments.

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Abstract: The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system

A stable low-temperature H2-production catalyst by crowding Pt on α-MoC.

Abstract: The water–gas shift (WGS) reaction is an industrially important source of pure hydrogen (H2) at the expense of carbon monoxide and water1,2. This reaction is of interest for fuel-cell applications, but requires WGS catalysts that are durable and highly active at low temperatures3. Here we demonstrate that the structure (Pt1–Ptn)/α-MoC, where isolated platinum atoms (Pt1) and subnanometre platinum clusters (Ptn) are stabilized on α-molybdenum carbide (α-MoC), catalyses the WGS reaction even at 313 kelvin, with a hydrogen-production pathway involving direct carbon monoxide dissociation identified. We find that it is critical to crowd the α-MoC surface with Pt1 and Ptn species, which prevents oxidation of the support that would cause catalyst deactivation, as seen with gold/α-MoC (ref. 4), and gives our system high stability and a high metal-normalized turnover number of 4,300,000 moles of hydrogen per mole of platinum. We anticipate that the strategy demonstrated here will be pivotal for the design of highly active and stable catalysts for effective activation of important molecules such as water and carbon monoxide for energy production. A stable, low-temperature water–gas shift catalyst is achieved by crowding platinum atoms and clusters on α-molybdenum carbide; the crowding protects the support from oxidation that would cause catalyst deactivation.

QoS-Guarantee Resource Allocation for Multibeam Satellite Industrial Internet of Things With NOMA

Abstract: The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution

Abstract: Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.

Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach

Abstract: The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Silver Single-Atom Catalyst for Efficient Electrochemical CO2 Reduction Synthesized from Thermal Transformation and Surface Reconstruction

Abstract: We report an Ag1 single-atom catalyst (Ag1 /MnO2 ), which was synthesized from thermal transformation of Ag nanoparticles (NPs) and surface reconstruction of MnO2 . The evolution process of Ag NPs to single atoms is firstly revealed by various techniques, including in situ ETEM, in situ XRD and DFT calculations. The temperature-induced surface reconstruction process from the MnO2 (211) to (310) lattice plane is critical to firmly confine the existing surface of Ag single atoms; that is, the thermal treatment and surface reconstruction of MnO2 is the driving force for the formation of single Ag atoms. The as-obtained Ag1 /MnO2 achieved 95.7 % Faradic efficiency at -0.85 V vs. RHE, and coupled with long-term stability for electrochemical CO2 reduction reaction (CO2 RR). DFT calculations indicated single Ag sites possessed high electronic density close to Fermi Level and could act exclusively as the active sites in the CO2 RR. As a result, the Ag1 /MnO2 catalyst demonstrated remarkable performance for the CO2 RR, far surpassing the conventional Ag nanosized catalyst (AgNP /MnO2 ) and other reported Ag-based catalysts.

Strategies to suppress hydrogen evolution for highly selective electrocatalytic nitrogen reduction: challenges and perspectives

Abstract: Ammonia, as a significant chemical for fertilizer production and also a promising energy carrier, is mainly produced through the traditional energy-intensive Haber–Bosch process. Recently, the electrocatalytic N2 reduction reaction (NRR) for ammonia synthesis has received tremendous attention with the merits of energy saving and environmental friendliness. To date, the development of the NRR process is primarily hindered by the competing hydrogen evolution reaction (HER), whereas the corresponding strategies for inhibiting this undesired side reaction to achieve high NRR selectivity are still quite limited. Furthermore, for such a complex reaction involving three gas–liquid–solid phases and proton/electron transfer, it is also rather meaningful to decouple and summarize the current strategies for suppressing H2 evolution in terms of NRR mechanisms, kinetics, thermodynamics, and electrocatalyst design in detail. Herein, on the basis of the NRR mechanisms, we systematically summarize the recent strategies to inhibit the HER for a highly selective electrocatalytic NRR, focusing on limiting the proton- and electron-transfer kinetics, shifting the chemical equilibrium, and designing the electrocatalysts. Additionally, insights into boosting the NRR selectivity and efficiency for practical applications are also presented in detail with regard to the determination of ammonia, the activation of the N2 molecule, the regulation of the gas–liquid–solid three-phase interface, the coupled NRR with value-added oxidation reactions, and the development of flow cell reactors.

