Search

High energy consumption has become a growing concern in the operation of complex cloud data centers due to the ever-expanding size of cloud computing facilities and the ever-increasing number of users. It is critical to find viable solutions to cloud task scheduling so that cloud resources can be utilized in an energy-efficient way while still meeting diverse user requirements in real time. In this research we propose a Q-learning based task scheduling framework for energy-efficient cloud computing (QEEC). QEEC has two phases. In the first phase a centralized task dispatcher is used to implement the M/M/S queueing model, by which the arriving user requests are assigned to each server in a cloud. In the second phase a Q-learning based scheduler on each server first prioritizes all the requests by task laxity and task life time, then uses a continuously-updating policy to assign tasks to virtual machines, applying incentives to reward the assignments that can minimize task response time and maximize each server’s CPU utilization. We have conducted simulation experiments, which have confirmed that implementing a M/M/S queueing system in a cloud can help to reduce the average task response time. The experiments have also demonstrated that the QEEC approach is the most energy-efficient as compared to other task scheduling policies, which can be largely credited to the M/M/S queueing model and the Q-learning strategy implemented in QEEC.

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher-quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (Tg) and decomposition temperature (Td), but experimental determinations of these two important properties genernally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than one thousand samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of MAE, RMSE, and R2 were 17.15 K, 24.63 K, and 0.77 for Tg prediction and 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models were further tested by out-of-sample data, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based model. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://oledtppxmpugroup.com. We expect that this platform will become a useful tool for experimental investigation of Tg and Td, accelerating the design of OLED materials with desired properties.

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, further development and commercialization of OLEDs requires higher-quality OLED materials, including high thermal stability associated with the glass transition temperature (Tg) and decomposition temperature (Td). Experimental determinations of the two important properties genernally involve a time-consuming and laborious process. Thus, it is highly desired to develop a quick and accurate prediction tool. Motivated by the changelle, we explored machine learning based framework by constructing new dataset with more than one thousand samples collected from a wide range of literaturesm, throngh which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibit the best prediction performance, where the values of MAE, RMSE, and R2 are 17.15 K, 24.63 K, and 0.77 for Tg prediction, 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models are further tested by out-of-sample dataset, also exhibiting satisfactory results. Experimental verification further demonstrates the reliability and the practical potential of the ML-based model. In order to extend the practice application of the ML-based models, an online prediction platform was constructed, including the optimal predition models and all the thermal stability data under study, which are freely available at http://oledtppxmpugroup.com. We expect that they will become a useful tool for experimental investigations on Tg and Td, in turn accelerating the design of the OLED materials with high performance.

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, further development and commercialization of OLEDs requires higher-quality OLED materials, including high thermal stability associated with the glass transition temperature (Tg) and decomposition temperature (Td). Experimental determinations of the two important properties genernally involve a time-consuming and laborious process. Thus, it is highly desired to develop a quick and accurate prediction tool. Motivated by the changelle, we explored machine learning based framework by constructing new dataset with more than one thousand samples collected from a wide range of literaturesm, throngh which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibit the best prediction performance, where the values of MAE, RMSE, and R2 are 17.15 K, 24.63 K, and 0.77 for Tg prediction, 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models are further tested by out-of-sample dataset, also exhibiting satisfactory results. Experimental verification further demonstrates the reliability and the practical potential of the ML-based model. In order to extend the practice application of the ML-based models, an online prediction platform was constructed, including the optimal predition models and all the thermal stability data under study, which are freely available at http://oledtppxmpugroup.com. We expect that they will become a useful tool for experimental investigations on Tg and Td, in turn accelerating the design of the OLED materials with high performance.

