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The existing deep learning compilers are unable to perform efficient hardware performance-related graph fusion when both time and power consumption are considered. The operator optimization cost is too high because of excessive fusion or skipping fusion. In addition, the compilers optimize the computational graph of Deep Neural Networks (DNN) by performing static graph transformation based on the greedy algorithm, only considering the runtime performance and ignoring the cost of the tuning process. To solve these problems, this paper proposes PCGC, a DNN computational graph optimization compiler. Through the performance feedback at runtime, PCGC designs a computational graph fusion and splitting optimization strategy based on multilevel operator layer fusion-splitting rules. First, PCGC uses a rule-guided graph segmentation algorithm to recursively segment the computational graph into smaller subgraph to achieve an efficient and detailed search. Then, PCGC uses the cost model to receive the feedback of hardware performance information, uses the cost model and operator fusion rules to guide the partial fusion and split of the nodes and edges of the computational graph, and flexibly generates the optimal subgraph according to different hardware. Finally, in the fusion process, the operator computing attributes are considered. The graph-level node and operator-level cyclic fusion are closely combined to optimize the search space of partial fusion. Compared with other advanced accelerators, PCGC optimizes the overall power consumption on an embedded GPU by an average of 130.5% when the time consumption on each hardware is not lower than the average time consumption. On Domain Specific Architecture (DSA), PCGC optimizes power consumption by an average of 66.5%. On FPGA, PCGC optimizes power consumption by 66.1%. In a sense, PCGC can achieve high-speed inference in specific power supply scenarios, reducing the carbon emissions of edge computing.

A novel colorimetric aptasensor has been developed for highly sensitive tetracycline (TC) detection based on the peroxidase-like activity of Fe

Urea concentration and pH are two crucial parameters in food and clinical analysis. Traditional analytical methods of urea and pH determination are unsuitable for field and involve complex instrumentation or and enzyme-based assays. In this study, we used screen printing technology to prepare an electrochemical sensor with a carbon electrode modified by a multiwalled carbon nanotube/polyaniline (MWCNT/PANi) composite for the simultaneous detection of urea and pH. Urea was detected by a simple current–potential (I–V) experiment and its concentration level on the MWCNT/PANi-modified screen-printed carbon electrode (SPCE) surface was determined by cyclic voltammetry. The MWCNT/PANi-modified SPCE had a linear response (R2 = 0.99902), lower detection limit, higher selectivity, and higher sensitivity than reported biosensors. Specifically, the detection limit was 10 µM and the sensitivity was 0.38 mA mM−1 cm−2 in the urea concentration range of 10–50 µM. Chronoamperometry was applied to investigate the changes in voltage on the MWCNT/PANi-modified SPCE with varying solution pH. The sensor exhibited excellent linearity (R2 = 0.99089) and an average sensitivity of 20.63 mV/pH over a wide pH range of 2–11. Thus, the MWCNT/PANi-modified SPCE has a promising field application as a simple, bifunctional non-enzymatic sensor.

