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There are many areas in the world where the ground water has been contaminated by arsenic. MnO2 is one of the most cheap materials which can both adsorb arsenic and oxide arsenite [As(III)]...

Objective To investigate the clinical characteristics of neonatal necrotizing enterocolitis (NEC) complicated by intestinal perforation and predict the incidence of intestinal perforation in NEC. Methods Neonates diagnosed with NEC at the Affiliated Hospital of Zunyi Medical University from January 2012 to May 2022 were enrolled, and the clinical data were collected and analyzed retrospectively. The patients were divided into two groups based on intestinal perforation occurrence or not. Mann-Whitney U tests, t-tests, chi-square tests, and fisher's exact tests were performed between-group comparisons. Logistic and lasso regressions were applied to screen independent risk factors for concomitant bowel perforation, and R software (RMS package) was used to formulate the nomogram prediction model. In addition, the receiver operating curve (ROC) and the calibration curve were drawn to verify the predictive power, while decision curve analysis (DCA) was constructed to evaluate the clinical applicability of the nomogram model. Results 180 neonates with NEC were included, of which 48 had intestinal perforations, and 132 did not; the overall incidence of intestinal perforation was 26.67% (48/180). Bloody stool (OR = 5.60), APTT ≥ 50s (OR = 3.22), thrombocytopenia (OR = 4.74), and hypoalbuminemia (OR = 5.56) were identified as independent risk variables for NEC intestinal perforation (P

In this paper, we are concerned with the following singular perturbation Kirchhoff equation −(e2a+eb∫R3|∇u|2dy)Δu+V(y)u=|u|p−1u,u∈H1(R3), where constants a,b,e>0 and 1

MnO2 is one of the most widespread and cheapest materials in nature that can both adsorb arsenic and oxidize arsenite [As(III)] to arsenate [As(V)]. In this study, column β-MnO2 [CM] with the main facet of {110} and pincer β-MnO2 [PM] with the facets of {110} and {101} are synthesized and used to remove arsenic in water under different conditions. For the adsorption process, the experimental data are fitted well with the pseudo second-order kinetic model; the Langmuir model is better than the Freundlich model to describe the adsorption equilibrium isotherms. Furthermore, the As(III) oxidation rate can be denoted by the pseudo zero-order kinetic model and is related to the O2 concentration, the pH value, the light source and the initial concentration of As(III). Finally, the oxidation mechanism is investigated, and the oxidant should be related to O2. It is interesting to find that these two kinds of β-MnO2 exhibit different pH effects for both adsorption and oxidation. For As(III), the adsorption and oxidation abilities of CM follow the order pH 9 > pH 7 > pH 4, whereas the adsorption and oxidation orders of PM are pH 4 > pH 7 > pH 9.

In order to speed up the development and utilization of hydrothermal energy, it is essential to assess the potential of geothermal resources in petroliferous basins. In this paper, the distribution of reservoirs (aquifers) and the characteristics of geothermal fields have been studied systematically based on geological, geophysical, well drilling, temperature, and sample test data obtained from the major petroliferous basins of China. It has been found that some of the porous sandstone formations in these petroliferous basins are major geothermal reservoirs and are extensively thick and widely distributed. In general, the geothermal gradient in China is higher in the eastern basins and lower in the western basins. On average, the geothermal gradient is above 30 °C/km in the Bohai Bay, Songliao, and Subei basins. The geothermal resource abundance is also higher in eastern China and in the Beibuwan basin in southern China, and the geothermal source forming condition is better, followed by the Ordos, Qaidam, and Sichuan basins in Central China. Other potential basins include the Tarim and Junggar basins in western China, where the geothermal gradient ranges between 21 and 22 °C/km, on average. In this paper, three methods, stochastic simulation, unit volumetric, and analogy, were used for the assessment of geothermal resources. Using the stochastic simulation and unit volumetric methods, the geothermal resources, annual recovered geothermal resources, geothermal water resources, and thermal energy of water in 11 basins or blocks of up to 4000 m deep were calculated. Grading evaluation criteria were established by considering the heterogeneity of geothermal reservoirs. The results showed that the petroliferous basins are very rich in geothermal resources. The annual recoverable resources reach 1626.8 × 106 tons of standard coal, in which grade I, grade II, and grade III resources are 641.9 × 106, 298.6 × 106, and 686.3 × 106 tons of standard coal, respectively. The results demonstrate that the development and utilization of geothermal energy in oilfields has a huge potential for industrial production and family use, and a great significance for the development of green oilfields. With the high demand of heat, the eastern oilfields with high geothermal resource abundance should be the first to be considered for the production and utilization of geothermal energy, followed by the central and western oilfields.