Recent progress on 2D magnets: Fundamental mechanism, structural design and modification

Abstract: The two-dimensional (2D) magnet, a long-standing missing member in the family of 2D functional materials, is promising for next-generation information technology. The recent experimental discovery of 2D magnetic ordering in CrI3, Cr2Ge2Te6, VSe2, and Fe3GeTe2 has stimulated intense research activities to expand the scope of 2D magnets. This review covers the essential progress on 2D magnets, with an emphasis on the current understanding of the magnetic exchange interaction, the databases of 2D magnets, and the modification strategies for modulation of magnetism. We will address a large number of 2D intrinsic magnetic materials, including binary transition metal halogenides; chalogenides; carbides; nitrides; oxides; borides; silicides; MXene; ternary transition metal compounds CrXTe3, MPX3, Fe-Ge-Te, MBi2Te4, and MXY (M = transition metal; X = O, S, Se, Te, N; Y = Cl, Br, I); f-state magnets; p-state magnets; and organic magnets. Their electronic structure, magnetic moment, Curie temperature, and magnetic anisotropy energy will be presented. According to the specific 2D magnets, the underlying direct, superexchange, double exchange, super-superexchange, extended superexchange, and multi-intermediate double exchange interactions will be described. In addition, we will also highlight the effective strategies to manipulate the interatomic exchange mechanism to improve the Curie temperature of 2D magnets, such as chemical functionalization, isoelectronic substitution, alloying, strain engineering, defect engineering, applying electronic/magnetic field, interlayer coupling, carrier doping, optical controlling, and intercalation. We hope this review will contribute to understanding the magnetic exchange interaction of existing 2D magnets, developing unprecedented 2D magnets with desired properties, and offering new perspectives in this rapidly expanding field.

Activity‐Based NIR Enzyme Fluorescent Probes for the Diagnosis of Tumors and Image‐Guided Surgery

Abstract: Near-infrared (NIR) activatable fluorescent probes have been considered to be the effective edge tools for the investigation of cell biology and disease diagnosis because of their outstanding advantages. Related genes involved in tumor genesis and progression regulate the overexpression of certain enzymes. Owing to the distinctive characteristics of quick reaction time and favorable pharmacokinetics, enzyme-reactive NIR optical probes have shown great potential in the diagnosis of tumorigenesis and in image-guided intraoperative surgeries with high signal-to-noise ratios. In this review, we mainly summarize the latest advancements in enzyme-reactive NIR fluorescent probes from design strategy to biomedical application. Specifically, some challenges and prospects in this field are presented at the end of the review, which will be beneficial to innovatively construct new multifunctional fluorescent probes and actively promote their clinical transformation in the future.

Dynamic and causality interrelationships from municipal solid waste recycling to economic growth, carbon emissions and energy efficiency using a novel bootstrapping autoregressive distributed lag

Abstract: This study contributes to estimate the municipal solid waste (MSW) recycling effect on environmental quality and economic growth in the United States. Few studies have been given to macro-level aggregate analysis through national scale MSW recycling, environmental, and economic indicators. This study employs bootstrapping autoregressive distributed lag modeling for investigating the cointegration relationship among MSW recycling, economic growth, carbon emissions, and energy efficiency utilized quarterly data from 1990 to 2017. The result implies that a one percent increase in MSW recycling contributes to economic growth and reduce carbon emissions by 0.317% (0.157%) and 0.209% (0.087%) in the long-run (short-run). Similarly, a one percent improvement in energy efficiency stimulates economic growth by 0.489% (0.281%) and mitigates carbon emissions by 0.285% (0.197%) in the long-run (short-run). A higher per capita income and population growth caused higher emissions by 0.197% and 0.401% in the long-run. The overall results reveal stronger impacts in the long-run than the short-run with significant convergence towards long-run equilibrium, suggesting a prominent long-run transmission of economic and environmental fallouts. This study confirms a uni-directional causality from MSW recycling to economic growth, carbon emissions, and energy efficiency. These outcomes signify that any policy intervention related to MSW recycling produces significant changes in the level of economic growth and carbon emissions. The finding provides valuable insight for policymakers to counteract carbon emissions through recyclable waste management that simultaneously create significant economic value.