Whole genome of Pyemotes zhonghuajia using PacBio Sequencing

Background Dyslipidemia is one of the mechanisms of atherosclerosis (AS). Depletion of estrogen plays a key role in the pathogenesis of postmenopausal AS in women, and the blood lipid levels of women are closely related to endogenous estrogen levels. Phytoestrogens (PEs) exert estrogenic effects, including protection against AS, without the adverse effects of estrogen administration. Bazi Bushen capsule (BZBS) is a traditional Chinese medicine herbal compound prescription that has been shown to contain 11 unique PEs. In the present study, we assessed the effects of BZBS against lipid metabolism disorders. Methods All ApoE-/- mice underwent ovary ligation and bilateral ovariectomy (Ovx) to induce surgical menopause (Ovx/ApoE-/- mice), whereas the C57BL/6J mice underwent sham surgery (needle threading). Ovx/ApoE-/- mice were given a high-fat diet without estrogen and C57BL/6J mice were given a normal diet for 12 weeks. Ovx/ApoE-/- mice treated with G1, a highly selective G-protein-coupled estrogen receptor1 (GPER1) agonist with proven activity against AS, were used as positive controls. Estrogen levels were measured and uterine atrophy index was calculated to determine the success of the model. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured in each group. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) model was used to separate the groups, MetaboAnalyst was then used to analyze the metabolic pathway, and the most representative metabolites were finally identified. Results Removal of bilateral ovaries resulted in successful surgical menopause models, where BZBS increased estrogen levels but did not increase the risk of uterine proliferation. BZBS attenuated dyslipidemia, including decreased TG, TC, and LDL-C levels, but increased HDL-C levels. The OPLS-DA model successfully distinguished the groups with good predictive ability and revealed their tendency to separate from each other. MetaboAnalyst suggested that both the G1 group and high-dose BZBS (HD-BZ) could improve the effect of lipid metabolism: the glycerophospholipid metabolism pathway was mainly improved by the G1 group, while the inositol phosphate metabolism pathway was mainly improved by the HD-BZ group. For the four compounds with the highest content, the concentrations of docosahexaenoic acid (DHA), 3-hydroxybutyric acid, and 5(Z), 8(Z), 11(Z)-eicosatrienoic acid were dramatically lower in the model group compared to the control group. Lysophosphatidylethanolamine (18:0) was higher in the model group than in the control group. BZBS corrected these effects. Conclusions BZBS treatment reduced serum lipid levels and improved fatty acid metabolism in high-fat diet-fed, surgically induced menopausal ApoE-/- mice.

This paper aims to design a fast convergent distributed cooperative learning (DCL) algorithm for feedforward neural networks with random weights (FNNRWs) over undirected and connected networks. First, a continuous-time fast convergent DCL algorithm is proposed, whose finite-time convergence is guaranteed based on the Lyapunov method. Second, we extend this algorithm to a discrete-time form by using the fourth-order Runge–Kutta method. Compared with the distributed alternating direction method of multipliers (ADMM) and the Zero-Gradient-Sum-based (ZGS-based) algorithms, the proposed algorithm has high learning capability and convergence speed. Simulation results demonstrate that the proposed algorithm has fast convergence rate, and the convergence rate may be adjusted by properly selecting some tuning parameters.

This study presents an inequality which can be used to analyse the stability of fractional order systems by constructing Lyapunov functions. By using the presented inequality, it is shown that the fractional order system is Mittag-Leffler stable if there is a convex and positive definite function such that its fractional order derivative is negative definite. This result generalises the existing works and gives a useful method to construct the Lyapunov function for the stability analysis of the fractional order systems. Finally, the authors illustrate the advantages of the proposed method by two examples and their numerical simulation.

A highly efficient preparation method for cypress-like copper oxide (CuO) ink used in quick modification of screen-printed electrodes (SPE) is developed; the ink is used for the rapid detection of nitrite (NO2–). The cypress-like CuO nanomaterial is mixed with multi-walled carbon nanotubes (MWCNTs) to form an electronic ink and then evenly printed over SPE. The modified electrodes are used for enzyme-free detection of NO2–. The structure, morphology and composition and surface oxidation states of CuO is determined from X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). The electrochemical performance of sensors utilizing the modified SPE is evaluated by cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). Morphology of the prepared CuO is similar to that of cypress leaves, with a length of about 50 nm, and numerous cypress leaves with a length of 10 nm are vertically arranged on the surface. The electrode current response showed a linear dependence on the nitrite concentration and is in the range of 0.1–6500 μM. An electrode sensitivity of 501 μA·mM−1·cm−2 and a detection threshold of 0.039 μM with a signal to noise ratio of 3 is observed. Therefore, CuO-MWCNTs modification of nitrite sensor electrodes enhances sensing selectivity, stability, and reliability.

