Search

The difficulty of deploying remaining oil from unconventional reservoirs and the increasing CO2 emissions has prompted researchers to delve into carbon emissions through Carbon Capture, Utilization, and Storage (CCUS) technologies. Under the confinement of nanopore in unconventional formation, CO2 and hydrocarbon molecules show different density distribution from in the bulk phase, which leads to a unique phase state and interface behavior that affects fluid migration. At the same time, mineral reactions, asphaltene deposition, and CO2 pressurization will cause the change of porous media geometry, which will affect the multiphase flow. This review highlights the physical and chemical effects of CO2 injection into unconventional reservoirs containing a large number of micro-nanopores. The interactions between CO2 and in situ fluids and the resulting unique fluid phase behavior, gas-liquid equilibrium calculation, CO2 adsorption/desorption, interfacial tension, and minimum miscible pressure (MMP) are reviewed. The pore structure changes and stress distribution caused by the interactions between CO2, in situ fluids, and rock surface are discussed. The experimental and theoretical approaches of these fluid-fluid and fluid-solid reactions are summarized. Besides, deficiencies in the application and safety assessment of CCUS in unconventional reservoirs are described, which will help improve the design and operation of CCUS.

Compared with the traditional fixed relay assisted communication network, the mobile relay assisted wireless communication network has greater communication coverage and better flexibility. It enables to extend the lifetime of a communication system and improve the throughput of uses. Due to the time-varying nature of the actual communication environment and the existence of various interferences, there are uncertain factors in the communication channel that affect the quality of service of users. In this paper, we study the energy efficiency maximization problem based on power control, mobile relay selection, and subcarrier allocation under uncertain channel. The proposed mobile relay selection and subcarrier allocation algorithm improves the energy efficiency of the system while ensuring the normal communication of mobile users. Probabilistic constraints are introduced into the optimization problem to describe the uncertain channel, and after the mathematical transformation, it becomes a solvable form. The subcarrier allocation algorithm and distributed robust power control algorithm are also presented. Simulation results show that the improved system energy efficiency has been achieved and the proposed algorithm behaves better scalability and robustness under dynamic communication environment.

Thermal management of Al composites is a crucial issue to keep the promising performance and excellent reliability of the devices. We revealed a novel method to enhance the surface property of graphite flakes (GF) with Cu and Ni coatings by chemical plating way. Taking advantage of Cu and Ni co-doped GF and silicon powders, the GF@Cu@Ni/Si/Al composites were fabricated by vacuum pressure infiltration method. The GF@Cu-30@Ni-10 composites revealed about 2 times larger TC (474.4 W/m·K) and lower CTE 6.22 ppm/K than neat Al. Our strategy for highly TC offers not only the advanced process for dispersing reinforcements but also an extremely simple way for the anisotropic composites with high TC and low CTE.

Three-dimensional (3D) flower-like Bi2SiO5 nanomaterials (FLB) and 3D flower-like Bi2SiO5@nitrogen doped graphene nanomaterials (FLBNG) have been originally synthesized. Notably, as-synthesized FLB and FLBNG nanomaterials have not yet been discussed in previous reports. The FLBNG nanomaterials have been for the first time studied as the cathodic catalysts for Fuel cell. Research results have shown that the FLBNG-2 possesses perfectly 3D flower-like Bi2SiO5 with numerous uniformly coiled nanowires and exhibits good methanol immunity, high activity and high durability during oxygen reduction reaction (ORR) process under acidic and basic electrolytes. The value of onset potential (E0) for FLBNG-2 in alkaline electrolyte can reach to 1.091 V, Tafel slope of FLBNG-2 is 45.637 mV dec−1 and its limiting current density (JL) is 6.49 mA cm−2. Similarly, These ORR performances in acidic electrolyte are also better than those of 20 wt% commercial Pt/C and most of the associated reports. Thus, the FLBNG-2 could be a potential ORR catalyst, applied in the cathode of fuel cells to enhance the kinetic rate, decrease costs and enhance durability. And this synthesis method may be provide a good design to synthesize other 3D flower-like nanomaterials with desirable properties.