A series of blue-emissive 7-(diphenylamino)-4-phenoxycoumarin derivatives bearing -CF3 , -OMe, or -N(Me)2 substituents on the phenoxy subunit were synthesized. Although both the -CF3 and -N(Me)2 modifications were found to trigger redshifted fluorescence, the -OMe substitution was demonstrated to exert an unexpected blueshift color-tuning effect toward the deep-blue region. The reason is that the moderate electron-donating -OMe group can endow coumarins with unaltered HOMO but elevated LUMO energy levels. Moreover, the -OMe substitution was found to be beneficial to the thermal stability of these coumarins. Therefore, the trimethoxy-substituted objective compound can act as a high-performance deep-blue organic light-emitting diode (OLED) emitter, and OLED based on it emits deep-blue light with CIE coordinates of (0.148, 0.084), maximum luminance of 7800 cd m-2 , and maximum external quantum efficiency of 5.1 %. These results not only shed light on the molecular design strategy for high-performance deep-blue OLED emitters through color-tuning, but also show the perspective of coumarin derivatives as deep-blue OLED emitters.

Nitroaromatic compounds pose a significant risk to human health and other living organisms. However, conventionally high performance liquid chromatography (HPLC) methods are complicated and time-consuming for complete separation of multiple nitroaromatic compounds due to their high structure-similarity. In the work, a facile yet effective strategy, which combines HPLC embedded by diode array detection (HPLC-DAD) with multivariate curve resolution-alternating least squares (MCR-ALS), is explored to improve the HPLC analysis. Based on the strategy, seven nitroaromatic compounds with similar structures could be rapidly quantified under a simple isocratic elution condition (acetonitrile/water: 65:35, v/v) within 10 min. With the aid of the second-order advantage of MCR-ALS, acceptable quantification results are still achieved for the real water samples, despite of significantly overlapping peaks resulted from the similar structures of the analytes and the unexpected interferences from the real water sample. The error analysis and the elliptical joint confidence region (EJCR) test further confirm the reliability of the predicted result. Thereby, the HPLC method coupled with the second-order calibration algorithm could provide a simple, quick and accurate strategy for simultaneous determination on the multiple nitroaromatic compounds in environmental monitoring.

Organic light-emitting diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (Tg) and decomposition temperature (Td), but experimental determinations of these two important properties generally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than 1,000 samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of mean absolute error, root mean squared error, and R2 were 17.15 K, 24.63 K, and 0.77 for Tg prediction and 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the ML models were further tested by two applications, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based models. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://www.oledtppxmpugroup.com . We expect that this platform will become a useful tool for experimental investigation of Tg and Td, accelerating the design of OLED materials with desired properties.

A facile yet efficient strategy was proposed by means of a combination of ultraviolet (UV) spectrophotometry with multivariate calibration methods, through which 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene in water samples could be simultaneously determined without any pre-separation process. The competitive adaptive reweighted sampling combined with successive projections algorithm (CARS–SPA) approach was used to diminish uninformative variables and select the important ones from spectral data measured. The multivariate calibration models were constructed by partial least squares (PLS-1) regression with high accuracy, in which the coefficients of determination of prediction ( R pred 2 ) were 0.9935, 0.9969, and 0.9971 and the root mean square error of prediction (RMSEP) were 0.7850, 0.5411 and 0.5414 for 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene, respectively. The optimized model was successfully applied to simultaneously determine the content of the three studied analytes in several real water samples with good recovery close to 100%. Finally, the elliptical joint confidence region (EJCR) tests further confirm that the proposed method has no proportional and constant error in the predicted concentrations, providing a statistic support for the accuracy of the model. These results indicate that it is promise for UV spectroscopy coupled to the multivariate calibration technique to establish a simple, quick, accurate and reliable analysis method for simultaneous determination of some nitroaromatic compounds in real environments. Also, the strategy proposed by the work will advance the analytical methods used in the other complicated sample systems.