In this paper, an oxygen plasma-treated ZnO nanoflower-based screen-printed electrode (SPE) is proposed as an electrochemical sensor for the simultaneous detection of dopamine (DA), and diclofenac sodium (DS). Scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy were applied to characterize the morphology and structure of the nanomaterials. The ZnO nanoflowers with a large specific surface area exhibited good electrochemical performance, and they were conducive to ion exchange. The electrochemical behaviors of DA, and DS were evaluated by cyclic voltammetry in the potential range of 0.3–1 V; the linear response ranges were 0.1–300, and 0.01–40 μM, and the sensitivities were 0.26, and 0.37 μA·μM−1 cm−2, respectively, with detection limits of 0.28, and 0.11 μM. The selective detection of DA, and DS was evaluated in the presence of other compounds. The results revealed that the oxygen plasma-treated ZnO/SPE, as an electrochemical sensor, could simultaneously detect these substances with good stability and low detection limit. The proposed method was successfully applied using urine samples, and has a good recovery rate. In the future, it may serve as a new detection technology for drug detection and health monitoring.

In this study, a finite-time convergent distributed continuous-time algorithm is proposed to solve a network optimisation problem where the global cost function is the sum of strictly convex local cost functions under an undirected network with fixed topologies. The algorithm is inspired by finite-time consensus protocols and continuous-time zero-gradient-sum algorithms. Instead of the exponential convergence in existing works, the finite-time convergence is guaranteed based on the Lyapunov method. A numerical simulation example is provided to illustrate the effectiveness of the developed algorithm.

Facing multiple collecting channels for used products, consumers may choose the online collection service provided by third-party platforms for convenience, which also brings competition to other channels. To explore the influence of consumer behavior on the competitive dual-collecting supply chain, we build two closed-loop supply chain models with dual-collecting channels: one in which the used products are collected by the retailer and the third-party (model RT) and another in which the used products are collected by the manufacturer and the third-party (model MT). Results show that, with increasing consumer preference to the third-party collecting channel in model RT, the manufacturer’s optimal decisions maintained, while the optimal collecting prices of the retailer and the third-party decrease. In model MT, the manufacturer’s optimal decisions are not affected by consumer preference, but the third-party’s collecting price decreases. In addition, the numerical analysis shows that in both models, the total collecting quantity and the overall profit decrease. Accordingly, we use revenue sharing contracts and two-part tariff contracts to coordinate the reverse channels. We find that the latter one can coordinate both model RT and model MT, while the former one cannot, and besides, the influence of consumer preference on contract parameters is studied.

In the original version of this article, Fig. 7a, b and c was unfortunately displayed incorrectly.

This paper aims to solve the distributed cooperative learning (DCL) problem over networks, such as data approximation, where each node only has access to local information which is produced by the same unknown pattern (map or function). Different from the traditional centralized learning (CL) scheme, DCL scheme needs all nodes in the network cooperatively learn the unknown pattern by exchanging its own learned information with their neighbors. In order to share learned information of each node, a novel finite-time convergent DCL algorithm via High-Order Neural Networks (HONN) over undirected network with fixed topologies is developed. The numerical experiment and rigorous theoretical analysis show that not only the proposed algorithm owns high learning ability, but also owns high rate of convergence.

A substantial amount of research has focused on the persistence or availability of Web citations. The present study analyzes Web citation distributions. Web citations are defined as the mentions of the URLs of Web pages (Web resources) as references in academic papers. The present paper primarily focuses on the analysis of the URLs of Web citations and uses three sets of data, namely, Set 1 from the Humanities and Social Science Index in China (CSSCI, 1998-2009), Set 2 from the publications of two international computer science societies, Communications of the ACM and IEEE Computer (1995-1999), and Set 3 from the medical science database, MEDLINE, of the National Library of Medicine (1994-2006). Web citation distributions are investigated based on Web site types, Web page types, URL frequencies, URL depths, URL lengths, and year of article publication. Results show significant differences in the Web citation distributions among the three data sets. However, when the URLs of Web citations with the same hostnames are aggregated, the distributions in the three data sets are consistent with the power law (the Lotka function).

