# Beijing University of Technology

• Beijing, Beijing, China
Recent publications
This study represents the first quantitative evaluation of pollution transport budget within the boundary layer of typical cities in the Beijing-Tianjin-Hebei (BTH) region from the perspective of horizontal and vertical exchanges and further discusses the impact of the atmospheric boundary layer (ABL)-free troposphere (FT) exchange on concentration of fine particulate matter (PM2.5) within the ABL during heavy pollution. From the perspective of the transport flux balance relationship, differences in pollution transport characteristics between the two cities is mainly reflected in the ABL-FT exchange effect. The FT mainly flowed into the ABL in BJ, while in SJZ, the outflow from the ABL to the FT was more intense. Combined with an analysis of vertical wind profile distribution, BJ was found to be more susceptible to the influence of northwest cold high prevailing in winter, while sinking of strong cold air allowed the FT flowing into the ABL influence the vertical exchange over BJ. In addition, we selected a typical pollution event for targeted analysis to understand mechanistic details of the influence of ABL-FT exchange on the pollution event. These results showed that ABL-FT interaction played an important role in PM2.5 concentration within the ABL during heavy pollution. Especially in the early stage of heavy pollution, FT transport contributed as much as 82.74% of PM2.5 within the ABL. These findings are significant for improving our understanding of pollution transport characteristics within the boundary layer and the effect of ABL-FT exchange on air quality.
Considering that the assumption of time consistency does not adequately reveal the mechanisms of exit decisions of venture capital (VC), this study proposes two kinds of time-inconsistent preferences (i.e., time-flow inconsistency and time-point inconsistency) to advance research in this field. Time-flow inconsistency is in line with the previous time inconsistency literature, while time-point inconsistency is rooted in the VC fund’s finite lifespan. Based on the assumption about the strategies guiding future behaviors, we consider four types of venture capitalists: time-consistent, time-point-inconsistent, naïve, and sophisticated venture capitalists, of which the latter three are time-inconsistent. We derive and compare the exit thresholds of these four types of venture capitalists. The main results include: (1) time-inconsistent preferences accelerate the exits of venture capitalists; (2) the closer the VC funds expiry dates are, the more likely time-inconsistent venture capitalists are to accelerate their exits; and (3) future selves caused by time-flow inconsistency weaken the effect of time-point inconsistency. Our study provides a behavioral explanation for the empirical fact of young VCs’ grandstanding.
Segmenting the tongue body is an essential step for automated tongue diagnosis, which is a challenge task due to the tongue body’s specificity and heterogeneity. The current deep-learning based tongue image segmentation networks are bloated with high computational complexity. In this study, a light-weight segmentation network for tongue images is proposed under the basic encoder-decoder framework, in which MobileNet v2 is adopted as the backbone network, due to its few parameters and low computational complexity. The high-level semantic information and low-level positional information are combined together to detect the tongue body’s boundary. And the dilated convolution operations are performed on the final feature maps of the network to enlarge the receptive field, so as to capture rich global semantic information. An attention mechanism is embedded to re-calibrate the feature maps spatially and channel-wise to enhance important features for the segmentation task, while suppressing the irrelevant ones. Moreover, a supervision output is added to each level of the decoder to guide the network to capture both the local and global image features for accurate tongue image segmentation. All supervision outputs are fused to produce good segmented results. The quantitative and qualitative results on two tongue datasets indicate that the proposed network can achieve a competitive performance with smaller model size and lower computational cost. The proposed method could accurately extract the tongue body, which can fully meet the requirements of practical applications.
The control of breast motions is a critical indicator to evaluate the comfort and function of sports bras. If the breast motions can be predicted based on the gait parameters detected by wearable sensors, it will more economical and convenient to evaluate the bras. Thirteen unmarried Chinese females with a breast cup of 75B were recruited in this study to investigate the regularity of breast motions and the relevance between breast motions and gaits during running exercises. The breast motion indicator is the distance alteration of breast regions. The gaits were described by the rotation angles of the hip, knee, ankle joints, and the foot height off the ground. Firstly, the Mann-Whitney U test and the Kruskal-Wallis H test were utilized to analyze the motion diversity among the eight breast regions. Then, the gray correlation analysis was applied to explore the relevance between breast motions and gaits. Finally, the back-propagation neural network, the genetic algorithm, and the particle swarm optimization algorithm were utilized to construct the prediction models for breast motions based on gait parameters. The results demonstrate that the same breast regions on the bilateral breasts and the different breast regions on the ipsilateral breasts present a significant motion diversity. There is a moderate correlation between breast motions and gait parameters, and the back-propagation neural network optimized by the particle swarm optimization algorithm performs better in breast motion prediction, which has a coefficient of determination of 84.58% and a mean absolute error of 0.2108.
