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Converting low-grade heat from environment into electricity shows great sustainability for mitigating the energy crisis and adjusting energy configurations. However, thermally rechargeable devices typically suffer from poor conversion efficiency when a semiconductor is employed. Breaking the convention of thermoelectric systems, we propose and demonstrate a new zinc ion thermal charging cell to generate electricity from low-grade heat via the thermo-extraction/insertion and thermodiffusion processes of insertion-type cathode (VO2-PC) and stripping/plating behaviour of Zn anode. Based on this strategy, an impressively high thermopower of ~12.5 mV K−1 and an excellent output power of 1.2 mW can be obtained. In addition, a high heat-to-current conversion efficiency of 0.95% (7.25% of Carnot efficiency) is achieved with a temperature difference of 45 K. This work, which demonstrates extraordinary energy conversion efficiency and adequate energy storage, will pave the way towards the construction of thermoelectric setups with attractive properties for high value-added utilization of low-grade heat., Low-grade heat conversion has recently emerged and displayed great promise in sustainable electronics and energy areas. Here, the authors propose a new zinc ion thermal charging cell with hybrid behaviours for high value-added conversion from heat to electricity.

Data visualization plays an important role in data analysis by displaying data to observers in an interpretable way. Visualizing multidimensional data requires projecting the data into a low-dimensional space that is visible to humans. In this paper, we propose a neural network model that can generate such projections while preserving the topology relationships within data points, which is named Visible Self Organizing Incremental Neural Network (V-SOINN). V-SOINN is able to construct a topology preserving visible network automatically and classify visible nodes to different classes in the low-dimensional space. The thought of topology preserving visualization stems from Self-Organizing Map (SOM). Compared to SOM, the main advantage of V-SOINN is that it does not need prior decision of network structure, including the number of nodes and grid in the output layer. V-SOINN can show the density distribution of datasets by using the activation counts of datasets. V-SOINN is able to depict the number of classes in the low-dimensional space as well. We perform experiments on artificial and real-world datasets, and V-SOINN outperforms PCA, MDS, t-SNE, Neural Gas and SOM on the datasets. Experiments show that V-SOINN can preserve the topology and V-SOINN can produce the correct classification result when the number of samples is small.

Additional file 7: Figure S1. The purity of binning results generated by genome binning tools on chicken gut metagenomic datasets. Figure S2. The completeness of binning results generated by genome binning tools on chicken gut metagenomic datasets. Figure S3. The contamination of bins recovered from CAMI high-complexity datasets. DASTool, Binning-refine and MetaWrap combined the results of Groopm2, MetaBat2 and Solidbin. Figure S4. The contamination of bins recovered from CAMI medium-complexity datasets. DASTool, Binning-refine and MetaWrap combined the results of Groopm2, MetaBat2 and Solidbin. Figure S5. Heatmap of confusion matrices of (a) Groopm2, (b) MetaBat2, (c) Solidbin, (d) DASTool (e) MetaWRAP binning results from CAMI medium-complexity datasets, indicating the number of base parirs that were assigned to predicated bins (x-axis) generated by genome binner and underlying genomes (y-axis). Figure S6. Boxplot of completeness of binning results for CAMI (a) high, (b) medium, (c) low-complexity datasets. Figure S7. Boxplot of purity of binning results for CAMI (a) high, (b) medium, (c) low-complexity datasets.

Additional file 7: Figure S1. The purity of binning results generated by genome binning tools on chicken gut metagenomic datasets. Figure S2. The completeness of binning results generated by genome binning tools on chicken gut metagenomic datasets. Figure S3. The contamination of bins recovered from CAMI high-complexity datasets. DASTool, Binning-refine and MetaWrap combined the results of Groopm2, MetaBat2 and Solidbin. Figure S4. The contamination of bins recovered from CAMI medium-complexity datasets. DASTool, Binning-refine and MetaWrap combined the results of Groopm2, MetaBat2 and Solidbin. Figure S5. Heatmap of confusion matrices of (a) Groopm2, (b) MetaBat2, (c) Solidbin, (d) DASTool (e) MetaWRAP binning results from CAMI medium-complexity datasets, indicating the number of base parirs that were assigned to predicated bins (x-axis) generated by genome binner and underlying genomes (y-axis). Figure S6. Boxplot of completeness of binning results for CAMI (a) high, (b) medium, (c) low-complexity datasets. Figure S7. Boxplot of purity of binning results for CAMI (a) high, (b) medium, (c) low-complexity datasets.

