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This paper focuses on urban regeneration practices in central urban areas, aiming to find key points for reshaping the publicness of urban spaces by exploring the morphological features of public spaces and the spatial distribution patterns of public activities. Now that China’s urbanization process has stabilized, large-scale regeneration is no longer applicable to the current urban environment, and urban morphology has proved to be significantly useful in understanding and designing the built environment. However, current research lacks quantitative studies on morphology and public activities, and thus is hardly instructive for the cognition and design of spatial morphology in specific locations. Therefore, this paper attempts to subdivide spatial morphology at the level of “micro-renewal” or “micro-renovation” in order to explore the impact of spatial morphology on public participation in cities. The site chosen for this study is Xinjiekou in Nanjing. As a key area of two important arteries in the center of Nanjing, Xinjiekou has been a gathering place for a variety of commercial forms, such as finance, retail department stores, restaurants, and entertainment, and has been the commercial and financial center of Nanjing since the 1940s. In an on-site observation of urban development and pedestrian flow in the Xinjiekou area, the study found that despite its status as the area with the highest degree of spatial accessibility and public participation, its public space has gradually lost its attractiveness to residents, who lack a sense of participation and place identity. Based on the study of urban public spaces, both accessibility and choice play an important role in increasing public participation. Therefore, this study combined observation and quantitative analysis of Space Syntax to obtain the distribution of accessibility, choice, and public activity. Based on the results of the analysis, this research uses GWR as the statistical method to clarify the correlation between different variables. The final conclusion is that when the space type is a path with high choice value and the paths are connected, the enhancement of accessibility and choice plays an important role in promoting public participation. This statistically based empirical study of testable correlations is very helpful for the perception of location-specific spaces with high levels of interpretability and confidence. Thus, it further guides the design and has a high reference value for future spatial planning.

In ultra-wideband (UWB) impulse parameter monitoring, amplitude and pulse width measurements are crucial for detecting and classifying unknown impulse signals. Meanwhile, mass sampling data and the complexity of the current measurement technologies are the limitations of real-time and continuous monitoring. It urges to adopt a simple method that reduces the sampling rate without sacrificing accuracy. This article suggests a novel approach for the monopole UWB by analog signal preprocessing before sampling. A peak detector and integrator are utilized to retain the impulse amplitude and the area inside the impulse for a period, which is the so-called waveform transformation. A low-speed two-channel synchronous ADC samples the output signals from the peak detector and the integrator to determine the amplitude and the area. In the signal process, we demonstrate the concept that pulse width value equals the division of area and amplitude. Due to the distorted amplitude caused by the response time of the peak detector, the relationship between the distorted amplitude, the actual amplitude, and the pulse width is analyzed and examined based on the response function. Finally, the actual amplitude and pulse width are acquired and calculated by solving two equations. In terms of impulse waveform, rectangular and Gaussian waveform factors are developed and applied in the measurement. The proof-of-concept experimental results of a 0.68 V/ 18.8 ns rectangular impulse at a 10 Msps sampling rate is 0.711 V/ 17.00 ns with 4.56 %/ 9.57% relative error, tested and verified.

The “Tianzihao” colony was built by the French Jesuits in the 1890s. As one of the earliest examples of the French Catholic Church’s mission in China, as well as the only case in Nanjing, it shows the historical scenes of Western missionaries in Nanjing 120 years ago. It is a demonstration of cultural exchanges between China and the West after China opened to the Western world in the late 19th century. In architectural style, the “Tianzihao” colony is Western-style townhouses, but a large number of traditional Chinese architectural technologies were used for it, and therefore it is characterized by Western space and Chinese technology. The “Tianzihao” colony was badly damaged during these decades, with a lot of decayed building materials and structures on the verge of collapse. Based on the historical research and technical analysis of the “Tianzihao” colony, this article explores the conservation strategies and methods of reusing the architectural heritage. In addition, this article is to study the characteristics of the times before introduction of Western architectural technology in Nanjing based on an analysis on the building technology used for the “Tianzihao” colony. The authors participated in the conservation and restoration project of the “Tianzihao” colony, and the objective of this study was achieved through some qualitative methods, including collection and analysis of archival data, analysis of old maps and photos, architectural mapping and a large amount of historical information found in the conservation process.

This paper proposes a deep neural network (DNN) based method to solve the multicell power control problem that maximizes the sum rate subject to per-user rate constraints. The basic idea is to employ a two-DNN concatenating network structure, where the second DNN associated with a randomization processing is designed to guarantee the per-user rate constraints via supervised learning, given which the first DNN is trained to directly maximize the sum rate by unsupervised learning. Simulation results demonstrate that the proposed method can achieve better performance with low complexity compared to existing deep learning and numerical optimization methods.