Photocatalytic conversion of CO 2 to high-value products plays a crucial role in the global pursuit of carbon–neutral economy. Junction photocatalysts, such as the isotype heterojunctions, offer an ideal paradigm to navigate the photocatalytic CO 2 reduction reaction (CRR). Herein, we elucidate the behaviors of isotype heterojunctions toward photocatalytic CRR over a representative photocatalyst, g-C 3 N 4 . Impressively, the isotype heterojunctions possess a significantly higher efficiency for the spatial separation and transfer of photogenerated carriers than the single components. Along with the intrinsically outstanding stability, the isotype heterojunctions exhibit an exceptional and stable activity toward the CO 2 photoreduction to CO. More importantly, by combining quantitative in situ technique with the first-principles modeling, we elucidate that the enhanced photoinduced charge dynamics promotes the production of key intermediates and thus the whole reaction kinetics.
Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8–13 Hz) and β band [13–30 Hz, with $$\beta_{1}$$ β 1 (13–21 Hz) and $$\beta_{2}$$ β 2 (21–30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands ( α , β , $$\beta_{1} ,$$ β 1 , $${ }\beta_{2}$$ β 2 ) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t -test and Kappa values show its statistical significance and high consistency as well.
Structural color printings have broad applications due to their advantages of long-term sustainability, eco-friendly manufacturing, and ultra-high resolution. However, most of them require costly and time-consuming fabrication processes from nanolithography to vacuum deposition and etching. Here, we demonstrate a new color printing technology based on polymer-assisted photochemical metal deposition (PPD), a room temperature, ambient, and additive manufacturing process without requiring heating, vacuum deposition or etching. The PPD-printed silver films comprise densely aggregated silver nanoparticles filled with a small amount (estimated <20% volume) of polymers, producing a smooth surface (roughness 2.5 nm) even better than vacuum-deposited silver films (roughness 2.8 nm) at ~4 nm thickness. Further, the printed composite films have a much larger effective refractive index n (~1.90) and a smaller extinction coefficient k (~0.92) than PVD ones in the visible wavelength range (400 to 800 nm), therefore modulating the surface reflection and the phase accumulation. The capability of PPD in printing both ultra-thin (~5 nm) composite films and highly reflective thicker film greatly benefit the design and construction of multilayered Fabry–Perot (FP) cavity structures to exhibit vivid and saturated colors. We demonstrated programmed printing of complex pictures of different color schemes at a high spatial resolution of ~6.5 μm by three-dimensionally modulating the top composite film geometries and dielectric spacer thicknesses (75 to 200 nm). Finally, PPD-based color picture printing is demonstrated on a wide range of substrates, including glass, PDMS, and plastic, proving its broad potential in future applications from security labeling to color displays.
For mobile streaming media service providers, it is necessary to accurately predict the quality of experience (QoE) to formulate appropriate resource allocation and service quality optimization strategies. In this paper, a QoE evaluation model is proposed by considering various influencing factors (IFs), including perceptual video quality, video content characteristics, stalling, quality switching and video genre attribute. Firstly, a no-reference video multimethod assessment fusion (VMAF) model is constructed to measure the perceptual quality of the video by the deep bilinear convolutional neural network. Then, the deep spatio-temporal features of video are extracted using a TSM-ResNet50 network, which incorporates temporal shift module (TSM) with ResNet50, obtaining feature representation of video content characteristics while balancing computational efficiency and expressive ability. Secondly, video genre attribute, which reflects the user’s preference for different types of videos, is considered as a IF while constructing the QoE model. The statistical parameters of other IFs, including the video genre attribute, stalling and quality switching, are combined with VMAF and deep spatio-temporal features of video to form an overall description parameters vector of IFs for formulating the QoE evaluation model. Finally, the mapping relationship model between the parameters vector of IFs and the mean opinion score is established through designing a deep neural network. The proposed QoE evaluation model is validated on two public video datasets: WaterlooSQoE-III and LIVE-NFLX-II. The experimental results show that the proposed model can achieve the state-of-the-art QoE prediction performance.
