Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results

Artificial Intelligence-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes

Lithium thiophosphates (LPSs) with the composition (Li2S)x(P2S5)1-x are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature (>10-3 S cm-1), soft mechanical properties, and low grain boundary resistance. Several glass-ceramic (gc) LPSs with different compositions and good Li conductivity have been previously reported, but the relationship among composition, atomic structure, stability, and Li conductivity remains unclear due to the challenges in characterizing noncrystalline phases in experiments or simulations. Here, we mapped the LPS phase diagram by combining first-principles and artificial intelligence (AI) methods, integrating density functional theory, artificial neural network potentials, genetic-algorithm sampling, and ab initio molecular dynamics simulations. By means of an unsupervised structure-similarity analysis, the glassy/ceramic phases were correlated with the local structural motifs in the known LPS crystal structures, showing that the energetically most favorable Li environment varies with the composition. Based on the discovered trends in the LPS phase diagram, we propose a candidate solid-state electrolyte composition, (Li2S)x(P2S5)1-x (x ∼0.725), that exhibits high ionic conductivity (>10-2 S cm-1) in our simulations, thereby demonstrating a general design strategy for amorphous or glassy/ceramic solid electrolytes with enhanced conductivity and stability

A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate

Silicate weathering, which is of great importance in regulating the global carbon cycle, has been found to be affected by complicated factors, including climate, tectonics and vegetation. However, the exact transfer function between these factors and the silicate weathering rate is still unclear, leading to large model–data discrepancies in the CO2 consumption associated with silicate weathering. Here we propose a simple parameterization for the influence of vegetation cover on erosion rate to improve the model–data comparison based on a state-of-the-art silicate weathering model. We found out that the current weathering model tends to overestimate the silicate weathering fluxes in the tropical region, which can hardly be explained by either the uncertainties in climate and geomorphological conditions or the optimization of model parameters. We show that such an overestimation of the tropical weathering rate can be rectified significantly by parameterizing the shielding effect of vegetation cover on soil erosion using the leaf area index (LAI), the high values of which are coincident with the distribution of leached soils. We propose that the heavy vegetation in the tropical region likely slows down the erosion rate, much more so than thought before, by reducing extreme streamflow in response to precipitation. The silicate weathering model thus revised gives a smaller global weathering flux which is arguably more consistent with the observed value and the recently reconstructed global outgassing, both of which are subject to uncertainties. The model is also easily applicable to the deep-time Earth to investigate the influence of land plants on the global biogeochemical cycle.</p

Sources and formation of carbonaceous aerosols in Xi'an, China:Primary emissions and secondary formation constrained by radiocarbon

To investigate the sources and formation mechanisms of carbonaceous aerosols, a major contributor to severe particulate air pollution, radiocarbon (C-14) measurements were conducted on aerosols sampled from November 2015 to November 2016 in Xi'an, China. Based on the C-14 content in elemental carbon (EC), organic carbon (OC) and water-insoluble OC (WIOC), contributions of major sources to carbonaceous aerosols are estimated over a whole seasonal cycle: primary and secondary fossil sources, primary biomass burning, and other non-fossil carbon formed mainly from secondary processes. Primary fossil sources of EC were further sub-divided into coal and liquid fossil fuel combustion by complementing C-14 data with stable carbon isotopic signatures. The dominant EC source was liquid fossil fuel combustion (i.e., vehicle emissions), accounting for 64 % (median; 45 %-74 %, interquartile range) of EC in autumn, 60 % (41 %-72 %) in summer, 53 % (33 %-69 %) in spring and 46 % (29 %-59 %) in winter. An increased contribution from biomass burning to EC was observed in winter (similar to 28 %) compared to other seasons (warm period; similar to 15 %). In winter, coal combustion (similar to 25 %) and biomass burning equally contributed to EC, whereas in the warm period, coal combustion accounted for a larger fraction of EC than biomass burning. The relative contribution of fossil sources to OC was consistently lower than that to EC, with an annual average of 47 +/- 4 %. Non-fossil OC of secondary origin was an important contributor to total OC (35 +/- 4 %) and accounted for more than half of non-fossil OC (67 +/- 6 %) throughout the year. Secondary fossil OC (SOCfossil) concentrations were higher than primary fossil OC (POCfossil) concentrations in winter but lower than POCfossil in the warm period. Fossil WIOC and water-soluble OC (WSOC) have been widely used as proxies for POCfossil and SOCfossil, respectively. This assumption was evaluated by (1) comparing their mass concentrations with POCfossil and SOCfossil and (2) comparing ratios of fossil WIOC to fossil EC to typical primary OC-to-EC ratios from fossil sources including both coal combustion and vehicle emissions. The results suggest that fossil WIOC and fossil WSOC are probably a better approximation for primary and secondary fossil OC, respectively, than POCfossil and SOCfossil estimated using the EC tracer method

Spike 1 trimer, a nanoparticle vaccine against porcine epidemic diarrhea virus induces protective immunity challenge in piglets

Porcine epidemic diarrhea virus (PEDV) is considered the cause for porcine epidemic diarrhea (PED) outbreaks and hefty losses in pig farming. However, no effective commercial vaccines against PEDV mutant strains are available nowadays. Here, we constructed three native-like trimeric candidate nanovaccines, i.e., spike 1 trimer (S1-Trimer), collagenase equivalent domain trimer (COE-Trimer), and receptor-binding domain trimer (RBD-Trimer) for PEDV based on Trimer-Tag technology. And evaluated its physical properties and immune efficacy. The result showed that the candidate nanovaccines were safe for mice and pregnant sows, and no animal death or miscarriage occurred in our study. S1-Trimer showed stable physical properties, high cell uptake rate and receptor affinity. In the mouse, sow and piglet models, immunization of S1-Trimer induced high-level of humoral immunity containing PEDV-specific IgG and IgA. S1-Trimer-driven mucosal IgA responses and systemic IgG responses exhibited high titers of virus neutralizing antibodies (NAbs) in vitro. S1-Trimer induced Th1-biased cellular immune responses in mice. Moreover, the piglets from the S1-Trimer and inactivated vaccine groups displayed significantly fewer microscopic lesions in the intestinal tissue, with only one and two piglets showing mild diarrhea. The viral load in feces and intestines from the S1-Trimer and inactivated vaccine groups were significantly lower than those of the PBS group. For the first time, our data demonstrated the protective efficacy of Trimer-Tag-based nanovaccines used for PEDV. The S1-Trimer developed in this study was a competitive vaccine candidate, and Trimer-Tag may be an important platform for the rapid production of safe and effective subunit vaccines in the future

Light-driven C-H activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials

Light-driven C-H bond activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials

Light-driven C–H activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation and carbon dots synthesis. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials