Showing papers by "Dalian University of Technology published in 2020"
14 Jun 2020
TL;DR: The Efficient Channel Attention (ECA) module as discussed by the authors proposes a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution, which only involves a handful of parameters while bringing clear performance gain.
Abstract: Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity. To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local cross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore, we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction. The proposed ECA module is both efficient and effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFlops vs. 3.86 GFlops, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts.
1,378 citations
TL;DR: A comprehensive overview of the recent advances in energy-efficient CO2 conversion, especially focusing on structure-activity relationship, is provided as well as the importance of combining catalytic measurements, in situ characterization, and theoretical studies in understanding reaction mechanisms and identifying key descriptors for designing improved catalysts.
Abstract: The utilization of fossil fuels has enabled an unprecedented era of prosperity and advancement of well-being for human society. However, the associated increase in anthropogenic carbon dioxide (CO2) emissions can negatively affect global temperatures and ocean acidity. Moreover, fossil fuels are a limited resource and their depletion will ultimately force one to seek alternative carbon sources to maintain a sustainable economy. Converting CO2 into value-added chemicals and fuels, using renewable energy, is one of the promising approaches in this regard. Major advances in energy-efficient CO2 conversion can potentially alleviate CO2 emissions, reduce the dependence on nonrenewable resources, and minimize the environmental impacts from the portions of fossil fuels displaced. Methanol (CH3OH) is an important chemical feedstock and can be used as a fuel for internal combustion engines and fuel cells, as well as a platform molecule for the production of chemicals and fuels. As one of the promising approaches, thermocatalytic CO2 hydrogenation to CH3OH via heterogeneous catalysis has attracted great attention in the past decades. Major progress has been made in the development of various catalysts including metals, metal oxides, and intermetallic compounds. In addition, efforts are also put forth to define catalyst structures in nanoscale by taking advantage of nanostructured materials, which enables the tuning of the catalyst composition and modulation of surface structures and potentially endows more promising catalytic performance in comparison to the bulk materials prepared by traditional methods. Despite these achievements, significant challenges still exist in developing robust catalysts with good catalytic performance and long-term stability. In this review, we will provide a comprehensive overview of the recent advances in this area, especially focusing on structure-activity relationship, as well as the importance of combining catalytic measurements, in situ characterization, and theoretical studies in understanding reaction mechanisms and identifying key descriptors for designing improved catalysts.
639 citations
14 Jun 2020
TL;DR: The consistency-enhanced loss is exploited to highlight the fore-/back-ground difference and preserve the intra-class consistency in the aggregate interaction modules to integrate the features from adjacent levels, in which less noise is introduced because of only using small up-/down-sampling rates.
Abstract: Deep-learning based salient object detection methods achieve great progress. However, the variable scale and unknown category of salient objects are great challenges all the time. These are closely related to the utilization of multi-level and multi-scale features. In this paper, we propose the aggregate interaction modules to integrate the features from adjacent levels, in which less noise is introduced because of only using small up-/down-sampling rates. To obtain more efficient multi-scale features from the integrated features, the self-interaction modules are embedded in each decoder unit. Besides, the class imbalance issue caused by the scale variation weakens the effect of the binary cross entropy loss and results in the spatial inconsistency of the predictions. Therefore, we exploit the consistency-enhanced loss to highlight the fore-/back-ground difference and preserve the intra-class consistency. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches. The source code will be publicly available at https://github.com/lartpang/MINet.
487 citations
TL;DR: In this article, the development and recent advancements of Pt and Pt-based electrocatalysts are discussed in a review, mainly focused on the structure and composition of Pt, which significantly affect the catalytic activities and durability of fuel cell catalysts.
