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Since the Antarctic scientific research device has been operating in Antarctica for a long time, the power generation cabin needs to continuously provide heat so that its equipment can work within a suitable temperature range. In view of the complexity of the environment and the high cost of equipment, it is difficult to test and evaluate, which can be well solved by numerical simulation test. The thermodynamic mechanism model of the Antarctic redundant cabin is established to simulate the temperature distribution of the Antarctic scientific research power cabin. The temperature data of the mechanism model is combined with the time series algorithm to predict the future high temperature accumulation in the engine room, thereby providing theoretical and data support for the decision-making of the temperature control system of the scientific research device.

AAOISE, the Antarctic Astronomical Observations Intelligent Support Equipment, is an autonomous control equipment serving for energy support and environment thermal preservation, which is used for astronomical science observations in the Antarctic “Dome A”. It was deployed to “Dome A” and had an unattended run until now. The AAOISE stressed on the ways to adapt to adverse circumstances of “Dome A” and to have as little influence on the environment as possible. Its shape and structure are fully qualified for transportation and thermal insulation demands. The power generation and control systems are designed to provide continuous power and heat. Its communication system can support high-reliability data transmission and communications. It offers a possibility for developing “Dome A” scientific activities and remote monitoring of the running situation of the science instruments. This paper presents a detailed description of the power generation, power control, thermal management, instrument interface, and communications systems for AAOISE.

Graph-based methods have been widely adopted to detect salient objects in images. However, there are two limitations of these methods. First, only one kind of query is employed for saliency propagation on the graph. Second, these methods only represent pairwise relations between vertices and thus give an incomplete representation of the relationships between image regions. In this paper, we propose a foreground- and background-queries-based hypergraph optimization framework for salient region detection. In this framework, both foreground queries and background queries are explicitly exploited to uniformly highlight the salient foreground and suppress the non-salient background. Furthermore, to include both the pairwise and the higher-order relations among two or more vertices, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among image regions from different views. Extensive experimental results demonstrate the effectiveness of the proposed framework.

To improve the accuracy of photovoltaic power prediction under haze weather, a short-term PV power prediction method combining the PM2.5 forecast and SVR model is proposed. The WRF-CHEM air quality model is used to realize the simulation calculation of PM2.5 concentration, combined with the historical output data of PV power plants, and based on the SVR machine learning model to build a direct mapping relationship model between multiple meteorological elements and PV output. A forecast experiment was carried out for a certain area in North China, and the results showed that the proposed method can effectively improve the accuracy of PV power forecasting under haze weather conditions, thereby providing strong support for power grid dispatch and operation.

Pituitary adenylate cyclase activating polypeptide (PACAP) is a neuroendocrine factor that activates both the receptor VPAC1 and VPAC2. Although PACAP possesses insulinotropic activity, its therapeutic application is limited by the extremely short acting half-life and the stimulatory effects on glucagon production via a VPAC1-dependent mechanism. Here we have generated a recombinant PACAP-derived peptide (named as MHDBAY) comprising a 7-mer albumin-binding peptide identified by phage display screening (WQRPSSW), a cleavage peptide for Factor Xa (FXa) and dipeptidyl peptidase IV (DPP IV), and a 31-amino acid PACAP-derived peptide (DBAY) that can specifically bind to the VPAC2 receptor. MHDBAY binds to albumin both in vitro and in animals, thereby leading to an orderly slow release of the active peptide DBAY via the protease cleavage. In db/db mice and New Zealand rabbits, the circulating half-life of MHDBAY is approximately 12.2 h, which is 146-fold longer than DBAY (∼5 min). A single injection of MHDBAY into db/db diabetic mice markedly increases insulin secretion, thereby leading to sustained alleviation of hyperglycemia. The potency and duration of MHDBAY in increasing insulin secretion and decreasing blood glucose levels is much greater than Exendin-4, an anti-diabetic drug via its insulinotropic actions. Furthermore, chronic administration of MHDBAY by daily injection for 8 weeks significantly improves both glucose and lipid profiles and also greatly increases insulin sensitivity in db/db mice. These findings suggest that serum albumin may act as a reservoir for slow-release of small bioactive peptides, and MHDBAY may represent a promising therapeutic peptide for diabetes.

In recent years, many graph-based methods have been introduced to detect saliency. These methods represent image regions and their similarity as vertices and edges in a graph. However, since they only represent pairwise relations between vertices, they give an incomplete representation of the relationships between regions. In this work, we propose a hypergraph based optimization framework for salient object detection to include not only the pairwise but also the higher-order relations among two or more vertices. In this framework, besides the relations among vertices, both the foreground and the background queries are explicitly exploited to uniformly highlight the salient objects and suppress the background. Furthermore, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among vertices from different views. Extensive experiments demonstrate the effectiveness of the proposed method.