This study aims to examine the green innovation effect of the carbon emissions pilot policy in China. First, using the difference-in-differences method and regressions of instrumental variables using the data from Chinese listed firms, we verify that the policy promotes green innovation among regulated firms and is more pronounced among state-owned enterprises, firms in the eastern region, and those with lower financing constraints. Furthermore, this positive effect spreads downstream relative to the regulated firms through input–output linkages, but reduces green innovation to upstream firms. Accordingly, such diffusion of innovation is achieved through the price mechanism. The results necessitate the introduction of various derivatives to mobilize the market to reduce the speculative volatility of carbon prices. In addition, relevant supporting policies must be established to encourage corporate innovation to reduce the crowding-out effect owing to emission reduction and the nonmarket factors.
With the rapid development of metal halide perovskite, reducing its dimensionality into two-dimensional (2D) or one-dimensional (1D) nanostructures has been reported to be a good alternative for expanding the spectral absorption or emission range. For example, when substituting monovalent cation Cs⁺ with phenylethylammonium (PEA⁺) on fabricating 2D-CsPbI3, the photoluminescence peak processes a maximum regulation from 710 to 625 nm. Simultaneously, when slicing into 1D CsPbI3 nanowires, the light emission could also achieve a maximum blue shift from 700 to 600 nm. Herein, by using a ligand-assistant reprecipitation (LARP) method, oleic acid (OA) molecule is successfully inserted into the lattice of one-dimensional CsPbI3 nanowire (namely OA-CsPbI3), which presents a monochromatic yellow light emission at 558 nm with narrow emission-band (about 28 nm), and records high photoluminescence quantum yield (PLQY) of 94%. Such a yellow-light emission in single halide CsPbI3 systems has never been discovered before. Meanwhile, a shallow energy level in the OA-CsPbI3 nanowire is further identified by the ultrafast transient absorption (TA) and first-principle calculation, which helps the photoexcited carriers bypass the trap state level in the bandgap and enhances the radiative excitons lifetime with maximum binding energy up to 212.5 meV. What's more, the excellent thermal and moisture stabilities of the newly formed one-dimensional OA-CsPbI3 nanowire indicate a promising application prospect in the field of luminescent devices.
Background Wild potato species harbor a distinctive rhizosphere microbiome relative to their modern counterparts, thus providing a competitive advantage for acquiring phosphorus (P) in their native habitats. Despite this, the effects of transferring phosphorus-solubilizing bacteria (PSB), recruited from wild potatoes rhizosphere, on modern potato varieties’ performance has not been investigated. Here, it was hypothesized that PSB isolated from wild potatoes could enhance plant growth and solubilization of various P forms when co-inoculated with commercial potatoes ( Solanum tuberosum ). Results To test this hypothesis, three bacteria Enterobacter cloacae , Bacillus thuringiensis , and Pseudomonas pseudoalcaligenes were isolated from the rhizosphere of the wild potato Solanum bulbocastanum grown under greenhouse conditions and characterized for their P-solubilizing activities. It was found that both individual bacterial species and the consortium of the three bacteria, dissolved organic (i.e., phytin) and inorganic P (i.e., calcium phosphate) in vitro. The bacterial consortium increased dissolved P by 36-fold for calcium phosphate and sixfold for phytin compared to a sterile control and surpassed the effect of each individual PSB strain. To further evaluate the effect of the PSB consortium on plant growth and P use efficiency, the bacteria were co-inoculated on a commercial potato cultivar and amended separately with phytin, calcium phosphate, commercial P fertilizer, or a combination of the three P sources. The results showed an overall increase in total dry biomass and shoot P content in treatments co-inoculated with PSB. Conclusions Our findings indicate that PSB isolated from wild potatoes and inoculated with modern potato varieties have the potential to enhance yield and nutrient uptake.