G-protein-coupled receptors (GPCRs) are important drug targets and generally activated by ligands. However, some experiments found that GPCRs also give rise to constitutive activity through some mutations (viz., CAM), which are usually associated with different kinds of diseases. However, the mechanisms of CAMs and their roles in interactions with drug-ligands are unclear in experiments. Herein, we used microsecond molecular dynamics simulations to study the effect of one important F282L mutation on β2AR in order to address the questions above. With the aid of principle component and correlation analysis, our results revealed that the F282L mutation could increase the instability of the overall structure, increase the dramatic fluctuations of NPxxY and extracellular loops, and decrease restraint of the helices through weakening interhelical H-bonding and correlations between residues, which could partly contribute to the constitutive activity reported by the experiments. The observations from the protein structure network (PSN) analysis indicate that the mutant exhibits less information flow than the wild β2AR and weakens the role of TM5 and TM6 in the signal transmission, but it enhances the impact of TM3 on the orthosteric pathway and TM4 on the allosteric one. In addition, the results from the virtual screening reveal that the mutant prefers to select agonists rather than antagonists, similar to the active state but opposite of the inactive state, further confirming that the F282L mutation advances the activation of β2AR. Our observations provide valuable information for understanding the mechanism of the mutation-caused constitutive activity of GPCR and related drug-design.

Owing to the difficulty in acquiring compounds with combined high energy bandgaps and lower-lying intramolecular charge-transfer excited states, the development of ultraviolet (UV) thermally activated delayed fluorescence (TADF) materials is quite challenging. Herein, through interlocking of the diphenylsulfone (PS) acceptor unit of a reported deep-blue TADF emitter (CZ-PS) by a dimethylmethylene bridge, CZ-MPS, a UV-emissive TADF compound bearing a shallower LUMO energy level and a more rigid structure than those of CZ-PS is achieved. This represents the first example of a UV-emissive TADF compound. Organic light-emitting diode (OLED) using CZ-MPS as the guest material can emit efficient UV light with emission maximum of 389 nm and maximum total external quantum efficiency (EQEmax ) of 9.3%. Note that this EQEmax value is twice as high as the current record EQEmax (4.6%) for UV-OLEDs. This finding may shed light on the molecular design strategy for high-performance UV-OLED materials.

Explosive determination is of great importance in national defense and security fields. Simple, quick and reliable analytical techniques have been highly demanded in the field. In this work, we proposed a novel method for simultaneous determination of pentaerythritol tetranitrate (PETN), 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), and 2,4,6-trinaitrotolunene (TNT) in binary-component polymer bonded explosive (PBX) samples by means of a combination of ultraviolet (UV) spectrophotometry with multivariable calibration methods. An orthogonal array design (DAD) was employed to construct the calibration set, which contains 27 reference samples. The calibration models were constructed using Partial Least Square regression (PLS-1) and Multiple Linear Regression (MLR). The variables were selected by the Successive Projection Algorithm (SPA) in MLR model. The predictive ability of the optimized models was validated by a test set including 18 samples. Finally, the two optimized models were successfully applied to simultaneously determine the content of PETN, RDX and TNT in five real binary-component PBX samples. Satisfactory results were obtained for the two models, in which the recovery yields were close to 100% for all the analytes. The computed elliptical joint confidence region (EJCR) further shows that the two models have no proportional and constant errors in the predicted concentrations. In addition, the statistical analysis indicates that MLR model with reasonable variable selection (SPA-MLR) could exhibit a slight superiority toward PLS-1 in the system. In a word, UV-spectroscopy in combination with multivariable calibration techniques has high potential to be a simple, quick and accurate analysis method for explosive determination in practice.