To discuss the clinical significance of fluid management of severe patients according to arterial pressure-based cardiac output (APCO) monitoring volume responsiveness index.A retrospective cohort study was conducted. The severe patients were selected from the intensive care unit (ICU) of the First Hospital of Jilin University from June 1st, 2012 to December 31st, 2013. The hemodynamic parameters were monitored by APCO, and the fluid resuscitation was managed by stroke volume variation (SVV) and passive leg-raising test (PLR) when the acute physiology and chronic health evaluation II (APACHEII) score ≥ 15, heart rate100 bpm with the result that the preload and heart function could not be evaluated. The heart rate, SVV, lactic acid (Lac) and central venous pressure (CVP) and curative effect were recorded before and after carrying out fluid management strategy. The criteria of clinical effective was defined as heart rate decreased and (or) stroke volume (SV) increased ≥ 10%, accompanied by blood Lac and SVV decreased, other than, the cases did not meet above criteria were considered ineffective.Sixty-eight patients were enrolled in the study. (1) Before carrying out fluid management strategy: 40 cases with CVP12 cmH₂O (1 cmH₂O=0.098 kPa), and 16 cases with 5-12 cmH₂O, 12 with5 cmH₂O. SVV13% in 35 cases, SVV13% in 9 cases. PLR positive in 18 cases, and PLR negative in 6 cases. It was implicated that the patients with poor preload (SVV13% and PLR positive) accounted by 77.9% (53/68). (2) There were 49 effective cases and 19 ineffective cases 4 hours after carrying out fluid management strategy, and the effective rate was 72.06% (49/68). While there were 56 effective cases and 12 ineffective cases after 12 hours, and the total effective rate was 82.35% (56/68). (3) In effective group, heart rate, SVV, Lac after fluid management strategy were significantly lower than those before fluid management strategy [4 hours after fluid management strategy: heart rate (bpm) 112.45 ± 13.53 vs. 129.55 ± 15.49, SVV (15.47 ± 6.32)% vs. (21.20 ± 7.40)%, Lac (mmol/L) 4.16 ± 3.12 vs. 6.21 ± 4.11; 12 hours after fluid management strategy: heart rate (bpm) 110.02 ± 13.92 vs. 129.61 ± 14.93, SVV (14.61 ± 15.52)% vs. (20.66 ± 7.40)%, Lac (mmol/L) 3.35 ± 2.26 vs. 6.11 ± 4.02, P0.05 or P0.01], while there was no significant difference in those markers between before and after fluid management strategy in ineffective group [4 hours after fluid management strategy: heart rate (bpm) 119.53 ± 11.68 vs. 125.79 ± 11.58, SVV (16.95 ± 6.48)% vs. (18.47 ± 4.96)%, Lac (mmol/L) 5.55 ± 3.80 vs. 6.54 ± 3.72; 12 hours after fluid management strategy: heart rate (bpm) 115.92 ± 11.71 vs. 123.40 ± 11.59, SVV (17.17 ± 6.09)% vs. (19.42 ± 8.25)%, Lac (mmol/L) 6.33 ± 3.40 vs. 7.21 ± 3.81, all P0.05]. CVP only at 12 hours after fluid management strategy in effective group was significantly higher than that before fluid management strategy (cmH₂O: 12.8 8 ± 3.38 vs. 11.27 ± 4.97, P0.05).SVV monitored by APCO is a good indicator of volume responsiveness index, which can be used as an important reference combined with PLR for fluid management of severe patients.

This paper reviews studies concerning complementary assets and enterprise niche and discusses the interaction between them. Expounding on the background of the development of electric vehicle (EV) ...

EVs (electric vehicle), as sustainable technologies, hold the potential to achieve the sustainability of the transport system and challenge the prevailing business models of internal combustion engines (ICEs). To unlock the dominant logic of ICEs and promote the diffusion of EVs, Business model (BM) innovation is necessary. However, BM innovation for EVs still faces many obstacles. This paper makes contribution theoretically and practically by constructing a “3-7” system and proposing a two-phase conceptual framework for BM innovation of EVs. The case of Shenzhen City, China is studied to illustrate how the “3-7” system and two-phase conceptual framework is applied. Based on the case study, this paper concludes two new barriers that are not highlighted by the previous literature and makes a number of suggestions to help address the issues found in BM innovation.