This paper presents a graphical, performance-based energy storage capacity sizing method for residential feeders with high solar penetration levels. The rated power and storage capacity of an energy storage device (ESD) are calculated to fulfill a specified operational requirement. Three locations for installing ESDs are investigated: 1) consumer-owned ESDs inside single-family households; 2) utility-owned distribution transformer-level ESDs; and 3) third-party owned ESDs in a community. First, historical solar radiation data, residential household load data, and residential load models are used for creating the net load (load minus solar generation) ensembles at the house level with resolution of 15 min. Then, a novel graphical capacity selection method using equal probability lines on compressed, composite cumulative distribution function curves is developed for sizing the energy storage needs at the house, distribution transformer, and community levels. Demand-side management methods are investigated for further reducing the need of energy storage. Simulation results demonstrate that the proposed method avoids over- or under-sizing ESDs and allows the users to compare the marginal benefit of increasing the capacity of the ESD.

Chronic cerebral hypoperfusion (CCH) is a basic pathological process that is comorbid with brain diseases, such as vascular Parkinsonism and Alzheimer’s disease. Icariside II (ICS II), which is one of the main metabolites of icariin, has anti-inflammatory and antioxidant effects and protects against ischemic brain injury. This study aims to investigate the neuroprotective effects of ICS II on neuronal axon regeneration-related factors in a CCH rat model. Sprague-Dawley (SD) rats were divided into the following four groups: sham group, model group and 4 and 8 mg/kg/day ICS II administration groups. Learning and spatial memory functions were tested using a Morris water maze. Pathological changes were observed in the rat hippocampal tissue by hematoxylin and eosin (H&E) staining. Neuronal axon regeneration-related proteins (GAP-43, MAP-2 and Nogo-A) were observed by immunohistochemical staining and detected by the average optical density method. The results showed that 8 mg/kg/day of ICS II can effectively reduce the escape latency and prolong the target quadrant residence time at 12 weeks and that ICS II can improve the histopathological changes in the CA1 area of the rat hippocampus. Moreover, ICS II administration at 8 mg/kg/day significantly increased GAP-43 and MAP-2 expression and reduced Nogo-A expression in the CA1 area of the rat hippocampus at 12 weeks; however, significant differences were not observed at 4 and 8 weeks. Hence, ICS II at a dosage of 8 mg/kg/day could promote learning and memory abilities and improve histopathological changes in the rat hippocampus in a CCH rat model. These results may be related to the promotion of neuronal axon regeneration in the CA1 area of the hippocampus under increases in hippocampal GAP-43 and MAP-2 protein expression and decreased Nogo-A protein expression.

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This paper proposes a new approach to estimate dominant mode for monitoring inter-area oscillation in the China Southern power grid (CSG) by the use of phasor measurement units (PMUs) under both ringdown and ambient conditions. The state space model is identified by the data driven stochastic subspace identification (Data-SSI) algorithm. The canonical variate algorithm is used first to construct the weighted projection matrix of the Data-SSI. Then, the criterion for model order selection is developed to estimate the model order, and the linear model of power system is built with Data-SSI. The dominant oscillation modes are calculated by eigenvalue analysis. To accurately identify the dominant modes, repetitive results are calculated with model order variation, and then clustering analysis and stepwise refinement are applied to discriminating the dominant modes from trivial ones to improve the estimation accuracy. Field-measurement data collected by PMUs in CSG is used to validate the proposed algorithm. The comparison between existing mode estimation techniques and the proposed approach demonstrates its accuracy and robustness under both ringdown and ambient conditions.

Metal cations as well as water are important factors to control the synthesis of MnO2 crystal nanostructures. In this work, systemic Density functional theory calculations about α, β, δ-MnO2 are presented to show the importance of metal cations and water for the structure stability and energy stability of MnO2. It is shown that the α-MnO2 crystal and its (110) surface will crash without the tunnel cations such as K+, and the distance between the layers of the δ-MnO2 will be significantly lower than that of the experimental results without the interlayer metal cations and water. At the same time, α-MnO2 and δ-MnO2 can be more stable than β-MnO2 with metal cations and water, and vice versa.

Moraine dams usually collapse due to overtopping by the surge wave in the dammed lake, and the surge wave is most likely caused by an earthquake. The seismic water wave (SWW) is a major factor causing the dam to break in the earthquake zone. This paper focused on the SWW by model experiments with a shaking water tank under conditions of various water depths, seismic waves, and peak ground accelerations. Two empirical equations were obtained for estimating maximal wave height for the low and high frequency, respectively. Finally, we present the application of the empirical equations on Midui Glacier Lake in Tibet plateau.

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This paper introduces the design of a mobile energy management unit (M-EMU) for off-grid mini-microgrids. Mini-microgrids can be used for applications such as tents, emergency shelters, temporary houses, and mobile homes. In this paper, we introduce the system setup, the modeling approach for different appliances, and the prototype design of the M-EMU for mini-microgrids. A data-driven load-forecasting algorithm is developed for energy consumption prediction. We then formulate a mini-microgrid optimal energy management (OEM) problem whose objective is to maximize the user comfort respecting to the system constraints and duration requirement. A novel power cap based scheduling algorithm is proposed to iteratively solve the OEM problem. Case studies results show that the M-EMU can operate its loads to meet the backup duration requirement without violating the user comfort settings.

There was a common use of instrument and control (I&C) system based on analog technology in design and construction of nuclear power plant built more than ten years ago. With the development and update of automation technology, digital control system has almost completely replaced the older generation technology in many areas of industry. For the nuclear power plant still using the analog I&C system, it caused the reduction of the production lines of related components and the lack of specified technical engineer. Along with the aging and obsolescence of analog technology equipment, the unsustainability of spares prompted the owners of nuclear power plant to implement modernization using digital technologies. Different from the design of digital control system in new nuclear power plant, the modernization project design is limited by the setting of the original system. Therefore, most owners adopt the function alternative strategy to implement the upgrading project. This strategy can effectively solve the problem of spares shortage, but it is difficult to fully elaborate the advantages of digital control system. Based on the technical characteristics of digital control system and the design experience derived from new nuclear power project construction, this paper puts forward the optimized design measures, under the limitation of the old power plant, to enhance the safety and economy of the nuclear power plant after final digital upgrading.

This paper presents a cost-benefit study of residential photovoltaic (PV) systems equipped with energy storage (ES) based on synthesized load profiles. By modeling major appliance consumptions, typical load profiles of a residential household in a given area with outdoor temperature profiles can be obtained to study whether or not installing a rooftop PV system can be profitable. Ten years' solar irradiation and temperature data in North Carolina area are used to build four case studies: a base case with no PV installation, a PV-only case, a PV with ES case, and a PV with storage and demand side management (DSM) case. Duke Energy time-of-use rate is used in major case studies, and PG&E time-of-use rate is added for comparison of bill saving brought by the PV-ES system under different rate structures. The net present value (NPV) and years of return (YR) are used to quantify the cost and benefit of installing a residential PV-ES system. Marginal savings brought by PV and ES per kW (/kWh) capacity are analyzed to help the home owners size the components of the PV-ES system.

This paper presents the architecture and modeling approach of a Matlab-based toolbox for developing and testing home energy management (HEM) algorithms under a number of typical operation conditions. This toolbox serves as a developer platform that includes a graphical user interface, a model database, a computational engine, and an input-output database. The model database consists of home appliance models, energy storage models, baseload models, roof-top photovoltaic models, as well as typical weather profiles. The graphical interface allows the users to select different inputs such as utility rate structure, customer comfort settings, solar profiles, and outdoor temperature profiles. The HEM algorithms serve as the computational engines to power the virtual house so that the performance of those algorithms can be evaluated fairly using the same set of inputs and models. Simulation results have demonstrated the effectiveness of the modeling platform when developing HEM algorithms.

This paper presents the results of a temperature-load sensitivity study carried out at the Midcontinent Independent System Operator (MISO) for adjusting MISO day-ahead load forecast. As the area operated by MISO is growing rapidly, operators at MISO have to examine the day-ahead load forecast results generated by commercial software packages on a daily basis and manually correct anticipated discrepancies to enhance the forecast accuracy. To quantify the adjustment of the forecasted load, a series of temperature-load sensitivity studies are conducted using the actual MISO load data from past 5 years. A piecewise general linear model (GLM) is developed to capture the impact of time-dependent factors and temperature variations on the magnitude of MISO temperature-load sensitivity. A numerical study case compares the results before and after the tuning with the presented temperature-load sensitivity analysis.

