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Ding, Yipu, Liu, Zinuan, Wang, Xi, Xin, Ran, Shan, Dongkai, He, Bai, Jing, Jing, Gao, Qi, Yang, Junjie, Chen, Yundai (2023): Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound. Journal of Herpetology 13 (4): 298-309, DOI: 10.1670/20-035, URL: http://dx.doi.org/10.21037/qims-22-832

Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by ``overfitting" specific task type, leading to enhanced performance compared to analytical optimizers. Generally, L2O develops a parameterized optimization method (i.e., ``optimizer") by learning from solving sample problems. This data-driven procedure yields L2O that can efficiently solve problems similar to those seen in training, that is, drawn from the same ``task distribution". However, such learned optimizers often struggle when new test problems come with a substantially deviation from the training task distribution. This paper investigates a potential solution to this open challenge, by meta-training an L2O optimizer that can perform fast test-time self-adaptation to an out-of-distribution task, in only a few steps. We theoretically characterize the generalization of L2O, and further show that our proposed framework (termed as M-L2O) provably facilitates rapid task adaptation by locating well-adapted initial points for the optimizer weight. Empirical observations on several classic tasks like LASSO and Quadratic, demonstrate that M-L2O converges significantly faster than vanilla L2O with only $5$ steps of adaptation, echoing our theoretical results. Codes are available in https://github.com/VITA-Group/M-L2O., Comment: This paper is accepted in ICLR 2023

Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order of several doped cuprate materials and structures. We find that we can correctly capture two well-known trends: the pressure effect, where pairing order increases with intra-layer pressure, and the layer effect, where the pairing order varies with the number of copper-oxygen layers. From these calculations, we observe that the strength of superexchange and the covalency at optimal doping are the best descriptors of the maximal pairing order. Our microscopic analysis further identifies short-range copper spin fluctuations, together with multi-orbital charge fluctuations, as central to the pairing trends. Our work illustrates the possibility of a quantitative computational understanding of unconventional high-temperature superconducting materials., Comment: 10 pages, 5 figures, with supplementary materials

Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage. MRC systems must not only answer question when necessary but also distinguish when no answer is available according to the given passage and then tactfully abstain from answering. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder, though the latest practice on MRC modeling still most benefits from adopting well pre-trained language models as the encoder block by only focusing on the "reading". This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions. Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yield an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction. The proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0 and NewsQA, achieving new state-of-the-art results. Significance tests show that our model is significantly better than the strong ELECTRA and ALBERT baselines. A series of analysis is also conducted to interpret the effectiveness of the proposed reader., Accepted by AAAI 2021

Objective: Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease characterized by aggressive and symmetric polyarthritis. Our previous study showed that celastrol (Cel) protected against RA by inhibiting the NLRP3 inflammasome pathway, but the molecular mechanism has not been clarified. Methods: A type II collagen-induced arthritis (CIA) DBA/1 mouse model was used to study the antiarthritic effects of Cel, and paw swelling, arthritis scores, serum cytokine levels, and pathological examinations were carried out. The effects of Cel on the fibroblast-like synoviocytes (FLSs) viability, proliferation and migration of tumour necrosis factor-α (TNF-α)-induced FLSs were tested by MMT assays, EdU staining and scratch wound healing assays. The proinflammatory factors were evaluated by ELISA. The expression of NLRP3, ASC, cleaved caspase-1, p-p65, p65 and p-IκB-α was examined in vitro and in vivo by western blotting and immunofluorescence analysis, respectively. The putative binding sites between Cel and Hsp90 were predicted through molecular docking analysis, the Octet RED96 system and coimmunoprecipitation. Results: The results showed that Cel decreased arthritis severity and reduced TNF-α-induced FLS migration and proliferation. Additionally, Cel decreased the protein expression of NLRP3, ASC, and cleaved caspase-1 and the secretion of proinflammatory cytokines. Furthermore, Cel directly interacted with Hsp90, which interacts with NLRP3, and Cel blocked the interaction between Hsp90 and NLRP3 in FLSs. Conclusion: In summary, our findings demonstrate that Cel regulates NLRP3 inflammasome pathway in vivo and in vitro. Cel inhibits the proliferation and migration of FLSs by blocking the interaction between Hsp90 and NLRP3, thus inhibiting NLRP3 inflammasome pathway.

Additional file 1.

Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames. Previous animal-related datasets focus either on animal tracking or single-frame animal pose estimation, and never on both aspects. The lack of APT datasets hinders the development and evaluation of video-based animal pose estimation and tracking methods, limiting real-world applications, e.g., understanding animal behavior in wildlife conservation. To fill this gap, we make the first step and propose APT-36K, i.e., the first large-scale benchmark for animal pose estimation and tracking. Specifically, APT-36K consists of 2,400 video clips collected and filtered from 30 animal species with 15 frames for each video, resulting in 36,000 frames in total. After manual annotation and careful double-check, high-quality keypoint and tracking annotations are provided for all the animal instances. Based on APT-36K, we benchmark several representative models on the following three tracks: (1) supervised animal pose estimation on a single frame under intra- and inter-domain transfer learning settings, (2) inter-species domain generalization test for unseen animals, and (3) animal pose estimation with animal tracking. Based on the experimental results, we gain some empirical insights and show that APT-36K provides a valuable animal pose estimation and tracking benchmark, offering new challenges and opportunities for future research. The code and dataset will be made publicly available at https://github.com/pandorgan/APT-36K., Comment: Neurips 2022 dataset and benchmark track

Drug addiction is a serious health problem prevalent worldwide. Currently available therapies including pharmacotherapy and psychotherapy are insufficient to meet the clinical needs for treating drug abuse. Recently, immunotherapy has emerged as a promising approach to treat such drug-use disorders. Pharmacokinetic antagonists are used in immunotherapy, functioning by sequestering the drugs in the periphery but without allowing the drug to cross the blood-brain barrier. This can reduce the toxic and rewarding effects of the drugs, while preventing addiction and facilitating reduced relapse rates. Herein, we update recent developments in the immunotherapeutic strategies to treat abuse of drugs like methamphetamine, heroin and cocaine. In addition, we summarize the drug design used so far and its optimization strategies. Further, we document the efficacy of anti-drug vaccines and monoclonal antibodies, with an aim to promote development of new anti-drug immunotherapies.

The prostanoid EP4 receptor is one of the key receptors associated with inflammatory mediator PGE2-elicited immunosuppression in the tumor microenvironment. Blockade of EP4 signaling to enhance immunity-mediated tumor elimination has recently emerged as a promising strategy for cancer immunotherapy. In our efforts to discover novel subtype-selective EP4 antagonists, we designed and synthesized a class of 1H-1,2,3-triazole-based ligands that display low nanomolar antagonism activity toward the human EP4 receptor and excellent subtype selectivity. The most promising compound 59 exhibits single-digit nanomolar potency in the EP4 calcium flux and cAMP-response element reporter assays and effectively suppresses the expression of multiple immunosuppression-related genes in macrophage cells. On the basis of its favorable ADMET properties, compound 59 was chosen for further in vivo biological evaluation. Oral administration of compound 59 significantly inhibited tumor growth in the mouse CT26 colon carcinoma model accompanied by enhanced infiltration of cytotoxic T lymphocytes in the tumor tissue.

Nowadays with the development of smart residential microgrid (RMG), the distributed energy storage system (DESS) can help consumers to not only balance generation and consumption but also participate in demand respond. However, the unadjustable capacity of DESS and the lack of energy sharing among users have become the major challenges to the further development of RMG. This paper proposes a novel electricity scheduling architecture based on energy cloud (EC) for RMGs and designs an electricity scheduling optimisation. The EC is used in order to link different end-users and promote coordination. In the proposed EC-based electricity scheduling architecture, the mathematical model for the RMG is provided. Moreover, considering the depreciation cost of battery, the optimisation model is established with the objective of minimising the electricity cost. Compared with the traditional RMG, simulation results show that the proposed strategy can not only allow consumers to adjust their optimal energy storage capacity but also further reduce electricity payment costs. The designed strategy provides a new and effective research perspective for electricity scheduling of RMGs.

Hydrogen sensors with fast response and recovery rate based on nanoporous palladium (Pd) and titanium dioxide (TiO2) composite films supported by anodic aluminum oxide (AAO) template have been demonstrated. Nanoporous TiO2 film was sprayed on the porous AAO templates, followed by Pd film deposited on TiO2 layer by DC magnetron sputtering. We have researched the detection performance of the hydrogen sensors depending on different thickness of TiO2 layer from 6 to 30 nm with keeping the thickness of Pd as 30 nm. The results have demonstrated the sensors with 10 nm thickness of TiO2 achieve the best performance with a response/recovery time as short as 4/8s at 0.8% and 0.4% hydrogen concentration, respectively. The sensors exhibited very good performance under hydrogen concentrations from 0.4% to 1.8%.