User scheduling is an efficient way to harvest the frequency and multiuser diversity gain for uplink Orthogonal Frequency Division Multiple Access (OFDMA) system. To solve the non-convex scheduling problem, existing numerical or searching based solutions face the difficulty of meeting the real-time requirement of fast scheduling. In this paper, a deep learning based method is proposed to solve the user scheduling problem, aimed at reducing the scheduling complexity for real-time implementation. The key challenge of learning the scheduling decisions lies in how to ensure that the learned decisions satisfy the coupled binary constraint. To tackle the difficulty, we design a deep neural network (DNN) to approximate the binary vector quantization operation. The DNN is then used as the activation function in the output layer of another DNN, where the latter is trained to directly maximize the performance utility via unsupervised learning. Simulation results demonstrate that the proposed method is able to largely reduce the complexity with marginal performance and fairness loss compared to the greedy searching method. Index Terms–User scheduling, deep learning, OFDMA.

In the paper we study a deep learning based method to solve the multicell power control problem for sum rate maximization subject to per-user rate constraints and per-base station (BS) power constraints. The core difficulty of this problem is how to ensure that the learned power control results by the deep neural network (DNN) satisfy the per-user rate constraints. To tackle the difficulty, we propose to cascade a projection block after a traditional DNN, which projects the infeasible power control results onto the constraint set. The projection block is designed based on a geometrical interpretation of the constraints, which is of low complexity, meeting the real-time requirement of online applications. Explicit-form expression of the backpropagated gradient is derived for the proposed projection block, with which the DNN can be trained to directly maximize the sum rate via unsupervised learning. We also develop a heuristic implementation of the projection block to reduce the size of DNN. Simulation results demonstrate the advantages of the proposed method over existing deep learning and numerical optimization~methods, and show the robustness of the proposed method with the model mismatch between training and testing~datasets.

In this paper we investigate the codebook design for a time division duplex (TDD) non-antenna switching system, where the user device is equipped with two receive radio frequency (RF) chains but only one transmit RF chain. To acquire the channel state information at transmitter (CSIT), we resort to a mixture of channel sounding and limited feedback, where the former is employed to obtain the CSIT of the antenna connected to the transmit RF chain and the latter is employed for the other antenna. We propose a deep learning method to design the codebook for limited feedback. The learned codebook is distinguished from the traditional ones in two ways. First, the learned codebook is so-called personalized, which is not fixed but adapt to the partially known CSIT. Second, the codebook exhibits different beam patterns from the traditional codebook that is designed for quantization error minimization. Simulation results demonstrate that the learned codebook can achieve higher data rate with lower complexity than traditional codebook.

Climate change has been recognized as a threatening environmental problem around the world. CO2 is considered to be the main component of greenhouse gas. By using solar energy (light energy) as the energy source, photocatalytic conversion is one of the most effective technologies to reveal the clean utilization of CO2. Herein, using sodium tungstate, nickel nitrate, and selenium powder as the main raw materials, the high absorption and utilization of WSe2 for light energy and the high intrinsic conductivity of NiSe2 were combined by a hydrothermal method to prepare NiSe2/WSe2 and hydrazine hydrate as the reductant. Then, high-performance NiSe2/WSe2 photocatalytic material was prepared. The characterization results of XRD, XPS, SEM, specific surface area, and UV-visible spectroscopy show that the main diffraction peak of synthesized NiSe2/WSe2 is sharp, which basically coincides with the standard card. After doping NiSe2, the morphology of WSe2 was changed from a flake shape to smaller and more trivial crystal flakes, which demonstrates richer exposed edges and more active sites; the specific surface area increased from 3.01 m2 g−1 to 8.52 m2 g−1, and the band gap becomes wider, increasing from 1.66 eV to 1.68 eV. The results of a photocatalytic experiment show that when the prepared NiSe2/WSe2 catalyst is used to conduct photocatalytic reduction of CO2, the yield of CH3OH is significantly increased. After reaction for 10 h, the maximum yield could reach 3.80 mmol g−1, which presents great photocatalytic activity.

Flow-induced formation of crystalline precursors was systematically investigated for entangled poly (vinyl alcohol) (PVA) aqueous solutions. The preshear flow was applied at 25 °C, a temperature well above the crystallization temperature. The crystalline precursors formed when the Weissenberg number ( W i R = γ ˙ τ R , defined with respect to the Rouse time) became larger than one such that polymer chains are stretched. The precursors lead to an additional relaxation process that was much slower than the chain relaxation process in the linear viscoelasticity. The precursors were very stable at 25 °C, which could be effectively smeared by application of a secondary shear flow that was much slower than the preshear, but faster than the inverse of a characteristic time scale of the slow process (arising from the crystalline precursors). During the secondary shear flow, the stress growth coefficient shows a strong stress overshoot, indicating that the flow-induced crystalline precursors had a structure akin to the associative network, probably owing to the disruption of the protective hydration shell around PVA molecules by the preshear flow, allowing the interchain hydrogen bonds to form.