Reasonable design of multi-component metal oxides hierarchical nanostructures is an effective and promising way to improve electrochemical performances of electrode materials. In this work, novel hydrangea-like ZnCo2O4/Ni3V2O8 hierarchical nanostructures are constructed by adjusting the ratio of ZnCo2O4 and Ni3V2O8. The perfect architecture maximizes the synergistic effect between different components and provides abundant electrochemical reactive sites, promoting the diffusion and transfer of electrolyte ions in electrochemical reaction process. As a result, ZnCo2O4/Ni3V2O8 electrodes deliver an enhanced specific capacitance of 1734 F g−1 at 1 A g−1 and cyclic stability (96% of initial capacitance is retained after 8000 cycles). Furthermore, the assembled ZnCo2O4/Ni3V2O8//AC device e achieves excellent energy density of 90 Wh kg−1 at power density of 812 W kg−1, proving the optimized hydrangea-like ZnCo2O4/Ni3V2O8 hierarchical nanostructures are promising electrode material for supercapacitor. In addition, this work supplies helpful insights into rational design of structure and composition of multi-component metal oxides for enhanced electrochemical performance.

The hierarchical hollow framework involving interconnected highly conductive N-doped carbon nanotube networks and CoS2 particles were successfully prepared by metal-organic framework (MOF) derived method. After the two pyrolysis process in the atmosphere of reducing gas and inert gases, numerous carbon nanotubes interlaced on the surface of framework and CoS2 nanoparticles also attached on the surface. The electromagnetic parameters of CoS2/NCNTs composites can be well controlled by regulating the loadings of sample in sample-paraffin mixture. The results demonstrate that CoS2/NCNTs with 50% loadings show superior electromagnetic wave absorption properties in the wide frequency range, almost covering the whole X bands (8–12 GHz) only with a relative thin thickness of 1.6 mm. Hierarchical hollow structure and better impedance matching performance between N-doped carbon nanocube and CoS2 nanoparticles contribute to the enhancement of microwave absorption ability. Our work confirms that hollow framework CoS2/NCNTs composites can provide a novel idea for designing high-absorbability microwave absorbers.

Rational construction of unique hierarchical nanostructured electrodes with excellent electrochemical performance is a promising solution to satisfy the increasing demand of supercapacitors with high energy density. In this work, a novel nanostructure of bowl-like MnO2 nanosheets with ultra-thin thickness (about 4 nm) has been successfully designed by a simple template-assisted hydrothermal method. The composition and morphology of the obtained bowl-like nanostructures have been characterized by FESEM, TEM, XRD, XPS and N2 adsorption-desorption. Such novel nanostructures present several advantages, such as large surface area, compact contacts, reduced interior space, additional electroactive sites for reversible redox reactions and short/unimpeded diffusion channels for ions/electron transport. When evaluated as electrode materials, bowl-like MnO2 nanosheets exhibit a specific capacitance of 379 F g−1 at a current density of 0.5 A g−1, a capacitance retained ratio of 60.5% from 0.5 to 10 A g−1 and 87.3% capacitance retention after 5000 cycles. Moreover, a possible mechanism for the excellent electrochemical performance of the novel bowl-like nanostructures compared with hollow particles has been systematically studied. We believe that this strategy can open up an avenue and appropriate idea for the rational construction of other transition metal oxides with high performance for the practical supercapacitor applications.