Light-emitting fabric can facilitate the innovation of wearable display applications. Electronic and luminescent textiles capable of communicating, sensing, and supplying energy have been achieved. However, a facile strategy for fabricating large-area flexible lasing textiles has not yet been reported. In this work, we propose a gravity-assisted rotatory drawing method for fabricating flexible lasing microfibers, which can be woven into multicolor lasing textiles. By regulating the doped dyes and solution viscosity, we achieve the mass manufacturing of lasing microfibers with different emission colors and modes and further weave them into full-color textiles with a wide color gamut, approximately 79.1% larger than that of standard RGB space. For application, we print nanoparticle patterns on the lasing textile and encode it with programmable lasing signal distribution, which can supply an anticounterfeiting label for efficient authentication. This work unifies the fabrication and application of lasing textiles, and we expect that this will provide a new platform for flexible lasing devices.
Extraordinary angularly asymmetric electromagnetic manipulation is desirable in angle-encoded steganography, directional display, one-side sensing and other unconventional applications, which however is challenging. Here, we build the non-Hermitian surface scattering systems by silicon nano-posts with spatially modulated loss, showing the extraordinary angular-asymmetry in the near infrared regime under different polarized light incidence. Especially a polarization-insensitive unidirectional retroreflector with non-Hermitian metasurface is first demonstrated at a fourth-order exceptional point (EP), related to the collapsed EPs of a generalized 4 × 4 non-Hermitian scattering matrix under TM and TE polarization states. Our investigations provide a new perspective for designing polarization-insensitive asymmetric photonic devices for wave manipulation and applying more generalized non-Hermitian physics to surface scattering systems.
This study deals with the influence of high concentration and high dispersion nanodots in toluene-based fluids on the improvement of the thermal conductivity of ZrO2/toluene nanofluids by experimental measurement and theoretical analysis. For this purpose, the high concentration (13.21 vol%) ZrO2/toluene nanofluids were diluted to 2.20%, 4.41%, 6.61%, 8.81% and 11.01%, respectively. And their thermal conductivities were measured using the transient planar heat source method (TPHS). Subsequently, the predicted values of four classical theoretical models were compared and discussed with experimental values in detail to verify the applicability of theoretical models. The results indicate that almost all of the thermal conductivities of ZrO2/toluene nanofluids are higher than that of the pure toluene fluid, and the trend is rising with the increase in temperature. Moreover, the thermal conductivity enhancement of ZrO2/toluene nanofluid with 13.21 vol% has achieved 38.7% and 59.9% at room temperature and 62.8 °C, respectively. The visualized behaviors of nanodots, including migration, collision, energy exchange between the nanodots reveal the physical mechanism that contributes to the thermal conductivity of nanofluids. And the thermal conductivity enhancement of nanofluid per unit concentration quantifies the mechanism. The Brownian motion of nanodots may play a crucial role, which influences the move, collision, and micro-convection. The predicted values of four models were compared and discussed with experimental values to verify the applicability of theoretical models. The analysis reveals that all predicted values are closer to experimental results at low temperatures. Compared with other models, the thermal conductivity value predicted by the Shukla model is closer to the experimental value at middle and high temperatures. In this work, the thermal conductivity of ZrO2/toluene nanofluids has been greatly improved, which has shown its promising applicability in thermal management in practical engineering applications.
In order to build Beijing into a world-class harmonious and livable capital city, its energy and environmental issues deserve special attention. Considering London’s experience and achievements in tackling smog crisis, New York’s world-class city positioning and influence, and Tokyo’s geographical and cultural characteristics, these three world-class megacities are taken as examples in this study for investigating and analyzing the establishment and optimization of an urban energy system which is clean, efficient, safe, and sustainable. The urban planning strategies, planning experience, and related energy policy of these three megacities for building low-carbon cities are reviewed and their enlightenment to Beijing is discussed. The study of international cases has a certain reference effect on the energy structure adjustment and urban planning practices related to Beijing’s construction of a low-carbon city.
Many actions have been carried out to promote the low-carbon society construction in China, emission trading system (ETS) and energy efficiency trading (EET) are two typical instruments during the past five years. This paper focused on the coexistence between ETS and EET aiming to explore whether policy-mix could enhance industries’ interests through the partial-equilibrium model. The results showed that when overlapping transaction scope is allowed, the shadow price of energy saving is certain for the industries. When is not, setting up the policy-mix would not improve energy savings of industries but would make the two trading systems complementary, via the internal mechanism of the policy-mix and providing multiple policy options. The analysis indicated that EET and ETS could coexist and yield better results. Energy saving quota and CO2 quotas can be mutually recognized in a certain method avoiding the repeated accounting between two quotas, which will not increase the burden of enterprises but encourage them to conduct flexible emission reduction to enhance the competitiveness.