General matrix multiplication (GEMM) plays a paramount role in a broad range of domains such as deep learning, scientific computing, and image processing. The primary optimization method is to partition the matrix into many tiles and exploit the parallelism within and between tiles. The tiling hierarchy closely mirrors the thread hierarchy on GPUs. In practice, GPUs can fully unleash its computing power only when the matrix size is large and there are sufficient number of tiles and workload for each tile. However, in many real-world applications especially deep learning domain, the matrix size is small. To this end, prior work proposes batched GEMM to process a group of small independent GEMMs together by designing a single CUDA kernel for all of these GEMMs. However, the current support for batched GEMM is still rudimentary. Tiling and batching are tightly correlated. A large tile size can increase the data reuse, but it will decrease the thread-level parallelism, which further decrease the optimization space for the batching. A small tile size can increase the thread-level parallelism and then provide larger optimization space for the batching, but at the cost of sacrificing data reuse. In this paper, we propose a coordinated tiling and batching framework for accelerating GEMMs on GPUs. It is a two-phase framework, which consists of a tiling engine and a batching engine to perform efficient batched GEMM on GPUs. Tiling engine partitions the GEMMs into independent tiles and batching engine assigns the tiles to thread blocks. Moreover, we propose a general programming interface for the coordinated tiling and batching solution. Finally, experiment evaluation results on synthetic batched GEMM cases show that our framework can achieve about 1.40X performance speedup on average over the state-of-the-art technique. We also use GoogleNet as a real-world case study and our framework can achieve 1.23X speedup.

In this work, a novel cellulose aerogel (CNC-PVAm/rGO) was fabricated using cellulose nanocrystalline (CNC) modified with polyvinylamine (PVAm) and reduced graphene oxide (rGO). The resultant CNC-PVAm/rGO was then applied for the adsorption of diclofenac sodium (DCF), a typical non-steroidal anti-inflammatory drug. Characterization using ultra-high-resolution field emission scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and the Brunauer-Emmett-Teller surface area revealed that the obtained CNC-PVAm/rGO displayed an evident 3D porous structure, which had an ultralight weight, good recovery, abundant surface functional groups (e.g., –NH2 and –OH), and rGO nanosheets. In addition, the material presented a stable crystal structure and large specific surface area (105.73 m2 g−1). During the adsorption of DCF, the CNC-PVAm/rGO aerogel showed a rather excellent adsorption performance, with a maximum adsorption capacity (qmax) of 605.87 mg g−1, which was approximately 53 times larger than that of the bare CNC aerogel (11.45 mg g−1). The adsorption performance of CNC-PVAm/rGO was also better than that of other reported adsorbents. The adsorption of DCF to CNC-PVAm/rGO obeyed the Langmuir isotherm and pseudo-second-order kinetic models, and underwent a spontaneous exothermic process. Moreover, DCF was easily desorbed from CNC-PVAm/rGO with sodium hydroxide solution (0.1 mol L−1), and the absorbent could be reused four times. The introduction of PVAm and rGO to the CNC-PVAm/rGO aerogel also greatly enhanced electrostatic interactions, π-π interactions, and hydrophobic effects. These enhancements significantly promoted the hydrogen bonding interactions between the DCF molecules and CNC-PVAm/rGO, thus resulting in a large improvement in the adsorption performance of the aerogel.