Purpose：To investigate the risk factors for calf venous thrombosis in patients with cerebral hemorrhage, so as to provide a reference for customized clinical decision.Methods：396 intracerebral hemorrhage (ICH) patients were enrolled in this retrospective study from January 2017 to December 2019. Risk factors and predictors of occurrence for Muscle calf venous thrombosis (MCVT) were summarized and analyzed by Chi-square test and logistic regression analysis.Results: 128 ICH patients with MCVT and 268 without ones were included. Demographic features comparison showed that ICH with MCVT patients showed older average age than those without ones (p=0.039). Co-morbid diseases analysis showed that patients with MCVT tend to complicate with Diabetes Mellitus (DM) (p=0.040). The comparison of laboratory examinations showed serum level of glucose (p=0.011), Fibrinogen (p=0.046) and D-Dimer (p=0.007) was significantly different. For clinical manifestations, the coma degree (GCS score) and muscle strength level were related to occurring of MCVT. Multivariate logistic regression identified higher age (OR=1.036, 95%CI =1.009-1.064, P= 0.008), higher D-Dimer level (OR=2.379, 95%CI =1.338-4.231, P= 0.003), low GCS (OR=0.932, 95%CI =0.868-0.998, P= 0.044) and lower affected side muscle strength (0-3 grade) (OR=1.624, 95%CI =1.009-2.614, P= 0.046) will predict higher incidence of MCVT in ICH patients.Conclusion: According to our study, higher age, higher D-Dimer level, lower GCS score and lower affected side muscle strength were associated with higher incidence of MCVT in ICH patients. Clinical doctors should pay more attention to prevent MCVT in ICH patients which coincide with those risk factors.

A simple synthesize method of hybrid transition metal oxides is important for the application of electrodes materials. In this work, we report one-pot hydrothermal method to synthesize 3D hierarchical MnMoO4/NiWO4 microspheres. Notably, the 3D hierarchical MnMoO4/NiWO4 microspheres electrodes with a MnMoO4/NiWO4 molar ratio of 4:3 present outstanding electrochemical performance. The excellent specific capacitance of the hybrid materials is 598 F g-1 at a discharge current density of 1 A g−1 and the capacitance retains 82% of its initial capacitance even after 5000 cycles, which are attributed to the obtained 3D hierarchical MnMoO4/NiWO4 microspheres are constituted by self-assembly MnMoO4 nanoflakes and NiWO4 nanoparticals and feature with abundant transportation shortcuts for electrolyte ions and excellent electrochemical performance. The results reveal that the 3D hierarchical MnMoO4/NiWO4 microspheres have great application prospect for electrochemical energy storage and the quick and facile method provides a new avenue for the preparation of hybrid materials.

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

The realization of the high energy density of lithium-sulfur batteries (Li-S) has always been one of the targets pursued by researchers. However, the shuttling effect, dissolution of sulfur/lithium polysulfides (LiPSs), and sluggish redox kinetics in Li-S batteries restricted its long-term application and development. Herein, an advanced sulfur immobilizer composed of SnP0.94 nanodot electrocatalysts@porous hollow carbon aerogel (SnP0.94@PHCA) was synthesized. As expected, the synergistic sulfur host can not only act as a good electrochemical interface but also provide adequate accommodation for active materials to alleviate the LiPS diffusion. More importantly, SnP0.94 nanodot electrocatalysts supported on the conductive PHCA skeleton effectively expedited the conversion/redox reaction kinetics and also constructed a stronger adsorption system for LiPSs to inhibit the LiPS shuttling behavior. Correspondingly, the SnP0.94@PHCA composite endowed the sulfur cathode the outstanding specific capacity of 1017.8 mAh g−1 under a high sulfur loading (2.94 mg cm−2) at 0. 1 C, superior rate performance (579.8 mAh g−1 at 1 C), excellent cycling stability (the capacity decay rate of 0.13% per cycle at 0.5 C), and a high areal capacity of 6.9 mAh cm−2 under starved electrolyte conditions (8.6 μL mgs−1). This work opens up a new direction for constructing the electroactive hosts in Li-S batteries.