Bilevel optimization has been widely applied in many important machine learning applications such as hyperparameter optimization and meta-learning. Recently, several momentum-based algorithms have been proposed to solve bilevel optimization problems faster. However, those momentum-based algorithms do not achieve provably better computational complexity than $\mathcal{\widetilde O}(\epsilon^{-2})$ of the SGD-based algorithm. In this paper, we propose two new algorithms for bilevel optimization, where the first algorithm adopts momentum-based recursive iterations, and the second algorithm adopts recursive gradient estimations in nested loops to decrease the variance. We show that both algorithms achieve the complexity of $\mathcal{\widetilde O}(\epsilon^{-1.5})$, which outperforms all existing algorithms by the order of magnitude. Our experiments validate our theoretical results and demonstrate the superior empirical performance of our algorithms in hyperparameter applications., Comment: This paper is accepted in NeurIPS 2021

Synthetic cannabinoids, as exemplified by SDB-001 (1), bind to both CB1 and CB2 receptors and exert cannabimimetic effects similar to (-)-trans-Δ9-tetrahydrocannabinol, the main psychoactive component present in the cannabis plant. As CB1 receptor ligands were found to have severe adverse psychiatric effects, increased attention was turned to exploiting the potential therapeutic value of the CB2 receptor. In our efforts to discover novel and selective CB2 receptor agonists, 1 was selected as a starting point for hit molecule identification and a class of 1H-pyrazole-3-carboxamide derivatives were thus designed, synthesized, and biologically evaluated. Systematic structure-activity relationship investigations resulted in the identification of the most promising compound 66 as a selective CB2 receptor agonist with favorable pharmacokinetic profiles. Especially, 66 treatment significantly attenuated dermal inflammation and fibrosis in a bleomycin-induced mouse model of systemic sclerosis, supporting that CB2 receptor agonists might serve as potential therapeutics for treating systemic sclerosis.

Additional file 2: Figure 2. Immunohistochemistry staining and real-time PCR for the protein and mRNA expressions of IL-1β in the fracture haematoma/callus of sham and OVX mice.

Inspired by the formulation of quantum-electrodynamical time-dependent density functional theory (QED-TDDFT) by Rubio and coworkers, we propose an implementation that uses dimensionless amplitudes for describing the photonic contributions to QED-TDDFT electron-photon eigenstates. The leads to a symmetric QED-TDDFT coupling matrix, which is expected to facilitate the future development of analytic derivatives. Through a Gaussian atomic basis implementation of the QED-TDDFT method, we examined the effect of dipole self-energy, rotating wave approximation, and the Tamm-Dancoff approximation on the QED-TDDFT eigenstates of model compounds (ethene, formaldehyde, and benzaldehyde) in an optical cavity. We highlight, in the strong coupling regime, the role of higher-energy and off-resonance excited states with large transition dipole moments in the direction of the photonic field, which are automatically accounted for in our QED-TDDFT calculations and might substantially affect the energy and composition of polaritons associated with lower-energy electronic states., Comment: 23 pages, 6 figures and 5 tables

Additional file 1: Figure 1. Radiographic changes of the tibias in sham and OVX mice at different time points of fracture healing.

E. coli BL21 (DE3) is an excellent and widely used host for recombinant protein production. Many variant hosts were developed from from BL21 (DE3), but improving the expression of specific proteins remains a major challenge in biotechnology. In this study, we found that when BL21 (DE3) overexpressed glucose dehydrogenase (GDH), a significant industrial enzyme, serious autolysis was induced. Subsequently, we observed this phenomenon in the expression of 10 other recombinant proteins. This precludes a further increase of the produced enzyme activity by extending the fermentation time, which is not conducive to the reduction of industrial enzyme production costs. The membrane structure and mRNA expression analysis showed that cells suffered programmed cell death (PCD) during autolysis period. However, blocking three known PCD pathway in BL21 (DE3) cannot alleviate autolysis completely. Furthermore, we attempted to develop a strong expression host resistant to autolysis by controlling the speed of recombinant protein expression. To find a more suitable protein expression rate, the high- and low-strength promoter lacUV5 and lac were shuffled and recombined to yield the promoter variants lacUV5-1A and lac-1G. The results showed that only one base in lac promoter needs to be changed, and the A at the +1 position was changed to a G, resulting in a host of BL21 (DE3-lac1G), which successfully withstand the PCD of the host. The GDH activity at 43h was greatly increased from 37.5 U/mL to 452.0 U/mL. In scale-up fermentation, the new host was able to produce the model enzyme with a high rate of 89.55 U/mL/h at 43h, compared to the 3 U/mL/h of BL21 (DE3). Importantly, BL21 (DE3-lac1G) also successfully improved the production of other 10 enzymes. The engineered E. coli strain in the study conveniently optimizes recombinant protein overexpression by suppressing cell autolysis, and shows potential industrial applications.