A dwarf mutant 'NDF-1', approximately 70 cm high, was derived from a 200-cm high doubled haploid (DH) line '3529' (Brassica napus), seeds of which were jointly treated with chemical inducers and bombardment of fast neutron. The leaves of the 'NDF-1' mutant were wrinkled and thicker compared with the wild-type control. The mutant had much lower values than its original parents for all agronomic traits, except for its seed weight. A genetic analysis revealed that dwarfism is under the control of a major gene (designated as ndf1) with a mainly additive effect and non-significant dominance effect. Because of the high level of resistance to lodging, breeding programmes for double low dwarf oilseed rape and heterosis utilization were initiated. Some new dwarf strains with improved agronomic performance were developed. The hybrid of the cross between the tall parent and the dwarf line showed increased harvest index and significantly higher seed yield than the tall parent or the control variety 'Zhongyou 821' and presented an estimated heterosis vigour rate as high as 12.5-25.8%. The dwarf trait will be a promising marker for a simple, economic and efficient way to control the purity of F 1 hybrid varieties in hybrid production of B. napus.

A mutant chlorophyll-reduced (Cr) seedling with yellow-green cotyledons and leaves was obtained from the Brassica napus inbred line 3529 induced by fast neutron and diethyl sulphate (DES). Genetic analysis revealed that the Cr seedling marker trait was controlled by a pair of recessive genes. A randomized complete block design was used to evaluate its agronomic performance. Results from 2 years of tests indicated that the seed yield of Cr lines was significantly lower than that of normal green plants; however, when the Cr gene was in the heterozygous condition, no deleterious effects on yield characteristics and disease resistance were observed. The Cr seedling marker trait was introduced into male-sterile lines, and Cr male-sterile lines revealed the same superior combining ability as normal chlorophyll (Nc) lines. The Cr trait can therefore be used as a marker to produce hybrid seed.

Capon Beamforming Algorithm involves calculating inverse covariance matrix. It's complex to calculate inverse matrix in engineering applications. On the basis of summary of distribution of covariance matrix elements, an improved inverse covariance matrix calculating method is proposed. Simulation shows that the improved algorithm achieves the same accuracy with original algorithm as reducing the computational complexity. (6 pages)

A method of Doppler frequency rate estimation is proposed to deal with motion compensations for missile-borne SAR imaging. Working in burst SAR mode, Map Drift method is improved to satisfy the complicated condition of missile-borne motion, such as PRF up to ten kHz and maneuvering speed up to 3 Ma. Appropriate block size of azimuth data sequence and correlative operation should be chosen according to performance analysis, which is shown in this paper. Experimentations proved the validity of this method.

In this paper, the principle of Unscented transformation is analyzed and an improved UKF (Unscented Kalman Filter) algorithm is proposed based on electronic technology. To simplify the computational complexity of the tracing system, identity matrix is introduced to the Unscented transformation, by which the complicated matrix calculation is replaced by simple numerical calculation for matrix square root solving. The new method called FMSRUKF (Fixed Matrix Square Root Unscented Kalman Filter) is an improved algorithm of UKF on target tracking based on Electronic Technology. Simulation results of variably accelerated motion target tracing under three dimensional coordinate shows that FMSRUKF achieves better precision and robustness.

A variety of graph-based algorithms have been proposed to generate attack graph automatically, few of which take network performance into consideration and satisfy the purpose of network vulnerability analysis. In this paper, Virtual Performance Node (VPN) is defined and an improved graph-based algorithm is proposed. The performance decrease due to attacker's incursion in the whole network is regarded as the attacker's expectation and VPNs are chosen as the net status. This method makes attack graph have an extra ability to measure the attack effect with network performance loss. The algorithm is compared with other methods in the analysis of an experimental network. The results show that the improved attack graph has the least status and acts well with human cognitive habits, which makes it more useful to analyze network vulnerability.

The performances of Background Difference Method and Frame-difference Method for detecting moving objects are analyzed. It is found that as the second method is characterized by being sensitive only to moving objects, it can be used to update the background image, an important step for the first method. A new technique integrating both methods is then proposed. Using the consecutive frames differencing images and the detection result of the previous frame, we restore background model, which can be utilized later to eliminate the detection error caused by the Frame-difference Method. Based on this truth, we design a detecting system to realize the new algorithm. Experiments on indoor and outdoor video streams show that the new technique has strong adaptability and veracity.