In the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information and connect it to precipitation estimates. In this study, the effectiveness of the state-of-the-art deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels, and to produce rain/no-rain (R/NR) detection is explored. To verify the methodologies, two models are designed and evaluated: the first model, referred to as the DL-IR only method, applies deep learning approaches to the IR data only; the second model, referred to as the DL-IR+WV method, incorporates WV data to further improve the precipitation identification performance. The radar stage IV data are the reference data used as ground observation. The operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), serves as a baseline model with which to compare the performances. The experiments show significant improvement for both models in R/NR detection. The overall performance gains in the critical success index (CSI) are 21.60% and 43.66% over the verification periods for the DL-IR only model and the DL-IR+WV model compared to PERSIANN-CCS, respectively. In particular, the performance gains in CSI are as high as 46.51% and 94.57% for the models for the winter season. Moreover, specific case studies show that the deep learning techniques and the WV channel information effectively help recover a large number of missing precipitation pixels under warm clouds while reducing false alarms under cold clouds.

The effect of RE2O3 (RE = La, Ce) fluxes on penetration and microstructure in 3-mm-thick Ti6Al4V weld joints made by activated flux tungsten inert gas (A-TIG) welding process was investigated. The mechanism of penetration increase was discussed. Microstructure characterization in the fusion zone region was also observed under optical microscope. It is shown that RE2O3 fluxes can effectively increase weld penetration of Ti6Al4V for applying a thin layer paste when welding. The mechanism would comply with changing the Marangoni convection in the weld pool, rather than the arc. A-TIG welding with RE2O3 fluxes can slightly refine the width of β-Ti grains. Phase constitution shows single α′-Ti in solidification structure in both two welding process. There are no significant changes in weld metal microstructure with and without Re2O3 fluxes. Microhardness distribution of weld seam indicates that the appearance of RE2O3 has not affected the metal plastic deformation capacity.

Thermal management is a crucial issue for the reliability and life of the electronic devices. In this study, we successfully fabricated aluminum (Al) matrix composites with Cu networks (CNs) using a straightforward way. Graphite flakes (GFs) and CNs coated with graphene (CNs@Gr) were used to improve the performance of Al-matrix composites by hot-pressing method. There-dimensional (3D) CNs@Gr in the composites displayed an effective thermal transfer pathway in the matrix. The thermal conductivity (TC) of Al/GFs/CNs@Gr-5 composites (482.14 W/m·K) at a low Gr content, which has increased about 103.4% compared to pure Al. The synthesized CNs@Gr networks with continuous dendritic structure can serve as high-efficiency thermal transfer pathways to enhance heat dissipation through Gr layers, which reduced interfacial resistance for phonon scatterings. Furthermore, all fabricated composites displayed a lower coefficient of thermal expansion (less than 2.5 × 10−5) (CTE) than pure Al matrix (below 340℃). As a result, the construction of 3D networks in Al-matrix composites with high comprehensive performance makes them promising thermal management materials.

Non-small cell lung cancer (NSCLC) accounts for 85% of all types of lung cancer and is the leading cause of world-wide cancer-associated mortalities. Radiation therapy has long been regarded as a fundamental therapeutic treatment strategy for NSCLC. However, alternative therapies for NSCLC remain insufficient, with the majority of cancers developing a high incidence of radioresistance. MicroRNAs (miRNAs/miRs) are endogenous oligonucleotide RNAs that serve an important role in carcinogenesis and tumor progression. In the present study, a novel function of miR-133b that is associated with the radiosensitivity of lung cancer cells is reported. miR-133 was downregulated in radioresistant lung cancer cells, which exhibited an elevated glycolysis rate when compared with radiosensitive cells. Additionally, it was observed that pyruvate kinase isoform M2 (PKM2) is a target of miR-133b, and that the expression of PKM2 is positively correlated with radioresistance. Finally, it was demonstrated that overexpression of miR-133b resensitizes radioresistant lung cancer cells through the inhibition of PKM2-mediated glycolysis. The current study may indicate a novel function of miR-133b, potentially aiding the development of anticancer therapeutics.

Author(s): Yang, T; Gao, X; Sorooshian, S; Li, X | Abstract: The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others.

Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework of a deep neural network (DNN) that improves the accuracy of satellite precipitation products, focusing on reducing the bias and false alarms. The state-of-the-art deep learning techniques developed in the area of machine learning specialize in extracting structural information from a massive amount of image data, which fits nicely into the task of retrieving precipitation data from satellite cloud images. Stacked denoising autoencoder (SDAE), a widely used DNN, is applied to perform bias correction of satellite precipitation products. A case study is conducted on the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) with spatial resolution of 0.08° × 0.08° over the central United States, where SDAE is used to process satellite cloud imagery to extract information over a window of 15 × 15 pixels. In the study, the summer of 2012 (June–August) and the winter of 2012/13 (December–February) serve as the training periods, while the same seasons of the following year (summer of 2013 and winter of 2013/14) are used for validation purposes. To demonstrate the effectiveness of the methodology outside the study area, three more regions are selected for additional validation. Significant improvements are achieved in both rain/no-rain (R/NR) detection and precipitation rate quantification: the results make 33% and 43% corrections on false alarm pixels and 98% and 78% bias reductions in precipitation rates over the validation periods of the summer and winter seasons, respectively.

Due to high theoretical specific capacity, energy density, and low-cost lithium-sulfur (Li-S) batteries became the most potential energy storage devices for the next generation. The harsh shuttling effect, poor electroconductibility of sulfur, and volume change affect the Li-S battery’s practical application. Herein, the rational design of porous hollow carbon aerogel (PHCA) with an ultra-high specific surface area of 2054.5 m2 g−1 and outstanding adsorption ability is firstly fabricated to restrict the shuttling effect of lithium polysulfides (LiPSs) by using commercialized nano-CaCO3 as a template. As a sulfur host, the S/PHCA cathode delivers initially discharge specific capacity of 992.7 mAh g−1 with an average Coulombic efficiency of 97.6%. After 200 cycles, it obtains the reversible specific capacities of 600.1 mAh g−1 for S/PHCA cathode at 0.1C (2.2 mg cm−2) corresponding to 60.45% of their initial discharge specific capacity. As a PHCA interlayer, the Li-S battery with the PHCA interlayer exhibits initially discharge specific capacity of 1267.7 mAh g−1 with an average Coulombic efficiency of 99.36% at 0.1C (3.2 mg cm−2) and maintains at 863.3 mAh g−1 with the discharge specific capacity retention rate of 69.2%. This is an effective strategy to improve the outstanding electrochemical performance which is beyond reported carbon materials, which is attributed to stronger adsorption of LiPSs by the micropore. This work provides a new direction for the modification of a sulfur host and interlayer and simultaneously reveals the relationship between the heteroatom distribution and LiPSs.

Uniform Ag and Si layers were successfully prepared on the surface of graphite films (GF) to improve thermal conductivity (TC) by physical vapor deposition method (PVD). The results demonstrate that the unique cubic crystal structure of Ag layer with different deposition time show good interface bonding and no carbide product of Ag/GF composites. Meanwhile, the dense Si coating contribute to the enhancement of the thermal diffusion ability. The Ag/GF composites with the deposition time of 2 h possess the best comprehensive property with the TC of 9.93 (W/m·K), which is approximately 73.30% higher than untreated GF. The TC of Si/GF composites reach the maximum TC of 11.12 W/(m·K) and increase about 101.81% due to the nano-Si layer comparing with untreated GF. Those composites with high performance make them promising thermal management laminated materials.