The interfacial electronic structure of layer-by-layer 4-(dicyanomethylene)-2-t-butyl-6-(1,1,7,7-tetramethyljulolidyl-9-enyl)-4H-pyran (DCJTB)/fullerene C 60 was investigated using ultra-violet photoemission spectroscopy (UPS). Photoemission data of period of DCJTB/C 60 /DCJTB films suggested the formation of surface dipole and interfacial band bending across the interfaces, which greatly facilitates the charge transfer from DCJTB to C 60 and from C 60 to DCJTB layer as well. When applied this layer-by-layer structure to a near-infrared photodetector, a maximum of photocurrent was achieved by the device with 3 periods of DCJTB/C 60 thin films. Finally, the detailed work mechanism of this detector was discussed.

The rapid growth of the Internet of Things (IoT) is enhancing customers' experience with an ever-increasing mass of intelligent devices. However, the security doesn't get enough attention compared with novel concepts and features by manufactures which even not provide service of updates and patches. These issues could have been mitigated by the mechanism of the network access control. But traditional authentication mechanisms based on computational complexity of cryptographic protocols cannot be implemented on IoT devices with limited physical and computational resources. Therefore, we turn our attention to the device fingerprint which is like a biometric fingerprint that be able to provide identity authentication. The fingerprint proposed in this paper generated by network traffic features divided into the packet-feature and the IAT-feature. These features are extracted from the traffic when the device is booted, because at this time the device's network behavior is stable compared to other times. Experiments illustrate that the average identification accuracy of our device fingerprint reaches 92.4%

As mass devices access to network in the era of the Internet of Things (IoT), the network access control becomes more important. Traditional authentication mechanisms are no longer suitable for IoT devices with limited physical and computational resources because of their complexity. This problem can be solved by the device fingerprinting, which can identify devices only by their traffic characteristics. However, existing device fingerprinting technologies cannot accurately identify devices' type from the same manufacturer which equips these devices with similar hardwares and softwares. Therefore, we proposed the TSMC-SVM algorithm which introduced the cosine similarity into the SVM for improving the identification accuracy of these similar devices described above. Experiments illustrate that the average identification accuracy of the TSMC-SVM reaches 93.2%.

Transformer-based pre-trained language models have proven to be effective for learning contextualized language representation. However, current approaches only take advantage of the output of the encoder's final layer when fine-tuning the downstream tasks. We argue that only taking single layer's output restricts the power of pre-trained representation. Thus we deepen the representation learned by the model by fusing the hidden representation in terms of an explicit HIdden Representation Extractor (HIRE), which automatically absorbs the complementary representation with respect to the output from the final layer. Utilizing RoBERTa as the backbone encoder, our proposed improvement over the pre-trained models is shown effective on multiple natural language understanding tasks and help our model rival with the state-of-the-art models on the GLUE benchmark.

Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum. In this study, we establish the convergence of SGD to a global minimum for nonconvex optimization problems that are commonly encountered in neural network training. Our argument exploits the following two important properties: 1) the training loss can achieve zero value (approximately), which has been widely observed in deep learning; 2) SGD follows a star-convex path, which is verified by various experiments in this paper. In such a context, our analysis shows that SGD, although has long been considered as a randomized algorithm, converges in an intrinsically deterministic manner to a global minimum., Comment: ICLR2019

With the reform of electric market, more and more microgrids of different interest subjects access to the active distribution system (ADS). At the same time, the interest game between ADS and multi-microgrids (MGs) brings a huge challenge to power system the economic dispatching of power. In this paper, non-cooperative perfect information dynamic game theory was introduced, making microgrid connect into energy management of ADS to realize interaction between distribution network and microgrid. ADS’s power loss, node voltage and flow constraints were considered to ensure the safe operation of the system. Path-following interior point algorithm was employed to optimize the microgrid and ADS’s output. Simulation results show that the proposed method can not only balance the microgrid’s profit, but also reduce the power loss of ADS and the purchasing cost.

