Energy Efficiency Evaluation of Power Equipment Based on DEA

As the current situation of imperfect model algorithm of electrical equipment for energy efficiency evaluation, we set up energy efficiency DEA model. In the model, we got average load rate, average power factor and tri-phase unbalance factor as inputs indexes. And we got economic output of per unit of power consumption and energy pollution as outputs indexes. Then we transformed multiple pollutants into a pollutant index through principal component analysis. And we got it as the desired output. The result of 12 industrial enterprises in the energy efficiency of electrical equipment shows that the evaluation model is suitable for energy efficiency evaluation and system analysis. The DEA model is useful to further improvement in the energy efficiency of equipment

Domain size and charge defects on the polarization switching of antiferroelectric domains

The switching behavior of antiferroelectric domain structures under the applied electric field is not fully understood. In this work, by using the phase field simulation, we have studied the polarization switching property of antiferroelectric domains. Our results indicate that the ferroelectric domains nucleate preferably at the boundaries of the antiferroelectric domains, and antiferroelectrics with larger initial domain sizes possess a higher coercive electric field as demonstrated by hysteresis loops. Moreover, we introduced charge defects into the sample and numerically investigated their influence. It is also shown that charge defects can induce local ferroelectric domains, which could suppress the saturation polarization and narrow the enclosed area of the hysteresis loop. Our results give insights into understanding antiferroelectric phase transformation and optimizing the energy storage property in experiments

Acute morphine activates satellite glial cells and up-regulates IL-1β in dorsal root ganglia in mice via matrix metalloprotease-9

<p>Abstract</p> <p>Background</p> <p>Activation of spinal cord glial cells such as microglia and astrocytes has been shown to regulate chronic opioid-induced antinociceptive tolerance and hyperalgesia, due to spinal up-regulation of the proinflammatory cytokines such as interleukin-1 beta (IL-1β). Matrix metalloprotease-9 (MMP-9) has been implicated in IL-1β activation in neuropathic pain. However, it is unclear whether acute opioid treatment can activate glial cells in the peripheral nervous system. We examined acute morphine-induced activation of satellite glial cells (SGCs) and up-regulation of IL-1β in dorsal root ganglia (DRGs), and further investigated the involvement of MMP-9 in these opioid-induced peripheral changes.</p> <p>Results</p> <p>Subcutaneous morphine injection (10 mg/kg) induced robust peripheral glial responses, as evidenced by increased GFAP expression in DRGs but not in spinal cords. The acute morphine-induced GFAP expression is transient, peaking at 2 h and declining after 3 h. Acute morphine treatment also increased IL-1β immunoreactivity in SGCs and IL-1β activation in DRGs. MMP-9 and GFAP are expressed in DRG neurons and SGCs, respectively. Confocal analysis revealed a close proximity of MMP-9 and GFAP immunostaining. Importantly, morphine-induced DRG up-regulation of GFAP expression and IL-1β activation was abolished after <it>Mmp9 </it>deletion or naloxone pre-treatment. Finally, intrathecal injections of IL-1β-selective siRNA not only reduced DRG IL-1β expression but also prolonged acute morphine-induced analgesia.</p> <p>Conclusions</p> <p>Acute morphine induces opioid receptors- and MMP-9-dependent up-regulation of GFAP expression and IL-1β activation in SGCs of DRGs. MMP-9 could mask and shorten morphine analgesia via peripheral neuron-glial interactions. Targeting peripheral glial activation might prolong acute opioid analgesia.</p

MicroRNA-148b is frequently down-regulated in gastric cancer and acts as a tumor suppressor by inhibiting cell proliferation

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are involved in cancer development and progression, acting as tumor suppressors or oncogenes. Our previous studies have revealed that miR-148a and miR-152 are significantly down-regulated in gastrointestinal cancers. Interestingly, miR-148b has the same "seed sequences" as miR-148a and miR-152. Although aberrant expression of miR-148b has been observed in several types of cancer, its pathophysiologic role and relevance to tumorigenesis are still largely unknown. The purpose of this study was to elucidate the molecular mechanisms by which miR-148b acts as a tumor suppressor in gastric cancer.</p> <p>Results</p> <p>We showed significant down-regulation of miR-148b in 106 gastric cancer tissues and four gastric cancer cell lines, compared with their non-tumor counterparts by real-time RT-PCR. <it>In situ </it>hybridization of ten cases confirmed an overt decrease in the level of miR-148b in gastric cancer tissues. Moreover, the expression of miR-148b was demonstrated to be associated with tumor size (P = 0.027) by a Mann-Whitney U test. We also found that miR-148b could inhibit cell proliferation <it>in vitro </it>by MTT assay, growth curves and an anchorage-independent growth assay in MGC-803, SGC-7901, BGC-823 and AGS cells. An experiment in nude mice revealed that miR-148b could suppress tumorigenicity <it>in vivo</it>. Using a luciferase activity assay and western blot, CCKBR was identified as a target of miR-148b in cells. Moreover, an obvious inverse correlation was observed between the expression of CCKBR protein and miR-148b in 49 pairs of tissues (P = 0.002, Spearman's correlation).</p> <p>Conclusions</p> <p>These findings provide important evidence that miR-148b targets CCKBR and is significant in suppressing gastric cancer cell growth. Maybe miR-148b would become a potential biomarker and therapeutic target against gastric cancer.</p

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with

Imprint of the stochastic nature of photon emission by electrons on the proton energy spectra in the laser-plasma interaction

The impact of stochasticity effects (SEs) in photon emissions on the proton energy spectra during laser-plasma interaction is theoretically investigated in the quantum radiation-dominated regime, which may facilitate SEs experimental observation. We calculate the photon emissions quantum mechanically and the plasma dynamics semiclassically via two-dimensional particle-in-cell simulations. An ultrarelativistic plasma generated and driven by an ultraintense laser pulse head-on collides with another strong laser pulse, which decelerates the electrons due to radiation-reaction effect and results in a significant compression of the proton energy spectra because of the charge separation force. In the considered regime the SEs are demonstrated in the shift of the mean energy of the protons up to hundreds of MeV. This effect is robust with respect to the laser and target parameters and measurable in soon available strong laser facilities

Non-coding RNAs participate in the regulatory network of CLDN4 via ceRNA mediated miRNA evasion

AbstractThousands of genes have been well demonstrated to play important roles in cancer progression. As genes do not function in isolation, they can be grouped into “networks” based on their interactions. In this study, we discover a network regulating Claudin-4 in gastric cancer. We observe that Claudin-4 is up-regulated in gastric cancer and is associated with poor prognosis. Claudin-4 reinforce proliferation, invasion, and EMT in AGS, HGC-27, and SGC-7901 cells, which could be reversed by miR-596 and miR-3620-3p. In addition, lncRNA-KRTAP5-AS1 and lncRNA-TUBB2A could act as competing endogenous RNAs to affect the function of Claudin-4. Our results suggest that non-coding RNAs play important roles in the regulatory network of Claudin-4. As such, non-coding RNAs should be considered as potential biomarkers and therapeutic targets against gastric cancer.</jats:p