MiRNAs are a class of endogenous, short, single-stranded, non-coding RNAs, which are tightly linked to cardiac disorders such as myocardial ischemia/reperfusion (I/R) injury. MiR-34a is known to be involved in the hypoxia-induced cardiomyocytes apoptosis. However, the molecular mechanisms are unclear. In the present study, we demonstrate that under low glucose supply, rat cardiomyocytes are susceptible to hypoxia. Under short-time hypoxia, cellular glucose uptake and lactate product are induced but under long-time hypoxia, the cellular glucose metabolism is suppressed. Interestingly, an adaptive up-regulation of miR-34a by long-time hypoxia was observed both in vitro and in vivo, leading to suppression of glycolysis in cardiomyocytes. We identified lactate dehydrogenase-A (LDHA) as a direct target of miR-34a, which binds to the 3′-UTR region of LDHA mRNA in cardiomyocytes. Moreover, inhibition of miR-34a attenuated hypoxia-induced cardiomyocytes dysfunction through restoration of glycolysis. The present study illustrates roles of miR-34a in the hypoxia-induced cardiomyocytes dysfunction and proposes restoration of glycolysis of dysfunctional cardiomyocytes by inhibiting miR-34a during I/R might be an effectively therapeutic approach against I/R injury.

Studies have been reported about the efficacy of satellites for measuring precipitation and about quantifying their errors. Based on these studies, the errors are associated with a number of factors, among them, intensity, location, climate, and season of the year. Several error models have been proposed to assess the relationship between the error and the rainfall intensity. However, it is unknown whether these models are adaptive to different seasons, different regions, or different types of satellite-based estimates. Therefore, how the error–intensity relationship varies with the season or region is unclear. To investigate these issues, a parametric joint pdf model is proposed to analyze and study the 9-yr satellite-derived precipitation datasets of Climate Prediction Center (CPC) morphing technique (CMORPH); PERSIANN; and the real-time TRMM product 3B42, version 7 (TRMM-3B42-RTV7). The NEXRAD Stage IV product is the ground reference. The adaptability of the proposed model is verified by applying it to three locations (Oklahoma, Montana, and Florida) and by applying it to cold season, warm season, and the entire year. Then, the heteroscedasticities in the errors of satellite-based precipitation measurements are investigated using the proposed model under those scenarios. The results show that the joint pdfs have the same formulation under these scenarios, whereas their parameter sets were adaptively adjusted. This parametric model reveals detailed information about the spatial and seasonal variations of the satellite-based precipitation measurements. It is found that the shape of the conditional pdf shifts across the intensity ranges. At the ~10–20 mm day−1 range, the conditional pdf is L shaped, while at the ~40–60 mm day−1 range, it becomes more bell shaped. It is also concluded that no single satellite-based precipitation product outperforms others with respect to the different scenarios (i.e., seasons, regions, and climates).

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California. A new multi objective optimization algorithm, entitled MOSPD, is developed.Comparison study is carried out over eight complex test functions.MOSPD is effective and efficient in searching global Pareto optimal.A reservoir system model is built for Oroville-Thermalito complex in California.MOSPD provides flexible reservoir release strategies to support decision making.

This manuscript introduces a novel computational science approach for studying the impact of climate variability on precipitation. The approach uses an object-oriented connectivity algorithm that segments gridded near-global satellite precipitation data into four-dimensional (4D) objects (longitude, latitude, time, and intensity). These precipitation systems have distinct spatiotemporal properties that are counted, tracked, described, and stored in a searchable database. A case study of western United States precipitation systems is performed, demonstrating the unique properties and capabilities of this object-oriented database. The precipitation dataset used in the case study is the University of California, Irvine, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) from 1 March 2000 to 1 January 2011. A search of the database for all western United States precipitation systems during this time period returns 626 precipitation systems as objects. By analyzing these systems as segmented objects, joint interactions of the selected climate phenomena 1) Arctic Oscillation (AO), 2) Madden–Julian oscillation (MJO), and 3) El Niño–Southern Oscillation (ENSO) on precipitation can be shown. They directly show the increased/decreased likelihood of having precipitation systems occurring over the western United States (monthly count) during phases of these climate phenomena. It is found that specific climate phenomena impact the monthly count of the events differently, and that the joint interaction of climate phenomena of the AO–MJO and AO–ENSO is important, especially during certain months of the year. It is also found that these interactions impact the physical features of the precipitation systems themselves.