Abstract: Due to the growing demand for energy and impending environmental issues, fuel cells have attracted significant attention as an alternative to conventional energy technologies. As cost is the main inhibitor of this technology, low cost catalysts with high activity and stable catalytic performance are the key to large scale application of fuel cells. The development and recent advancements of Pt and Pt-based electrocatalysts are discussed in this review. Discussion is mainly focused on the structure and composition of Pt and Pt-based electrocatalysts, which significantly affect the catalytic activities and durability of fuel cell catalysts. The electrocatalysts including Pt single metal, Pt-based alloys (including noble alloys, non-noble alloys, metal oxide alloys, and non-metal alloys), and structure-controlled alloys (nanopolyhedra, nanodendrites, and hollow and core–shell structures) were discussed. The activity, stability, and efficiency of Pt and Pt-based catalysts for both the oxygen reduction reaction (ORR) and methanol oxidation reaction (MOR) as well as the correlation between the catalytic performance, structure optimization, and composition modulation of catalysts are also discussed.
324 citations
TL;DR: The probability of transportation of COVID-19 from Wuhan to 369 other cities in China before the quarantine is estimated to be >50% in 130 (95% CI 89–190) cities and >99% in the 4 largest metropolitan areas.
Abstract: On January 23, 2020, China quarantined Wuhan to contain coronavirus disease (COVID-19). We estimated the probability of transportation of COVID-19 from Wuhan to 369 other cities in China before the quarantine. Expected COVID-19 risk is >50% in 130 (95% CI 89-190) cities and >99% in the 4 largest metropolitan areas.
317 citations
TL;DR: The strategy of "barrier-free rotation" provides a new platform for future design of PTT agents for clinical cancer treatment and can lead to complete tumor ablation in tumor-bearing mice after intravenous injection of tfm-BDP NPs.
Abstract: Traditional photothermal therapy requires high-intensity laser excitation for cancer treatments due to the low photothermal conversion efficiency (PCE) of photothermal agents (PTAs). PTAs with ultra-high PCEs can decrease the required excited light intensity, which allows safe and efficient therapy in deep tissues. In this work, a PTA is synthesized with high PCE of 88.3% based on a BODIPY scaffold, by introducing a CF3 "barrier-free" rotor on the meso-position (tfm-BDP). In both the ground and excited state, the CF3 moiety in tfm-BDP has no energy barrier to rotation, allowing it to efficiently dissipate absorbed (NIR) photons as heat. Importantly, the barrier-free rotation of CF3 can be maintained after encapsulating tfm-BDP into polymeric nanoparticles (NPs). Thus, laser irradiation with safe intensity (0.3 W cm-2 , 808 nm) can lead to complete tumor ablation in tumor-bearing mice after intravenous injection of tfm-BDP NPs. This strategy of "barrier-free rotation" provides a new platform for future design of PTT agents for clinical cancer treatment.
308 citations
TL;DR: Some typical application scenarios of edge computing in IIoT, such as prognostics and health management, smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics, are introduced.
Abstract: The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.
307 citations
TL;DR: This research provides a promising avenue to enhance the electrocatalytic performance of the catalysts by engineering interfacial active sites toward large-scale water splitting by fabricated by oxidation/hydrogenation-induced surface reconfiguration strategy.
Abstract: Rational design of the catalysts is impressive for sustainable energy conversion. However, there is a grand challenge to engineer active sites at the interface. Herein, hierarchical transition bimetal oxides/sulfides heterostructure arrays interacting two-dimensional MoOx/MoS2 nanosheets attached to one-dimensional NiOx/Ni3S2 nanorods were fabricated by oxidation/hydrogenation-induced surface reconfiguration strategy. The NiMoOx/NiMoS heterostructure array exhibits the overpotentials of 38 mV for hydrogen evolution and 186 mV for oxygen evolution at 10 mA cm−2, even surviving at a large current density of 500 mA cm−2 with long-term stability. Due to optimized adsorption energies and accelerated water splitting kinetics by theory calculations, the assembled two-electrode cell delivers the industrially relevant current densities of 500 and 1000 mA cm−2 at record low cell voltages of 1.60 and 1.66 V with excellent durability. This research provides a promising avenue to enhance the electrocatalytic performance of the catalysts by engineering interfacial active sites toward large-scale water splitting. While water splitting is an appealing carbon-neutral strategy for renewable energy generation, there is a need to develop new active, cost-effective catalysts. Here, authors prepare a nickel-molybdenum oxide/sulfide heterojunctions as bifunctional H2 and O2 evolution electrocatalysts.