This paper reports a form-stable molten salt based composite phase change material (CPCM) owning extremely low melting point and large temperature range that can be a promising candidate used in low and middle temperature thermal energy storage fields. The composite was prepared by a so-called cold compress and hot sintering approach with a eutectic quaternary nitrate of Ca(NO3)2-KNO3-NaNO 3-NaNO 2 used as phase change material (PCM), a MgO as structure supporting material (SSM) and graphite as thermal conductivity enhancer (TCE). A series of characterizations were carried out to investigate the composite microstructure, chemical compatibility and thermal properties as well as cycling stability. The results show no chemical reaction occurred among the compositions of salt, SSM and TCEM before and after sintering, indicating excellent chemical and physical compatibility in the composite. A fairly low melting point around 89.56 °C and relatively high decomposition temperature of 628 °C were observed, giving the composite a large energy storage density over 626 kJ/kg at temperature range of 50–600 °C. A mass loading of 50% MgO gives the optimal formulation of the composite at which over 10% graphite can be involved and a thermal conductivity over 1.4 W/m⋅ ∘C can be obtained. The present results indicate that such a salt based composite with fairly low melting temperature and large temperature range could be an effective alternative to organic based PCMs used in low-mid temperature thermal energy storage.
For the purpose of better matching the performance of the organic Rankine cycle (ORC) system concerning the vehicle engine waste heat recovery, this paper studies the output performance of free piston expander-linear generator (FPE-LG). A test bench of FPE-LG is established for small scale ORC system, and timing and displacement control strategy is proposed. Furthermore, the impact of the intake pressure and the torque on motion characteristics and output performance of FPE-LG are analyzed. According to evaluating different learning rates, number of hidden artificial neural networks and training functions, a prediction model of FPE-LG based on artificial neural network is established. Genetic algorithm is used to optimize the key operating parameters, to maximize the power output of FPE-LG. In consideration of the mean square error and determination coefficient, the artificial neural network model is verified and tested by experimental data. Finally, combining genetic algorithm with artificial neural network model, the maximum power output of FPE-LG is optimized and its performance is predicted. The results show that the maximum value of electric current, voltage and power output are 2.8 A, 14.75 V and 28.5 W, respectively. Based on artificial neural network, this method can provide useful guidance for performance prediction and coordinated optimization, with advantages of minimum deviation and high precision.
The distribution of rooftop PV is spatial heterogeneity with the location of buildings. With the assistance of geographic information systems, this study proposed an integrated research framework for the geo-spatial potential assessment of PV systems on the roof of urban buildings as well as its feasibility analysis. Furthermore, a geo-spatialization of electricity self-sufficiency rate is also introduced to explore the contribution of rooftop PV on urban energy security. The proposed approach was verified by a case study in the central area of Wuhan, China with a 100 m spatial resolution. The results show that the maximum annual PV power generation potential in the central area of Wuhan is 10,757 GWh, which could satisfy 31.83% of local energy consumption. A 100% deployment of rooftop PV would also lead to a great carbon emission reduction, i.e. 8.62 million tons per year, with an annual total cost of 8.07 bn USD. According to the distribution characteristics of the PV self-sufficiency rate on the map, priority is given to investing in the peripheral areas of the city with the higher return. It is estimated that when the PV conversion rate increases by 5%, the peak self-sufficiency rate to the area will increase by 20% in Wuhan. Moreover, if the conversion rate of PV system reaches 47.13%, a fully utilized rooftop in Wuhan for PV deployment could meet the local electricity demand, which implies a 100% electricity self-sufficiency. This study is helpful to understand the spatial heterogeneity of rooftop PV in urban areas, and could serve as the guidelines for local governments to develop renewable energy in a sustainable way.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
5,217 members
• Faculty of Materials and Manufacturing
• College of Economic and Management
• College of Materials Sciences and Engieering
• Dept. Mechanical Engineering
• College of Artificial Intelligence and Automation
Information