Supplemental material, Supplemental_Material_FISH_Dou_et_al for An Automated High-Throughput Fluorescence In Situ Hybridization (FISH) Assay Platform for Use in the Identification and Optimization of siRNA-Based Therapeutics by Hui H. Dou, Rommel Mallari, Andrew Pipathsouk, Amrita Das and Mei-Chu Lo in SLAS Discovery

In order to meet the thermal performance analysis accuracy requirements of high density permanent magnet synchronous motor (PMSM), a method of multi physical domain coupling thermal analysis based on control circuit, electromagnetic and thermal is presented. The circuit, electromagnetic, fluid, temperature and other physical domain are integrated and the temperature rise calculation method that considers the harmonic loss on the frequency conversion control as well as the loss non-uniformly distributed and directly mapped to the temperature field is closer to the actual situation. The key is to obtain the motor parameters, the realization of the vector control circuit and the accurate calculation and mapping of the loss. Taking a 48 slots 8 poles high density PMSM as an example, the temperature rise distribution of the key components is simulated, and the experimental platform is built. The temperature of the key components of the prototype machine is tested, which is in agreement with the simulation results. The validity and accuracy of the multi physical domain coupling thermal analysis method are verified.

The objective of this study is to determine the main geochemical processes that affect the formation and genesis of geothermal water in the Guanzhong Basin. Samples in different zone and different depth are taken and analyzed to reflect the variety of chemical characteristics of geothermal water in the area. Based on the chemical analysis, the main water chemical actions are the decarbonation and desulfurization function. Dissolution, Decarbonation reaction is happened widely in Guanzhong basin while desulfurization function is only found in deep stratum of Xi’an. Dissolution of calcite results in the Ca-HCO3 type water of mountainfront recharge area.

A freestanding film composed of graphene (GN) sheets and polyaniline (PANI) nanofibres was fabricated by reducing a graphite oxide (GO)/PANI precursor that was prepared by flow-directed assembly from a complex dispersion of GO and PANI. This was followed by reoxidation and redoping of the reduced PANI in the composite to restore the conducting PANI structure. A scanning electron microscope (SEM) image indicates that the GN/PANI film is a layered structure with PANI nanofibres uniformly sandwiched between the GN sheets. In the composite film, the PANI nanofibres can increase the basal spacing between GN sheets. Therefore, electrolyte ions have better accessibility to the GN surfaces. The GN sheets can act as current collector to decrease the inner resistance of the electrode, which is convenient for electronic and ionic transportation during the redox process of PANI. The electrochemical properties of the freestanding GN/PANI film were estimated by cyclic voltammetry and galvanostatic charge-discharge in 1 mol·L - 1 HCl electrolyte. Electrochemical analysis demonstrates that the as-prepared GN/PANI film has good capacitive behavior. The specific capacitance was 495 F·g - 1

The recent years witnessed rapid emergence and proliferation of cloud computing. To fully utilize the compute resources, some cloud operators provide spot resources. Spot resources allow customers to bid on unused capacity. However, pricing policy of spot resources should be carefully designed and the impact on both present and future should be considered. For the present, the cloud provider can set a higher price to gain extra revenue. For the future, higher price will shift more requests with lower prices to later time and reduce the revenue of future. Meanwhile, the quality of service should be considered either since bad QoS will incur loss of potential users. In this paper, we present a demand curve to model the impact of pricing on the present and future revenue. Then we formulate the revenue maximization problem as a time-average optimization problem. Next, since this basic model fails to provide information of service delay, we extend it to a more generalized one that ensures the worst-case delay of user requests. While the future knowledge of arrival requests is unknown, it is necessary to design online algorithms for the optimization problems. We apply Lyapunov optimization framework and design an efficient online algorithm which dose not require any future knowledge of requests arrival. Evaluations based on real-life datacenter workload and Amazon EC2 Spot Price illustrate efficiency of our algorithms.

In order to analyze the distributing condition of urban passenger flow scientifically and correctly, the disaggregate Logit model is presented to predict the public transportation share ration in city, which is carried out by means of analysis of the outer and inner factors that affect the choice of modes of transportation and is based on the random utility theory. Firstly, the factors with major contribution to modes choice are selected according to the likelihood ratio statistic. Then the parameters of are estimated and the model is constructed. Finally, according to the proposed algorithm, the public transportation share ratio forecast test is carried out using the field survey data. The results of independent sample test indicate that the model has a finer precision and stability.

(1991). Reflections on the New World Order. Bulletin of the Atomic Scientists: Vol. 47, New World Order, pp. 19-37.

The efficiency and the effect of fault diagnosis are increasingly important for airplane flying on timetable and flight security. But the Fault diagnosis within airplane is a complex and time consuming task. In this paper, an agent based fault diagnosis support system (AFDSS) is proposed to support ground crew in process of airplane fault diagnosis. This AFDSS consists of four kinds of agents: management agent, interface agent, diagnosis agent and data agent. Management agent serves as an agent name server and keeps the all agents' information, such as name, location, capabilities. Interface agent is the interface of AFDSS. Fault diagnosis user and expert have their own interface agent to interact with the AFDSS. Diagnosis agent encapsulates a diagnosis method. Data agent gets the required signals for fault diagnosis. All agents have the capabilities of communication and cooperation with each other. We study the AFDSS architecture, and discuss the development technology of AFDSS.