294 citations
TL;DR: In this paper, the mediating effects of digital supply chain platforms and the moderating effect of environmental dynamism are evaluated using a survey of Chinese manufacturing firms, and the results indicate that digital supply chains platforms mediate the effects of Digital technologies on both economic and environmental performance.
291 citations
TL;DR: Eutectic high-entropy alloys (EHEAs) are becoming a new research hotspot in the metallic materials community because of their excellent castability, fine and uniform microstructures even in the as-cast state, high strength, and good ductility as discussed by the authors.
249 citations
TL;DR: A blockchain framework and its development processes are presented in detail, and algorithms for smart contracts are developed for the model implementation and the results suggest that the proposed framework facilitates the on-time delivery of precast components and tracks the reasons for disputes centered on PCs in the precast supply chain.
TL;DR: A cluster-based CIIoT is proposed, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA), and the simulations have indicated that the NOMA can better guarantee the transmission performance of each node than the traditional N OMA and orthogonalmultiple access.
Abstract: The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.
TL;DR: This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multi-modality deep learning fusion method and to motivate new multimodAL data fusion techniques of deep learning.
Abstract: With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to ...
TL;DR: A wireless, non-invasive technology is presented that not only offers measurement equivalency to existing clinical standards for heart rate, respiration rate, temperature and blood oxygenation, but also provides a range of important additional features, as supported by data from pilot clinical studies in both the NICU and PICU.
Abstract: Standard clinical care in neonatal and pediatric intensive-care units (NICUs and PICUs, respectively) involves continuous monitoring of vital signs with hard-wired devices that adhere to the skin and, in certain instances, can involve catheter-based pressure sensors inserted into the arteries. These systems entail risks of causing iatrogenic skin injuries, complicating clinical care and impeding skin-to-skin contact between parent and child. Here we present a wireless, non-invasive technology that not only offers measurement equivalency to existing clinical standards for heart rate, respiration rate, temperature and blood oxygenation, but also provides a range of important additional features, as supported by data from pilot clinical studies in both the NICU and PICU. These new modalities include tracking movements and body orientation, quantifying the physiological benefits of skin-to-skin care, capturing acoustic signatures of cardiac activity, recording vocal biomarkers associated with tonality and temporal characteristics of crying and monitoring a reliable surrogate for systolic blood pressure. These platforms have the potential to substantially enhance the quality of neonatal and pediatric critical care. Soft electronic patches worn on the skin of infants or children in intensive-care units have a wide range of capabilities in aiding critical care, including monitoring of hemodynamic parameters, cardiac activity, movement and crying.
TL;DR: In this article, the fabrication, characterization, mechanisms and performances of autogenous and autonomous healing concretes are reviewed, including self-healing concrete with biomimetic features such as shape memory alloys, capsules, vascular networks or bacteria.
Abstract: Cracks in cement concrete composites, whether autogenous or loading-initiated, are almost inevitable and often difficult to detect and repair, posing a threat to safety and durability of concrete infrastructures, especially for those with strict sealing requirements. The sustainable development of infrastructures calls for the birth of self-healing concrete composites, which has the built-in ability to autonomously repair narrow cracks. This paper reviews the fabrication, characterization, mechanisms and performances of autogenous and autonomous healing concretes. Autogenous healing materials such as mineral admixtures, fibers, nanofillers and curing agents, as well as autonomous healing methods such as electrodeposition, shape memory alloys, capsules, vascular and microbial technologies, have been proven to be effective to partially or even fully repair small cracks. As a result, the mechanical properties and durability of concrete infrastructure can be restored to some extent. However, autonomous healing techniques have shown a better performance in healing cracks than most of autogenous healing methods that are limited to healing of cracks having a width narrower than 150 μm. Self-healing concrete with biomimetic features, such as self-healing concrete based on shape memory alloys, capsules, vascular networks or bacteria, is a frontier subject in the field of material science. Self-healing technology provides concrete infrastructures with the ability to adapt and respond to the environment, exhibiting a great potential to facilitate the creation of a wide variety of resilient materials and infrastructures.
TL;DR: The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness.
Abstract: This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.