Aiming at the problem that the image defogging algorithm based on the dark channel prior will cause the edge halo effect in the restored image, an image defogging algorithm based on improved guided filtering is proposed. First, the calculation method of atmospheric light value based on the quadtree algorithm is adopted to obtain a more accurate atmospheric light value; then, the edge weight factor based on the Canny operator is added to the guided filtering algorithm to solve the local halo effect; finally, the image is restored based on the atmospheric physical model. Experiments show that this algorithm is better than other algorithms in subjective visual effects, and the image edge defogging is more natural; the comparison of objective performance indicators shows that the improved defogging algorithm has higher image quality and better image restoration.

Transformer temperature is an important indicator to detect the operating status of equipment. State evaluation of transformer temperature can analyze whether the equipment is in good operating condition. However, the internal structure of the transformer is complicated and collaborative failures are prone to occur. In addition, different ambient temperatures will also affect the temperature measurement of the equipment itself. Therefore, there are a large number of uncertain factors inside and outside the transformer, resulting in inaccurate temperature analysis. The article introduces the Pair-Copula function model to analyze the correlation of various variables in the transformer system, and combines the rank correlation coefficient to filter the variables, and finally constructs the correlation model of the transformer temperature, which provides an important analysis basis for subsequent state evaluation.

Based on the analysis of occurrence states and properties of a calcareous–siliceous phosphorite, the effects of GZ 1 collector dosage, regulator dosage, pulp density and particles size distribution of sample on concentrate indexes were investigated by reverse flotation technology. The rare earth oxidize (REO) grade and recovery of concentrate were also analyzed in the work. Two-stage reverse flotation technology was applied for the beneficiation of a calcareous–siliceous phosphorite. Under the integrated optimal single factor conditions of GZ 1 collector 0.9 kg/t, phosphate regulator 14 kg/t and pulp density 35 wt.%, mass fraction of particles size of − 74 μm of 85 wt.%, flotation time of 11 min and flotation pH of 5.5, the P 2 O 5 grade increases from 14.64 wt.% in raw ore to 26.82 wt.% in the concentrate, and the recovery is 72.09 wt.%, while REO grade increases from 0.08 wt.% in the raw ore to 0.175 wt.% in the concentrate, and the recovery is 86.08 wt.%.

Litter decomposition is an important ecosystem function, and is an indispensable process for carbon and nutrient cycling. Litter decomposition is influenced by many factors, especially soil microbial communities, which are subject to their corresponding plant communities. It is necessary to study changes in soil microbial communities during litter decomposition and how these changes respond to plant diversity in corresponding forest communities to understand the mechanisms of decomposition. In the present study, we investigated these changes and responses using a litter bag method. Our results showed: (1) After 183 days of litter bag placement, microbial diversity decreased, and there was a negative correlation between plant species diversity and microbial diversity; (2) Soil microbial communities varied before and after the placement of plant litter bags. In particular the PLFAs of fungi and bacteria after the placement of litter bags were much higher than those before the placement of litter bags; (3) Topography was the most important factor correlated with the soil microbial community, and explained 29.55% of the variation of microbial communities. In contrast, litter quality and plant diversity explained 15.39% and 8.45%, respectively, and the interaction of the three factors explained 2.97% of the variation of the microbial communities. In conclusion, we find plant diversity plays a less important role than topography in determining soil microbial diversity, and litter quality influences soil microbial communities during litter decomposition.

In this paper, an improved ORB feature extraction algorithm is proposed to solve the insufficient detection effect of the traditional ORB algorithm on the image edges as well as the low actual extraction of feature points. First, the image pyramid layering was established, and then on basis of the improved oFAST12-20 corner point algorithm, the method with a setting dynamic threshold, according to the idea of adaptive threshold, was proposed to extract feature points, and the candidate set of feature points was obtained. Then, Harris algorithm was used to calculate the response value of each candidate point, and the response value was sorted from high to low. Finally, the top 80% of the feature points with high response value were extracted as the most important feature points. The experimental results show that the improved oFAST algorithm improved the extraction rate of feature points and reduces the algorithm time compared with the traditional ORB corner detection.