When UAV Meets IRS: Expanding Air-Ground Networks via Passive Reflection

Abstract: Thanks to their flexibility and mobility, unmanned aerial vehicles (UAVs) have been widely applied in wireless networks. However, UAV communications may suffer from blockage and eavesdropping in practical scenarios due to the complex environment. Taking the recent advances in intelligent reflecting surface (IRS) to reconfigure the propagation environments, in this article, we employ IRS to enhance the performance of UAV-aided air-ground networks. First, we overview the combination of UAV and IRS, by introducing the diverse applications of IRS and the appealing advantages of UAV, and highlighting the benefits of combining them. Then, we investigate two case studies where the UAV trajectory, the transmit beamforming and the IRS passive beamforming are jointly optimized. In the first case study, by equipping the IRS on a UAV, the average achievable rate of the relaying network is maximized. In the second one, the IRS is deployed to assist the UAV-ground communication while combating the adversarial eavesdropper. Simulation results are provided to demonstrate the performance enhancement resulting from combining UAV and IRS in air-ground networks. Finally, we shed light on some challenging issues to be resolved for practical implementations in this direction.

Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System

Abstract: Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO’s revenue and users’ quality of experience, we define a profit function to calculate the MNO’s profits. After that, we formulate a joint optimization problem to maximize MNO’s profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

Electron-ion collider in China

Abstract: Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of ∼80%) and protons (with a polarization of ∼70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2–3) × 10$^{33}$ cm$^{−2}$ · s$^{−1}$. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC.The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies.This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China.[graphic not available: see fulltext]

Camouflaged Object Segmentation with Distraction Mining

Abstract: Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.

Asymmetric role of tourism development and technology innovation on carbon dioxide emission reduction in the Chinese economy: Fresh insights from QARDL approach

Abstract: This study examines the association between tourism development, technology innovation, and carbon emissions by simultaneously testing Environment Kuznets Curve (EKC) hypothesis in China. The study develops and uses a novel composite index of tourism development and technology innovation. Utilizing quarterly data from 1995Q1 to 2017Q4, the study employs QARDL (Quantile Autoregressive Distributive Lag) approach and Granger causality‐in‐quantiles. The outcome of the study reveals that the observed relationship is quantile‐dependent, which may disclose misleading results in previous studies using traditional linear methodologies (such as OLS/ARDL) that address the averages. Primarily, the findings indicate that tourism development (TOR) and technology innovation (TII) significantly mitigate the level of carbon dioxide emissions (CO2) in the long run at lower‐higher (0.05–0.95) emissions quantiles and higher‐highest (0.7–0.95) emissions quantiles, respectively. Economic growth (GDP) and globalization (GLO) exert a positive asymmetric influence on CO2 only at lower‐medium (0.05–0.40) emissions quantiles and medium‐higher emissions quantiles (0.50–0.95), respectively. In the short run, TII, and GDP2 possess an insignificant impact across all emissions levels, while TOR shows a positive influence on CO2 only at lowest‐lower (0.05–0.20) emissions quantiles. The study confirms the presence of the EKC hypothesis at lower‐higher (0.05–0.70) emissions quantiles in the long run. Moreover, the outcomes of Granger causality in quantiles confirm asymmetric bidirectional quantile causality between TOR, TII, GLO, and CO2, while a unidirectional causality running from GDP to CO2. The results recommend that the Chinese government should implement integrated “tourism‐technology” policies based on the asymmetric emissions‐reduction effects of tourism and technology innovation in the long run.

Recent advances in innovative strategies for the CO2 electroreduction reaction

Abstract: The carbon dioxide electroreduction reaction (CO2RR), an emerging electrocatalysis reaction, is promising for converting CO2 into value-added fuels or chemicals (e.g., hydrocarbons and oxygenates). However, the CO2RR system for practical evaluations and applications is still limited by its low current density and poor CO2 utilization and conversion as well as dismal energy efficiency. To reach up to the practical application level, the components of the CO2RR device/system need to be systematically considered and optimized. This review specifically focuses on the latest and innovative design strategies toward each part of the system. In particular, the innovative and idiographic design strategies for tandem catalysts, promising electrolytes, upgraded electrodes, and advanced devices as well as anodic reactions are discussed at length. Moreover, some individual perspectives on opportunities and future challenges for each component of the CO2RR system are also provided. Perspectives and new trends presented in this review are not just labels and classifications. Instead, it is particularly expected that innovative ideas and visionary discussions in this review can systematically instruct and inspire researchers to contribute more efforts toward comprehensively optimizing the performance of the CO2RR system to a higher level.