TL;DR: In this paper, the authors considered the photocatalytic production of H2O2 from the reduction of O2 by semiconductor photocatalysts (e.g., graphitic carbon nitride, C3N4).
Abstract: Photocatalytic production of H2O2 from the reduction of O2 by semiconductor photocatalysts (e.g., graphitic carbon nitride, C3N4) has been regarded as an alternative for small-scale decentralized H...
14 Jun 2020
TL;DR: This work presents a novel Adaptive Pyramid Loss (APLoss) to hierarchically calculate the estimation losses of sub-regions, which alleviates the training bias and demonstrates the superiority of the proposed approach to alleviate the counting performance differences in different regions.
Abstract: Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to overestimate or underestimate people counts of regions with different density patterns, which degrades the whole count accuracy. To overcome this problem, we propose an approach to alleviate the counting performance differences in different regions. Specifically, our approach consists of two networks named Density Attention Network (DANet) and Attention Scaling Network (ASNet). DANet provides ASNet with attention masks related to regions of different density levels. ASNet first generates density maps and scaling factors and then multiplies them by attention masks to output separate attention-based density maps. These density maps are summed to give the final density map. The attention scaling factors help attenuate the estimation errors in different regions. Furthermore, we present a novel Adaptive Pyramid Loss (APLoss) to hierarchically calculate the estimation losses of sub-regions, which alleviates the training bias. Extensive experiments on four challenging datasets (ShanghaiTech Part A, UCF_CC_50, UCF-QNRF, and WorldExpo'10) demonstrate the superiority of the proposed approach.
TL;DR: The single atom Cu encapsulated on N-doped porous carbon catalysts are designed for reducing CO2 to acetone at low overpotentials and the active sites are identified as Cu coordination with four pyrrole-N atoms.
Abstract: Efficient electroreduction of CO2 to multi-carbon products is a challenging reaction because of the high energy barriers for CO2 activation and C–C coupling, which can be tuned by designing the metal centers and coordination environments of catalysts. Here, we design single atom copper encapsulated on N-doped porous carbon (Cu-SA/NPC) catalysts for reducing CO2 to multi-carbon products. Acetone is identified as the major product with a Faradaic efficiency of 36.7% and a production rate of 336.1 μg h−1. Density functional theory (DFT) calculations reveal that the coordination of Cu with four pyrrole-N atoms is the main active site and reduces the reaction free energies required for CO2 activation and C–C coupling. The energetically favorable pathways for CH3COCH3 production from CO2 reduction are proposed and the origin of selective acetone formation on Cu-SA/NPC is clarified. This work provides insight into the rational design of efficient electrocatalysts for reducing CO2 to multi-carbon products. Efficient electroreduction of CO2 to multi-carbon products is challenging. Here, the single atom Cu encapsulated on N-doped porous carbon catalysts are designed for reducing CO2 to acetone at low overpotentials and the active sites are identified as Cu coordination with four pyrrole-N atoms.
TL;DR: High electrical conductivity and localized surface plasmon resonance (LSPR) effect of hybrid conductive network endow the TCF with low voltage driven Joule heating performance and excellent photo-thermal effect, respectively, which can ensure the normal functioning under extreme cold condition.
Abstract: Transparent conductive film (TCF) is promising for optoelectronic instrument applications. However, designing a robust, stable, and flexible TCF that can shield electromagnetic waves and work in harsh conditions remains a challenge. Herein, a multifunctional and flexible TCF with effective electromagnetic interference shielding (EMI) performance and outstanding electro-photo-thermal effect is proposed by orderly coating Ti3C2Tx MXene and a silver nanowire (AgNW) hybrid conductive network using a simple and scalable solution-processed method. Typically, the air-plasma-treated polycarbonate (PC) film was sequentially spray-coated with MXene and AgNW to construct a highly conductive network, which was transferred and partly embedded into an ultrathin poly(vinyl alcohol) (PVA) film using spin coating coupled with hot pressing to enhance the interfacial adhesion. The peeled MXene/AgNW-PVA TCF exhibits an optimal optical and electrical performance of sheet resistance 18.3 Ω/sq and transmittance 52.3%. As a consequence, the TCF reveals an effective EMI shielding efficiency of 32 dB in X-band with strong interfacial adhesion and satisfactory flexibility. Moreover, the high electrical conductivity and localized surface plasmon resonance (LSPR) effect of hybrid conductive network endow the TCF with low-voltage-driven Joule heating performance and excellent photothermal effect, respectively, which can ensure the normal functioning under extreme cold condition. In view of the comprehensive performance, this work offers new solutions for next-generation transparent EMI shielding challenges.