As a classic component of non-steroidal anti-inflammatory drug (NSAID), diclofenac has the anti-inflammatory, analgesic and antipyretic effects. As a result, the medical wastewater discharged from some diclofenac pharmaceutical factories may contain a certain amount of diclofenac. This pollutant is toxic and harmful to livestock, and if the livestock have digested diclofenac, it will affect their cellular metabolism process, cellular components and protein catalysis, which may even cause mass deaths under severe situation. An effective treatment method for this pollutant is to adopt composite aerogel adsorption. This paper briefly introduces the modification plan of cellulose and the process of using cellulose to adsorb diclofenac sodium (DCF), compares the adsorption effects before and after modification, and proposes a new direction to treat this kind of pollutant based on the comparison results. In this paper, by using cotton fiber as the material and polyvinylamine as the modifier, the hydrothermal synthesis method is employed to prepare the rGO/cellulose composite aerogel material, and the characterization analysis of its structure and performance is also conducted. The characterization results of SEM, EDS, XRD. FTIR etc. show that the composite aerogel material prepared in our experiment has maintained the crystal structure of cellulose, which has obvious 3D porous structure, and its specific surface area reaches 354.7m2/g. In addition, the material surface contains abundant amino, hydroxyl, C=N and other functional groups. Secondly, with diclofenac sodium as the targeted pollutant, this paper investigates the performance and mechanism of using rGO/cellulose composite aerogel to adsorb diclofenac sodium. The experimental results show that the rGO/cellulose composite aerogel presents outstanding performance in adsorption of DCF, which is significantly superior to the performance of cellulose aerogel material. When the rGO/cellulose composite aerogel and the cellulose aerogel are used to adsorb DCF, their maximum adsorption capacities are 242.94mgg-1 and 47.82mgg-1, respectively. In addition, the process of using composite aerogel to adsorb DCF satisfies the Langmuir isothermal adsorption model and the pseudo-second-order kinetic model, which indicates that this adsorption is chemical adsorption mainly consisting of monolayer adsorption. The thermomechanical analysis shows that the adsorption of DCF using composite aerogel is spontaneous exothermic reaction. In the meantime, the composite aerogel has wider pH adaptability, and it presents great adsorption efficiency of DCF within the pH range of 4-10.

High-salt wastewater refers to the wastewater containing more than 1% of total salinity in domestic wastewater and industrial wastewater. The wastewater contains relatively high levels of inorganic ions such as Cl-, SO4 2-, Na+, and Ca2+, which will inhibit the growth of microorganisms, making it one of the extremely difficult to treat wastewater. In this study, the known saline-alkali soil was used as a sample source, and halophilic strains were screened out using shaking, culture, and domestication methods. The morphological characteristics were observed by light microscopy. Microscopic examination showed that the strain was a milky white colony, round or Oval shape, smooth and moist, convex lens surface, moderate area, opaque, bulging in the middle, edge is not spread, the same color on both sides. The effects of salt concentration, culture time, initial organic wastewater concentration, and strain size on the degradation performance of the selected strains were studied. The experimental results show that: when the salt concentration of wastewater is 20%, the initial organic wastewater concentration is 2000mg L-1, the added culture volume is 20 ml, the culture time is 72 hours, the COD removal rate of wastewater can reach 66.4%.

Climate change can make a profound impact on the entire ecosystem, and severely affects the stability of human society. The urban is the economic, political and cultural center of human society. The assessment of urban disaster vulnerability is of great significance to the disaster prevention system. Through the review of the relevant literatures on disasters vulnerability and the situations of Sichuan Province, the study establishes urban climate change actual vulnerability assessment systems. The vulnerability degrees of the five economic zones of Sichuan Province are evaluated by the improved mutation progression method based on the entropy method. The study shows that the urban disaster vulnerability of Southern Sichuan Economic Region is the most serious, and the degree of disaster vulnerability of Chengdu Economic Region is the lowest. Chengdu Economic Region should concern about the floating population. And Southern Sichuan Economic Region needs to create a good environment for enterprises to increase the number of enterprises within its jurisdiction.

As a promising procedure of mobile application. So far, lifelogging has already some initial attempts on photos, audios and video records. However, they are just simple information recording tools, in which the receivers cannot feel the senders with empathy in space or time. In this poster, we propose a concept called MemoryRetrospect, which combines Lifelogging with Social Awareness. It considers not only our daily photos and videos, but also the weather, the locations and time. When and how to open the e-records can be set by the senders' willing. Thus the receivers have a chance to feel the true space-time meaning of the e-records. More exactly, every e-record will be packaged in a capsule, which the senders are able to set with kinds of scenes as the activation conditions for recipients. With this, the recipients can experience and understand senders' happiness, beautiful moments and emotions at some certain moment.

A series of new 2-phenyl-quinoline-4-carboxylic acid derivatives was synthesized starting from aniline, 2-nitrobenzaldehyde, pyruvic acid followed by Doebner reaction, amidation, reduction, acylation and amination. All of the newly-synthesized compounds were characterized by ¹H-NMR, (13)C-NMR and HRMS. The antibacterial activities of these compounds against Gram-negative (Escherichia coli, Pseudomonas aeruginosa) and Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis), as well as one strain of methicillin-resistant Staphylococcus aureus (MRSA) bacteria were evaluated by the agar diffusion method (zone of inhibition) and a broth dilution method (minimum inhibitory concentration (MIC)), and their structure-activity relationships were obtained and discussed. The results revealed that some compounds displayed good antibacterial activity against Staphylococcus aureus, and Compounds 5a₄ and 5a₇ showed the best inhibition with an MIC value of 64 μg/mL against Staphylococcus aureus and with an MIC value of 128 μg/mL against Escherichia coli, respectively. The results of the MTT assay illustrated the low cytotoxicity of Compound 5a4.