Background Deep vein thrombosis (DVT) is caused by blood clotting in the deep veins. Thrombosis resolution and recanalization can be accelerated by endothelial progenitor cells. In this report, we investigated the effects of miR-126-loaded EPC-derived exosomes (miR-126-Exo) on EPCs function and venous thrombus resolution. Methods In vitro promotional effect of miR-126-Exo on the migration and tube incorporation ability of EPCs was investigated via transwell assay and tube formation assay. In addition, a mouse venous thrombosis model was constructed and treated with miR-126-Exo to clarify the therapeutic effect of miR-126-Exo by histological analysis. Lastly, this study predicted a target gene of miR-126 using target prediction algorithms and confirmed it by luciferase activity assay, RT-qPCR, and Western blot. Results Transwell assay and tube formation assay indicated that miR-126-Exo could enhance the migration and tube incorporation ability of EPCs. Moreover, in vivo study manifested enhanced thrombus organization and recanalization after miR-126-Exo treatment. Meanwhile, we identified that Protocadherin 7 as a target gene of miR-126. Conclusions To sum up, our results demonstrated that EPC-derived exosomes loaded with miR-126 significantly promoted thrombus resolution in an animal model of venous thrombosis, indicating exosomes as a promising potential vehicle carrying therapeutic molecules for DVT therapy.

The influences of the different reactivity cokes on softening-melting dropping of iron-bearing burden were studied by melting-dropping experiment. The pressure difference and shrinkage rate of burden layer, as well as the burden reduction degree at the same temperature were also analyzed. The results show that the softening and melting starting temperature reduces from 1200 to 1160 °C, and the softening and melting end temperature increases from 1290 to 1320 °C, while the melting ending temperature is unchanged and the dropping temperature of molten iron reduces from 1413 to 1380 °C, when the coke reactivity increases from 24.76 to 65.16%. Softening-melting temperature interval increases with the improvement of coke reactivity based on the above temperatures change, meanwhile, melting temperature interval and the dropping temperature interval (ΔT3 = Td − TP) reduces, respectively. In addition, the shrinkage rate of charge column decreases and the permeability of charge column get better when the coke reactivity increases from 24.76 to 65.16%. The softening-melting and dropping characteristic evaluation of iron-bearing burden reduces with the improvement of coke reactivity. Appropriately increasing coke reactivity may improve charge column permeability and increase softening-melting and dropping characteristic of iron-bearing burden, therefore, it is of great significance for saving energy and reducing consumption of blast furnace smelting.

The test researched the influence of layered coke charging, sandwich clamping coke charging and central coke charging with edge delaminating on the development of carbon solution reaction in shaft and the reduction of the iron-bearing burden on the condition of the same proportion of high and low reaction coke. It showed that the reduction degree of the layered type coke distribution is the best, and the loss of carbon solution is the minimum using the same proportion of high reaction coke and low reaction coke. Compared with the other two type coke distribution, the layered type coke distribution is promising to increase reaction efficiency in blast furnaces and decrease the energy consumption of blast furnace melting.

Recent advances in the field of ultra wide band (UWB) localization system has brought renewed interest to the problem of indoor localization system, which has a wide range of applications, such as localization in mine, commodity localization in market or factory and other fields. However, in real scenario, the time synchronization and the limited located users problems are widely existed. An indoor localization system based on UWB is proposed in this paper, where a pseudo time synchronization algorithm is proposed to solve time synchronization problem. Moreover, a signal communication mechanism is established, by which located users only receive signals transmitted from reference base stations. In this case, it can locate unlimited number of users simultaneously in theory. The system has high localization accuracy and stability, and achieves the indoor 3D localization, which can be seen from the indoor static localization and the indoor dynamic localization experiments, respectively.