TL;DR: The proposed output feedback control method can be achieved in the absence of velocity measurements and the complexity of the cooperative time-varying formation maneuvering control laws is reduced without resorting to dynamic surface control.
Abstract: In this paper, a cooperative time-varying formation maneuvering problem with connectivity preservation and collision avoidance is investigated for a fleet of autonomous surface vehicles (ASVs) with position–heading measurements. Each vehicle is subject to unknown kinetics induced by internal model uncertainty and external disturbances. At first, a nonlinear state observer is used to recover the unmeasured linear velocity and yaw rate as well as unknown uncertainty and disturbances. Then, observer-based cooperative time-varying formation maneuvering control laws are designed based on artificial potential functions, nonlinear tracking differentiators, and a backstepping technique. The stability of closed-loop distributed formation control system is analyzed based on input-to-state stability and cascade stability. The salient features of the proposed method are as follows. First, cooperative time-varying formation maneuvering with the capability of connectivity preservation and collision avoidance can be achieved in the absence of velocity measurements. Second, the complexity of the cooperative time-varying formation maneuvering control laws is reduced without resorting to dynamic surface control. Third, the uncertainty and disturbance are actively rejected in the presence of position–heading measurements. Simulation results are given to substantiate the proposed output feedback control method for cooperative time-varying formation maneuvering of ASVs with connectivity preservation and collision avoidance.
TL;DR: This tutorial review will focus on the recent advancements of chemiluminescent platforms based on luminophore substrates including luminol and its derivatives, cypridina luciferin analogs, peroxyoxalates, and dioxetanes, and systematically summarize the design principles, sensing mechanisms, and bioimaging and therapeutic applications of representative chemilUMinescent probes as well as theranostic agents.
Abstract: Chemiluminescence, the generation of light through chemiexcitation as a result of chemical reactions, has emerged as a novel tool for bioimaging and therapy in vivo. Due to the elimination of external optical excitation, it can effectively avoid background autofluorescence existing in fluorescence techniques, providing extremely high signal-to-noise ratios and sensitivity in bioimaging. Furthermore, in situ emitted photons can replace traditional excitation light to construct chemiexcited photodynamic therapy or drug release systems for the monitoring and treatment of deeply seated diseases or tumors. In this tutorial review, we will focus on the recent advancements of chemiluminescent platforms based on luminophore substrates including luminol and its derivatives, cypridina luciferin analogs, peroxyoxalates, and dioxetanes, and systematically summarize the design principles, sensing mechanisms, and bioimaging and therapeutic applications of representative chemiluminescent probes as well as theranostic agents. Finally, the potential challenges and perspectives of chemiluminescent platforms for bioimaging and therapeutics are also discussed.
14 Jun 2020
TL;DR: A depth distiller (A2dele) is proposed to explore the way of using network prediction and attention as two bridges to transfer the depth knowledge from the depth stream to the RGB stream and achieves state-of-the-art performance.
Abstract: Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data. This inevitably incurs extra computational costs and memory consumption, and using depth data during testing may hinder the practical applications of RGB-D saliency detection. To tackle these two dilemmas, we propose a depth distiller (A2dele) to explore the way of using network prediction and attention as two bridges to transfer the depth knowledge from the depth stream to the RGB stream. First, by adaptively minimizing the differences between predictions generated from the depth stream and RGB stream, we realize the desired control of pixel-wise depth knowledge transferred to the RGB stream. Second, to transfer the localization knowledge to RGB features, we encourage consistencies between the dilated prediction of the depth stream and the attention map from the RGB stream. As a result, we achieve a lightweight architecture without use of depth data at test time by embedding our A2dele. Our extensive experimental evaluation on five benchmarks demonstrate that our RGB stream achieves state-of-the-art performance, which tremendously minimizes the model size by 76% and runs 12 times faster, compared with the best performing method. Furthermore, our A2dele can be applied to existing RGB-D networks to significantly improve their efficiency while maintaining performance (boosts FPS by nearly twice for DMRA and 3 times for CPFP).