The development of instrument technology enables total sky imagers with temporal and spatial high-resolution sky observations, which makes cloud classification based on ground-based sky images possible. However, it is difficult to classify clouds accurately just from a single image according to the International Cloud Classification System. Here, a method of cloud classification is proposed viewed the attenuation rate of solar direct normal radiation (DNI) as the judgment standard, based on a set of mainly statistical features describing the color and the texture of a sky image. To calculate the attenuation rate of DNI,a clear-sky model is firstly developed. And then, a neural network classifier is constructed to separate clouds into two classes: cloud with effect to DNI and cloud with no effect to DNI. The optimal architecture of the neural network is determined by means of two groups of training experiments. With the data from NREL Solar Radiation Research Laboratory, a test is carried out to evaluate the performance of the classifier. Results show that the proposed method can yield a success rate of about 91.67%. The method of cloud classification based on DNI is able to be applied in the photovoltaic system and may improve the accuracy of the short solar power forecasting.

The line of sight (LOS) stabilization control based on gyro stabilized platform is required to isolate the LOS from the disturbance and vibration of carrier and ensure pointing and tracking for target in electro-optical tracking system. A composite adaptive fuzzy proportional-integral-derivative (PID) control with hysteresis-band switching is developed to achieve real-time and high stabilization precision for this nonlinear uncertainty servo system. First of all, in the adaptive fuzzy controller, the pre-designed self-tuning factors are able to modify the parameters of fuzzy controller online, and a new learning algorithm of fuzzy rules modifier is proposed to adjust control efforts. Then, an improved PID controller is chosen to restrain motor saturation and eliminate the static error originated from the fuzzy controller, and fulfill non-error control. The hysteresis-band switching strategy is given to deal with jitter caused by single-point switching condition. The experimental results in four-axis servo turntable show that the proposed method can achieve nice control performance and is proved to be effective in bating carrier disturbances within the scope of definite noise and sensitivity to acceleration.

Recently, a number of graph-based approaches have been proposed to detect salient regions in images. Although the graph is essential for these approaches, the graph construction method has not been studied in much detail. We propose a graph construction method that makes better use of multiple Gestalt principles. Specifically, spatial proximity, color similarity, and texture similarity between image regions are employed to choose different edges of the graph. Furthermore, the confliction among multiple Gestalt principles is solved using a primal rank support vector machines algorithm to compute the edge weights. Our experimental results on various salient region detection databases with comparisons to representative approaches demonstrate that the proposed graph construction method helps to detect salient objects in images.

RMP16, a recombinant TNF α-derived polypeptide comprising a specific human serum albumin (HSA)-binding 7-mer peptide identified by phage display screening (WQRPSSW), a cleavage peptide for Factor Xa (IEGR) and a 20-amino acid bioactive peptide P16 (TNF α segment including amino acid residues 75–94), was prepared by gene-engineering technology. RMP16 showed prolonged half-life, 13.11 hours in mice (half-lives of P16 and TNF α are 5.77 and 29.0 minutes, respectively) and obviously higher receptor selectivity for TNFRI than TNF α. RMP16 had significant inhibition effects for multiple tumor cells, especially prostate cancer Du145 cells and human vascular endothelial cells but not for human mammary non-tumorigenic epithelial cells. RMP16 can more effectively induce apoptosis and inhibit proliferation for DU145 cells than P16 and TNF α via the caspase-dependent apoptosis pathway and G0/G1 cell cycle arrest. In nude mice with transplanted tumor of DU145 cells, RMP16 significantly induced apoptosis and necrosis of tumor tissues but causing less side effects and tumor inhibitory rate reached nearly 80%, furthermore, RMP16 can potently inhibit tumor angiogenesis and neovascularization. These findings suggest that RMP16 may represent a promising long-lasting antitumor therapeutic peptide with less TNF α-induced toxicity.

To guarantee performance of soft real-time applications operating in unpredictable environments, a predictive control based feedback scheduling is proposed. The performance metrics, utilization and tasks deadline miss ratio, are both considered in the cost function, while the corresponding weight matrixes are decided by system workload state. The control objectives are defined by their satisfaction degree represented with fuzzy membership functions, thus the control efforts achieves a balance between two metrics in the sense both with highest satisfaction degree when system overloaded. The optimization problem is solved by converting it into quadric programming problem subject to soft constraints. Simulation results show the proposed approach could achieve favorable performance and is robust against workload uncertainties.