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones.

Three popular evolutionary optimization algorithms are tested on high-dimensional benchmark functions. An important phenomenon responsible for many failures - “population degeneration” - is discovered. That is, through evolution, the population of searching particles degenerates into a subspace of the search space, and the global optimum is exclusive from the subspace. Subsequently, the search will tend to be confined to this subspace and eventually miss the global optimum. Principal components analysis (PCA) is introduced to discover population degeneration and to remedy its adverse effects. The experiment results reveal that an algorithm's efficacy and efficiency are closely related to the population degeneration phenomenon. Guidelines for improving evolutionary algorithms for high-dimensional global optimization are addressed. An application to highly nonlinear hydrological models demonstrates the efficacy of improved evolutionary algorithms in solving complex practical problems.

Objective: This work was designed to investigate the protection of cannabidiol (CBD) against palmitic acid (PA)-induced injury in cultured hepatocytes and the underlying mechanism associated with autophagic flux. Methods: Experiment 1: Primary cultured hepatocytes were stimulated with PA (800 μmol/L) and treated with CBD (5 μmol/L) and chloroquine (CQ, 50 nmol/L) or not for 24 hours (1: control group; 2: PA-stimulated group; 3: PA-stimulated group treated with CBD; 4: PA-stimulated group treated with CBD and CQ). Autophagic flux was evaluated by Western blot analysis. Apoptosis was measured by flow cytometry. The mRNA expression of genes involved in endoplasmic reticulum stress was determined by reverse transcription PCR. The mitochondrial function was determined by using fluorescent probe including Rh123 and lucigenin. Experiment 2: Primary cultured hepatocytes were treated with CBD alone for 24 h (1: control group; 2: lower-dose CBD-treated group; 3: higher-dose CBD-treated group). Then, the autophagic flux was evaluated by Western blot analysis. Results: When compared to control group, exposure to PA significantly led to impaired autopagic flux (evidenced by increased ratio of LC3-II/LC3-I and protein expression of p62), increased apoptosis, endoplasmic reticulum stress (evidenced by increased mRNA expression of C/EBP homologous protein, glucose-regulated protein 78, and X-box protein 1), and mitochondrial dysfunction (evidenced by reduced mitochondrial membrane potential and enhanced formation of mitochondrial reactive oxygen species). When compared to PA-stimulated group, CBD treatment significantly attenuated PA-induced impaired autophagic flux, apoptosis, endoplasmic reticulum stress, and mitochondrial dysfunction in cultured hepatocytes. The protection of CBD against PA was abolished by co-incubation with CQ. In addition, treatment with CBD alone had no significant effect on autophagic flux in cultured hepatocytes Conclusion: Cannabidiol attenuates palmitic acid-induced impaired autophagic flux, apoptosis, endoplasmic reticulum stress, and mitochondrial dysfunction in cultured hepatocytes through promoting autophagic flux.

This study downscaled more than five years of data (1999–2004) for hydrometeorological fields over the upper Rio Grande basin (URGB) to a 4-km resolution using a regional model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5, version 3)] and two forcing datasets that include National Centers for Environmental Prediction (NCEP)–NCAR reanalysis-1 (R1) and North America Regional Reanalysis (NARR) data. The long-term high-resolution simulation results show detailed patterns of hydroclimatological fields that are highly related to the characteristics of the regional terrain; the most important of these patterns are precipitation localization features caused by the complex topography. In comparison with station observational data, the downscaling processing, on whichever forcing field is used, generated more accurate surface temperature and humidity fields than the Eta Model and NARR data, although it still included marked errors, such as a negative (positive) bias toward the daily maximum (minimum) temperature and overestimated precipitation, especially in the cold season. Comparing the downscaling results forced by the NARR and R1 with both the gridded and station observational data shows that under the NARR forcing, the MM5 model produced generally better results for precipitation, temperature, and humidity than it did under the R1 forcing. These improvements were more apparent in winter and spring. During the warm season, although the use of NARR improved the precipitation estimates statistically at the regional (basin) scale, it substantially underestimated them over the southern upper Rio Grande basin, partly because the NARR forcing data exhibited warm and dry biases in the monsoon-active region during the simulation period and improper domain selection. Analyses also indicate that over mountainous regions, both the Climate Prediction Center’s (CPC’s) gridded (0.25°) and NARR forcings underestimate precipitation in comparison with station gauge data.