TL;DR: In this article, the impact of corporate social responsibility (CSR) activities on environmental sustainability and green innovation is investigated. But, as a determinant of environmental strategies, green innovation haven't received much attention.
TL;DR: In this article, the effect of the incorporation of coarse recycled concrete aggregates (RCAs) on the durability of cement-based cementitious materials was experimentally investigated, and the results indicate that the incorporation generally decreases the compressive strength, and inversely increases water and chloride transport coefficient compared to those of the control concrete.
TL;DR: This research focuses on the cross-border e-commerce context, to propose a blockchain-based framework, and develop a set of corresponding techniques and methods for achieving traceable products and transactions in supply chain management.
TL;DR: A wireless device designed to be conformally placed on the suprasternal notch can continuously provide real-time information of essential vital signs as well as talking time, swallow counts and sleep patterns.
Abstract: Skin-mounted soft electronics that incorporate high-bandwidth triaxial accelerometers can capture broad classes of physiologically relevant information, including mechano-acoustic signatures of underlying body processes (such as those measured by a stethoscope) and precision kinematics of core-body motions. Here, we describe a wireless device designed to be conformally placed on the suprasternal notch for the continuous measurement of mechano-acoustic signals, from subtle vibrations of the skin at accelerations of around 10−3 m s−2 to large motions of the entire body at about 10 m s−2, and at frequencies up to around 800 Hz. Because the measurements are a complex superposition of signals that arise from locomotion, body orientation, swallowing, respiration, cardiac activity, vocal-fold vibrations and other sources, we exploited frequency-domain analysis and machine learning to obtain—from human subjects during natural daily activities and exercise—real-time recordings of heart rate, respiration rate, energy intensity and other essential vital signs, as well as talking time and cadence, swallow counts and patterns, and other unconventional biomarkers. We also used the device in sleep laboratories and validated the measurements using polysomnography. A wireless device designed to be conformally placed on the suprasternal notch can continuously provide real-time information of essential vital signs as well as talking time, swallow counts and sleep patterns.
TL;DR: Using this two-step O2-economical strategy, under relatively low light dose irradiation, dramatically enhanced therapeutic responses toward hypoxic tumors are realized and a conceptual while practical paradigm for overcoming the pitfalls of phototherapeutic is offered.
Abstract: Tumor hypoxia has proven to be the major bottleneck of photodynamic therapy (PDT) to clinical transformation. Different from traditional O2 delivery approaches, here we describe an innovative binary photodynamic O2-economizer (PDOE) tactic to reverse hypoxia-driven resistance by designing a superoxide radical (O2•-) generator targeting mitochondria respiration, termed SORgenTAM. This PDOE system is able to block intracellular O2 consumption and down-regulate HIF-1α expression, which successfully rescues cancer cells from becoming hypoxic and relieves the intrinsic hypoxia burden of tumors in vivo, thereby sparing sufficient endogenous O2 for the PDT process. Photosensitization mechanism studies demonstrate that SORgenTAM has an ideal intersystem crossing rate and triplet excited state lifetime for generating O2•- through type-I photochemistry, and the generated O2•- can further trigger a biocascade to reduce the PDT's demand for O2 in an O2-recycble manner. Furthermore, SORgenTAM also serves to activate the AMPK metabolism signaling pathway to inhibit cell repair and promote cell death. Consequently, using this two-step O2-economical strategy, under relatively low light dose irradiation, excellent therapeutic responses toward hypoxic tumors are achieved. This study offers a conceptual while practical paradigm for overcoming the pitfalls of phototherapeutics.
TL;DR: Wang et al. as discussed by the authors used the difference-in-differences (DID) method to evaluate the impact of carbon emissions and economic growth following carbon emission trading implementation in China.
TL;DR: The objective of this study is to reveal and distinguish the individual roles of different activating agents during AC synthesis, highlighting the development of activating agent roles during the process of AC.