This paper presents a probabilistic-based sizing tool for residential home owners, load serving entities, and utilities to select energy storage (ES) and photovoltaic (PV) based on historical load characteristics and load management options. The inputs of the tool include historical residential load profiles and solar radiation data. The outputs of the tool include ensembles of the net load profiles (load minus solar), with and without applying load energy management for different PV and ES installation capacities. The operation statistics of the ES is used to determine the confidence levels of meeting selected performance criterion. In the simulation, a set of 1-year, 15-minute data collected from 50 actual residential homes is used as the load inputs. A set of 1-year, 5-minute actual solar radiation data is used as the solar inputs. Managing load consumptions for reducing the size of ES is investigated by controlling air conditioning loads. Simulation results show that the probabilistic-based sizing method can give the users a clear comparison of the tradeoffs among different options and assist them make more informed decisions.

This paper presents the design and setup, considerations, and preliminary results of an AC/DC hybrid smart house test system. Rapid growth of rooftop photovoltaic (PV) in distribution systems makes it viable to supply a residential home with both AC and DC power sources. Solid-state transformers (SST) used as energy routers for rerouting power during normal and emergency situations are being developed in the FREEDM center at North Carolina State University. To provide a load test system for studying the control and monitoring of such an SST supplied AC/DC mixed power supply system, an 1-SST AC/DC hybrid smart house test system is built. The test system consists of a solid state transformer (SST), on-site photovoltaic panels, energy storage devices and three AC/DC smart houses with a home energy management system (HEMS). A demonstration case is presented to illustrate the operation of the test system for algorithm development and validation.

GO enrichment analysis of maize lincRNAs functioning as miRNA targets associated with “biological processes”. (PDF 153 kb)

Geothermal energy is a clean, green renewable resource, which can be utilized for power generation, heating, cooling, and could effectively replace oil, gas, and coal. In recent years, oil companies have put more efforts into exploiting and utilizing geothermal energy with advanced technologies for heat-tracing oil gathering and transportation, central heating, etc., which has not only reduced resource waste, but also improved large-scale and industrial resource utilization levels, and has achieved remarkable economic and social benefits. Based on the analysis of oilfield geothermal energy development status, resource potential, and exploitation and utilization modes, the advantages and disadvantages of harnessing oilfield geothermal resource have been discussed. Oilfield geothermal energy exploitation and utilization have advantages in resources, technical personnel, technology, and a large number of abandoned wells that could be reconstructed and utilized. Due to the high heat demand in oilfields, geothermal energy exploitation and utilization can effectively replace oil, gas, coal, and other fossil fuels, and has bright prospects. The key factors limiting oilfield geothermal energy exploitation and utilization are also pointed out in this paper, including immature technologies, lack of overall planning, lack of standards in resource assessment, and economic assessment, lack of incentive policies, etc.

Background Long intergenic noncoding RNAs (lincRNAs) are endogenous non-coding RNAs (ncRNAs) that are transcribed from ‘intergenic’ regions of the genome and may play critical roles in regulating gene expression through multiple RNA-mediated mechanisms. MicroRNAs (miRNAs) are single-stranded small ncRNAs of approximately 21–24 nucleotide (nt) that are involved in transcriptional and post-transcriptional gene regulation. While miRNAs functioning as mRNA repressors have been studied in detail, the influence of miRNAs on lincRNAs has seldom been investigated in plants. Methods LincRNAs as miRNA targets or decoys were predicted via GSTAr.pl script with a set of rules, and lincRNAs as miRNA targets were validated by degradome data. Conservation analysis of lincRNAs as miRNA targets or decoys were conducted using BLASTN and MAFFT. The function of lincRNAs as miRNA targets were predicted via a lincRNA-mRNA co-expression network, and the function of lincRNAs as miRNA decoys were predicted according to the competing endogenous RNA (ceRNA) hypothesis. Results In this work, we developed a computational method and systematically predicted 466 lincRNAs as 165 miRNA targets and 86 lincRNAs as 58 miRNA decoys in maize (Zea mays L.). Furthermore, 34 lincRNAs predicted as 33 miRNA targets were validated based on degradome data. We found that lincRNAs acting as miRNA targets or decoys are a common phenomenon, which indicates that the regulated networks of miRNAs also involve lincRNAs. To elucidate the function of lincRNAs, we reconstructed a miRNA-regulated network involving 78 miRNAs, 117 lincRNAs and 8834 mRNAs. Based on the lincRNA-mRNA co-expression network and the competing endogenous RNA hypothesis, we predicted that 34 lincRNAs that function as miRNA targets and 86 lincRNAs that function as miRNA decoys participate in cellular and metabolic processes, and play role in catalytic activity and molecular binding functions. Conclusions This work provides a comprehensive view of miRNA-regulated networks and indicates that lincRNAs can participate in a layer of regulatory interactions as miRNA targets or decoys in plants, which will enable in-depth functional analysis of lincRNAs. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2024-0) contains supplementary material, which is available to authorized users.