This paper proposes a method to estimate the real-time regulation reserve requirement based on the NERC Control Performance Standard (CPS). This method is constructed via three steps: first, a ...Multiple Linear Regression (MLR) model is applied to abstract the relationship between CPS and regulation reserve and other system conditions using training observations generated from a load frequency control model; second, a stepwise method with cross validation is used to select the most relevant features of MLR; and third, the regulation reserve requirement is computed by the MLR model as a function of the predicted system conditions and target CPS score. The recursive least square (RLS) method is used to update the model parameters in an online environment. Testing on a single area automatic generation control model with load and wind data from CAISO 33% Renewable Portfolio Standard scenario indicates the method outperforms methods used in the industry today.
Source counting is the key procedure of autonomous detection for underwater unmanned platforms. A source counting method with local-confidence-level-enhanced density clustering using a single ...acoustic vector sensor (AVS) is proposed in this paper. The short-time Fourier transforms (STFT) of the sound pressure and vibration velocity measured by the AVS are first calculated, and a data set is established with the direction of arrivals (DOAs) estimated from all of the time–frequency points. Then, the density clustering algorithm is used to classify the DOAs in the data set, with which the number of the clusters and the cluster centers are obtained as the source number and the DOA estimations, respectively. In particular, the local confidence level is adopted to weigh the density of each DOA data point to highlight samples with the dominant sources and downplay those without, so that the differences in densities for the cluster centers and sidelobes are increased. Therefore, the performance of the density clustering algorithm is improved, leading to an improved source counting accuracy. Experimental results reveal that the enhanced source counting method achieves a better source counting performance than that of basic density clustering.
The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish ...between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.
Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected ...by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data.
The liver plays a central role in metabolism. Although many studies have described
liver models for drug discovery, to date, no model has been described that can stably maintain liver function. Here, ...we used a unique, scaffold-free 3D bio-printing technology to construct a small portion of liver tissue that could stably maintain drug, glucose, and lipid metabolism, in addition to bile acid secretion. This bio-printed normal human liver tissue maintained expression of several kinds of hepatic drug transporters and metabolic enzymes that functioned for several weeks. The bio-printed liver tissue displayed glucose production
cAMP/protein kinase A signaling, which could be suppressed with insulin. Bile acid secretion was also observed from the printed liver tissue, and it accumulated in the culture medium over time. We observed both bile duct and sinusoid-like structures in the bio-printed liver tissue, which suggested that bile acid secretion occurred
a sinusoid-hepatocyte-bile duct route. These results demonstrated that our bio-printed liver tissue was unique, because it exerted diverse liver metabolic functions for several weeks. In future, we expect our bio-printed liver tissue to be applied to developing new models that can be used to improve preclinical predictions of long-term toxicity in humans, generate novel targets for metabolic liver disease, and evaluate biliary excretion in drug development.
Transcatheter arterial chemoembolization (TACE) is usually considered more efficacious in the local treatment of parenchyma-sparing hepatocellular carcinoma (HCC). At present, embolic agents commonly ...used in TACE, include DC pellets, Hepasphere, Lipiodol, etc. Except that iodine oil is a viscous fluid embolic agent, other solid microsphere particles used clinically range from 70 to 700 µm, among which 100 to 300 µm is the most commonly used. With the technology development of micro-invasive interventional therapy, the specific distal embolization through TACE to occlude tumor arterial blood supply in patients with HCC is also required more accurately. Effective terminal embolization is considered to be a preferred option for TACE therapy due to significantly improving the survival rate of patients and preserving liver function. In this article, we prepared the multifunctional multivesicular liposomes (IVO-DOX-MVLs) (<100 µm) that can simultaneously encapsulate ioversol and doxorubicin based on the high-phase transition temperature (T
m
) lipid ingredients, and evaluated its local artery embolization and therapeutic effect in rabbit VX-2 tumor model. The influence of particle size on occlusion and therapeutic effect of MVLs on rabbit VX-2 liver tumor models were well evaluated, including the tumor volume change, tumor growth rate, and necrosis rate, which were evaluated by magnetic resonance (MR). MVL samples with average particle size distribution of 50-60 µm exhibited fewer off-target embolization. Through TACE, IVO-DOX-MVLs were directly transported to the tumor tissues, playing roles of embolization performance, CT imaging effect, and local tumor killing effect. The feasibility of MVLs as a multifunctional embolic agent in its clinical application can be further improved by optimization of lipid composition and preparation process.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
To improve bioavailability of pueraria flavones (PF), a self-microemulsifying drug delivery system (SMEDDS) dropping pills composed of PF, Crodamol GTCC, Maisine 35-1, Cremophor RH 40, 1,2-propylene ...glycol and polyethylene glycol 6000 (PEG6000) was developed. Particle size, zeta potential, morphology and in vitro drug release were investigated, respectively. Pharmacokinetics, bioavailability of PF-SMEDDS dropping pills and commercial Yufengningxin dropping pills were also evaluated and compared in rats. Puerarin treated as the representative component of PF was analyzed. Dynamic light scattering showed the ability of PF-SMEDDS dropping pills to form a nanoemulsion droplet size in aqueous media. The type of media showed no significant effects on the release rate of PF. PF-SMEDDS dropping pills were able to improve the in vitro release rate of PF, and the in vitro release of these dropping pills was significantly faster than that of Yufengningxin dropping pills. There was a dramatic difference between the mean value of t1/2, peak concentration (Cmax), the area of concentration–time curve from 0 to 6 h (AUC0–6 h) of PF-SMEDDS dropping pills and that of commercial Yufengningxin dropping pills. A pharmacokinetic study showed that the bioavailability of PF was greatly enhanced by PF-SMEDDS dropping pills. The value of Cmax and relative bioavailability of PF-SMEDDS dropping pills were dramatically improved by an average of 1.69- and 2.36-fold compared with that of Yufengningxin dropping pills after gavage administration, respectively. It was concluded that bioavailability of PF was greatly improved and that PF-SMEDDS dropping pills might be an encouraging strategy to enhance the oral bioavailability of PF.
Abstract Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D ...scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.
Aspect-based sentiment analysis aims to predict the sentiment polarity of each specific aspect term in a given sentence. However, the previous models ignore syntactical constraints and long-range ...sentiment dependencies and mistakenly identify irrelevant contextual words as clues for judging aspect sentiment. In addition, these models usually use aspect-independent encoders to encode sentences, which can lead to a lack of aspect information. In this paper, we propose an aspect-gated graph convolutional network (AGGCN), that includes a special aspect gate designed to guide the encoding of aspect-specific information from the outset and construct a graph convolution network on the sentence dependency tree to make full use of the syntactical information and sentiment dependencies. The experimental results on multiple SemEval datasets demonstrate the effectiveness of the proposed approach, and our model outperforms the strong baseline models.
Oxidative stress is known as one of the main contributors in renal ischemia/reperfusion injury (IRI). Here we hypothesized that Micro-vesicles (MVs) derived from human Wharton Jelly mesenchymal ...stromal cells (hWJMSCs) could protect kidney against IRI through mitigating oxidative stress. MVs isolated from hWJMSCs conditioned medium were injected intravenously in rats immediately after unilateral kidney ischemia for 60 min. The animals were sacrificed at 24 h, 48 h and 2 weeks respectively after reperfusion. Our results show that the expression of NOX2 and reactive oxygen species (ROS) in injured kidney tissues was declined and the oxidative stress was alleviated in MVs group at 24 h and 48 h in parallel with the reduced apoptosis and enhanced proliferation of cells. IRI-initiated fibrosis was abrogated by MVs coincident with renal function amelioration at 2 weeks. NOX2 was also found down-regulated by MVs both in human umbilical vein endothelial cells (HUVEC) and NRK-52E cell line under hypoxia injury model in vitro. In conclusion, a single administration of hWJMSC-MVs might protect the kidney by alleviation of the oxidative stress in the early stage of kidney IRI through suppressing NOX2 expression. Moreover, it could reduce the fibrosis and improved renal function.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