To investigate the accuracy of 128-slice dual-source CT using high-pitch spiral mode (HPS) for the assessment of coronary stents.We conducted a prospective study on patients with previous stent implantation due to recurred suspicious symptoms of angina with positive findings at stress testing scheduled for coronary angiography (CA), while dual source computed tomography (DSCT) examinations were randomly done by one of the three different scan modes [HPS, sequential mode (SEQ), low-pitch spiral mode (LPS)] one week before CA examinations. The image quality, radiation dose and stent patency of DSCT were evaluated blinded to the results of CA.180 patients with total 256 stents were enrolled in this study. There was no significant difference on the image quality of DSCT by HPS (1.4±0.5), SEQ (1.5±0.5) and LPS (1.3±0.6) (P0.05). The noise of images reconstructed with B26f kernel in HPS is significantly increased than in SEQ/LPS (P0.05), while no significant difference with images reconstructed with B46f kernel (P0.05). Heart rate (HR) variability had a slight impact on the image quality for HPS (P0.05), not for LPS/SEQ (P0.05). In the assessment of stent restenosis compared with CA on per-stent basis, there was no significant difference on sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DSCT using HPS (100%, 97.1%, 83.3%, 100%), LPS (92.3%, 95.9%, 80%, 98.6%) and SEQ (93.3%, 97.3%, 87.5%, 98.6%) (P0.05). The effective dose of DSCT by HPS (1.0±0.5mSv) is significant less than that by SEQ (3.0±1.4mSv) or LPS (13.0±5.4mSv) (P0.01).DSCT using HPS provides good diagnostic accuracy on coronary stent patency compared with CA, similar to that by SEQ/LPS, whereas with lower effective dose in patients with HR lower than 65bpm.

The backward stimulated scattering (BSS) from ZnO nanospheres dispersed in water has been investigated experimentally by employing a Nd:YAG pulse laser with ~532 nm wavelength and ~8 ns pulse width as the pump laser source. The present results show that the BSS effect is uniquely and unequivocally different compared to other known stimulated scattering, such as stimulated Rayleigh scattering, stimulated Brillouin scattering, and stimulated Raman scattering, and it displays the characteristics of no frequency shift and threshold dependence on initial spontaneous Mie scattering seed source. These can be understood by means of the Mie scattering theory and a laser-induced stationary Bragg grating model.

The significant development of fabricated steel structure is helpful to solve the issue of excessive productivity in steel industry and promote the greenization, informatization and industrialization in construction industry. This paper takes a 220kV indoor substation for example to compare the stress ratio, displacement, types of joints and steel quantity of the steel pipe column frame with those of the mixed one. The result demonstrates that the employment of steel pipe columns for indoor substation structures can generate certain economic and social benefits.

Conclusion: Scopolamine, a tropane alkaloid drug that mainly acts as an antagonist of muscarinic acetylcholine receptors, was found to reduce the local field potentials (LFP) of auditory cortex (AC) evoked by tone and gap-offsets whose effects may compensate the cortical hyperexcitability related to tinnitus. Objective: To study the effects of scopolamine on the AC and the inferior colliculus (IC) of awake rats in order to understand scopolamine’s effect on tinnitus and gap detection. Method: Silent gaps (duration varied from 2–100 ms) embedded in otherwise continuous noise were used to elicit AC and IC response. Gap evoked AC and IC field potentials were recorded from awake rats before and after treatment of scopolamine (3 mg/kg, i.m.). Results: Acute injection of scopolamine (3 mg/kg, i.m.) induced a significant reduction of the AC response, but not the IC response, to the offset of the gaps embedded in white noise. The results suggest that scopolamine may reduce AC neural synchrony.

We propose a novel metal–insulator–metal (MIM) waveguide structure consisting of a stub and a double side-coupled nanodisk. Its electromagnetically-induced transparency (EIT)-like response and transmission properties are numerically investigated by the finite element method (FEM). Numerical simulation results show that the EIT-like phenomenon appears in the transmission spectra. The transparency window can not only be manipulated by changing the radius of the nanodisk and the height of the stub, but also appear redshifted with the increasing of the refractive index n. Furthermore, the MIM waveguide structure is easy to yield a high sensitivity of 1200 nm/RIU with a figure of merit about [Formula: see text], which may be applied to nanosensors. These results would help us design the high-performance plasmonic devices based on the EIT-like response.

Common mathematical theory can have profound applications in understanding real materials. The intrinsic connection between aperiodic orders observed in the Fibonacci sequence, Penrose tiling, and quasicrystals is a well-known example. Another example is the self-similarity in fractals and dendrites. From transmission electron microscopy experiments, we found that FexTaS2 crystals with x=1/4 and 1/3 exhibit complicated antiphase and chiral domain structures related to ordering of intercalated Fe ions with 2a*2a and sqrt3a*sqrt3a superstructures, respectively. These complex domain patterns are found to be deeply related with the four color theorem, stating that four colors are sufficient to identify the countries on a planar map with proper coloring, and its variations for two-step proper coloring. Furthermore, the domain topology is closely relevant to their magnetic properties. Our discovery unveils the importance of understanding the global topology of domain configurations in functional materials.