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002–2003 cool season (1 November–31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as “truth.” The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small.

Estimating the water budgets in a small-scale basin is a challenge, especially in the mountainous western United States, where the terrain is complex and observational data in the mountain areas are sparse. This manuscript reports on research that downscaled 5-yr (1999–2004) hydrometeorological fields over the upper Rio Grande basin from a 2.5° NCEP–NCAR reanalysis to a 4-km local scale using a regional climate model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3]. The model can reproduce the terrain-related precipitation distribution—the trend of diurnal, seasonal, and interannual precipitation variability—although poor snow simulation caused it to overestimate precipitation and evapotranspiration in the cold season. The outcomes from the coupled model are also comparable to offline Variable Infiltration Capacity (VIC) and Land Data Assimilation System (LDAS)/Mosaic land surface simulations that are driven by observed and/or analyzed surface meteorological data.

This study examines the capabilities and limitations of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) in predicting the precipitation and circulation features that accompanied the 2004 North American monsoon (NAM). When the model is reinitialized every 5 days to restrain the growth of modeling errors, its results for precipitation checked at subseasonal time scales (not for individual rainfall events) become comparable with ground- and satellite-based observations as well as with the NAM’s diagnostic characteristics. The modeled monthly precipitation illustrates the evolution patterns of monsoon rainfall, although it underestimates the rainfall amount and coverage area in comparison with observations. The modeled daily precipitation shows the transition from dry to wet episodes on the monsoon onset day over the Arizona–New Mexico region, and the multiday heavy rainfall (>1 mm day−1) and dry periods after the onset. All these modeling predictions agree with observed variations. The model also accurately simulated the onset and ending dates of four major moisture surges over the Gulf of California during the 2004 monsoon season. The model reproduced the strong diurnal variability of the NAM precipitation, but did not predict the observed diurnal feature of the precipitation peak’s shift from the mountains to the coast during local afternoon to late night. In general, the model is able to reproduce the major, critical patterns and dynamic variations of the NAM rainfall at intraseasonal time scales, but still includes errors in precipitation quantity, pattern, and timing. The numerical study suggests that these errors are due largely to deficiencies in the model’s cumulus convective parameterization scheme, which is responsible for the model’s precipitation generation.

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as “truth,” with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800–0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill.

The aim of this study was to validate a criteria-specific long-term survival prediction model (MHCAT) in a large cohort of hepatocellular carcinoma (HCC) patients after liver transplantation (LT) in China. Independent risk factors in MHCAT were retrospectively analysed for HCC patients recorded in the China Liver Transplant Registry. Survival predictions for each patient were calculated using MHCAT scores and the Metroticket formula separately and the prediction efficacy of MHCAT and Metroticket was compared using the area under ROC curve (c-statistic). A total of 1371 LTs for HCC were analysed in the study, with a median follow-up of 22.2 months (IQR 6.1–72.4 months). The proportions meeting the Milan, UCSF, Fudan and Hangzhou criteria were 34.4%, 39.7%, 44.2% and 51.9%, respectively. The c-statistics for MHCAT predictions of 3- and 5-year survival rates of HCC recipients were 0.712–0.727 and 0.726–0.741, respectively. Among these patients, 1298 LTs for HCC were ultimately selected for the comparison analysis for prediction efficacy. The c-statistic of MHCAT for predictions of 3-year survival with reference to the Milan, UCSF and Fudan criteria was significantly increased compared with that for Metroticket (p

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside.