In recent years, high utility itemsets (HUIs) mining has been an active research topic in data mining. In this study, we propose two efficient pattern-growth based HUI mining algorithms, called High ...Utility Itemset based on Length and Tail-Node tree (HUIL-TN) and High Utility Itemset based on Tail-Node tree (HUI-TN). These two algorithms avoid the time-consuming candidate generation stage and the need of scanning the original dataset multiple times for exact utility values. A novel tree structure, named tail-node tree (TN-tree) is proposed as a key element of our algorithms to maintain complete utililty-information of existing itemsets of a dataset. The performance of HUIL-TN and HUI-TN was evaluated against state-of-the-art reference methods on various datasets. Experimental results showed that our algorithms exceed or close to the best performance on all datasets in terms of running time, while other algorithms can only excel in certain types of dataset. Scalability tests were also performed and our algorithms obtained the flattest curves among all competitors.
Aqueous zinc-ion batteries, in terms of integration with high safety, environmental benignity, and low cost, have attracted much attention for powering electronic devices and storage systems. ...However, the interface instability issues at the Zn anode caused by detrimental side reactions such as dendrite growth, hydrogen evolution, and metal corrosion at the solid (anode)/liquid (electrolyte) interface impede their practical applications in the fields requiring long-term performance persistence. Despite the rapid progress in suppressing the side reactions at the materials interface, the mechanism of ion storage and dendrite formation in practical aqueous zinc-ion batteries with dual-cation aqueous electrolytes is still unclear. Herein, we design an interface material consisting of forest-like three-dimensional zinc-copper alloy with engineered surfaces to explore the Zn plating/stripping mode in dual-cation electrolytes. The three-dimensional nanostructured surface of zinc-copper alloy is demonstrated to be in favor of effectively regulating the reaction kinetics of Zn plating/stripping processes. The developed interface materials suppress the dendrite growth on the anode surface towards high-performance persistent aqueous zinc-ion batteries in the aqueous electrolytes containing single and dual cations. This work remarkably enhances the fundamental understanding of dual-cation intercalation chemistry in aqueous electrochemical systems and provides a guide for exploring high-performance aqueous zinc-ion batteries and beyond.
•Real-time multi-forward-step prediction for battery state of charge is investigated.•Various battery states under complex real-world operation conditions can be learned.•A yearlong dataset of an ...electric taxi is employed for training a full-climate and full-state model.•Prediction accuracy and horizon can be collaboratively controlled based on accuracy benchmark.•Predictable and sufficient predicted time can be available to eliminate driving mileage anxiety.
Prediction of state of charge (SOC) is critical to the reliability and durability of battery systems in electric vehicles. The existing techniques are mostly model-based SOC estimation using experimental data, which are inefficient for learning the unpredictable battery state under complex real-world operating conditions of electric vehicles. This paper presents a novel machine-learning-enabled method to perform real-time multi-forward-step SOC prediction for battery systems using a recurrent neural network with long short-term memories (LSTM). The training results from a yearlong dataset show that the offline LSTM-based model can perform fast and accurate multi-forward-step prediction for battery SOC. Furthermore, the model has excellent practical application effects by taking into account weather and drivers’ driving behaviors during real vehicular operating, and the stability, flexibility, and robustness of this method are verified by 10-fold cross-validation. In order to achieve dual control of prediction accuracy and prediction horizon, we proposed an LRLSTM-based joint-prediction strategy while using LSTM and multiple linear regression algorithms, through which an accuracy benchmark can be obtained, and the prediction steps of LSTM within acceptable accuracy can be flexibly controlled. The smooth implementation of multi-forward-step SOC prediction on real-word vehicles can eliminate drivers’ mileage anxiety and safeguard vehicle operation in a big way.
AbstractIn the context of probabilistic analysis involving uncertain factors, efficient reliability methods play an important role for promoting a wider application in engineering practice. Although ...the ordinary Monte Carlo simulation (MCS) can simulate the probabilistic performance of a complex engineering system well and has been widely-employed in reliability analysis because of its simplicity and accuracy, the unavoidable computational burden to ensure sufficient accuracy often limits its use as a reference tool only for academic purposes. This paper proposes a modified weighted uniform simulation (WUS) method for reliability analysis involving nonnormal random variables, in which the Nataf transformation is adopted to effectively transform the correlated nonnormal variables into independent standard normal variables. This method takes into account the correlations between random variables, while the sample size is greatly reduced under the same accuracy requirements. Four examples of reliability analysis are presented to demonstrate the feasibility of the WUS method. It is shown that the proposed method can yield sufficiently accurate reliability analysis results with a reasonably small sample size compared to ordinary MCS. In particular, the most probable failure point (MPP), which is the basis for reliability-based design works, can also be directly obtained during the simulation process.
A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state ...is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.
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•Ore geology, distribution, magmatic affinity, and relations to porphyry, high-sulfidation, and low-sulfidation deposits of intermediate sulfidation epithermal (IS) deposits are ...reviewed.•Deep and slow fluids exsolving is favorable for the occurrence of IS deposits.•An empirical subclassification (NC-type IS in neutral-compressive settings and E-type IS in extensional settings) of IS deposits is proposed.•The subtypes of IS are mainly controlled by the source of parental magma.
Intermediate sulfidation (IS) veins is one of the subtypes of epithermal deposits formed in subduction-related arc settings or post-collisional orogenic belts. The economic and scientific significance of IS deposits has been highlighting importance in Ag-Au-Pb-Zn exploration and study of porphyry-epithermal systems. This epithermal clan of deposits typically have a close relationship with andesitic-dacitic volcanic-subvolcanic rocks, and formed at a depth of ∼0.3 to as much as 1 + km. IS deposits are typically related to oxidized calcic to calc-alkaline magmatism. Fluid homogenization temperatures and salinities range between 150 and 350 °C, and 0 and 23 wt% NaCl equivalent, respectively. The O and H isotope compositions are consistent with a mixture of magmatic and meteoric water, with an increase in meteoric diluent as the hydrothermal system wanes. Most of the IS deposits in the world, particularly those in Circum-Pacific metallogenic belts, formed during Cenozoic time. Several Mesozoic and Paleozoic IS deposits in the Central Asian Orogenic Belt imply great exploration potential for pre-Cenozoic IS deposits in this area.
The presence of Mn-carbonate such as rhodochrosite and manganocalcite (locally Mn-silicate, e.g., rhodonite, helvite) typically in mid to late hydrothermal stages is a common diagnostic feature to discern IS from low-sulfidation (LS) deposits. In addition, the occurrence of intermediate-sulfidation state sulfides such as pyrite, chalcopyrite, sphalerite, galena, and tetrahedrite/tennantite associations are another indicator of the IS type; light-colored (Fe-poor) sphalerite is typical of IS deposits, consistent with relatively oxidized fluids. Elevated fluid salinity is another characteristic, with maximum salinity values of base metal-rich IS veins usually >5 wt% NaCl equiv.
The reported IS deposits worldwide show that they develop in compressional volcanic arcs as well as in some extensional settings. In this review, IS deposits are subdivided into “NC (Neutral-Compressional)-type IS” with a low Ag/Au ratio (<60), formed in neutral to compressive stress state volcanic arcs, and “E (Extensional)-type IS”, on the contrary, with a high Ag/Au ratio (>60), formed in extensional settings such as extensional intra-arc, post-collisional orogenic belts, and back-arc settings. Another notable feature of E-type IS deposits is their large Ag endowment compared to NC-type IS. NC-type IS (Au ± Ag) deposits can be associated with porphyry Cu-Au and/or high sulfidation (HS) Au-Cu deposits, and their economic metals are mainly gold and/or silver. By contrast, some E-type IS deposits can occur on the flanks of porphyry molybdenum deposits; E-type IS veins can also occur together with LS precious metal veins in back arcs or extensional continental margins, the most representative examples occurring in Mexico. The occurrence of the two subtypes of IS are largely controlled by the parent magma, with parent magma of NC-type IS primarily derived from depleted mantle or juvenile crust, while parent magma of E-type IS mainly from (ancient?) continental crust.
The occurrence of IS deposits is presumably controlled by tectono-magmatic settings and fluid evolution paths. Neutral to compressive stress regime, relatively great depth to an exsolving magma (>4 km) and low exsolution rate of magmatic fluids, plus the presence of syn-ore dikes in conjunction with the development of interconnected fracture networks above the porphyry stock could be conducive for the occurrence of IS (and also HS) Au veins upon porphyry copper deposits (PCDs). Confirmation of sub-types and variations of IS veins can aid in exploration for spatially and genetic-related mineralization types, such as porphyry and HS deposits.
Abandoned houses (AH) are focal points in urban communities by threatening local security, destroying housing markets, and burdening government finance in the U.S. legacy cities. In particular, ...individual-level AH detection provides essential information for fine-resolution urban studies, government decision-makers, and private sector practitioners. However, three primary conventional data sources (field data, utility data, and remote sensing data) cannot suffice to collect such fine-resolution data in the large spatial area via a cost-effective approach. To this end, Google Street View (GSV) imagery, which emerges as the mainstream open-access data source with global coverage, provides an opportunity to address this issue. Subsequently, a follow-up challenge confronting the detection of AH arises from the fact that it lacks an effective method that can discern authentic visual features from the redundant noise in GSV images. In this study, we aim to develop an effective method to detect individual-level AH from GSV imagery. Specifically, we developed a new hierarchical deep learning method to leverage both global and local visual features of AH in the detection. The method can be further divided into three steps: (1) Scene-based classification that can extract global visual features of AH was implemented through fine-tuning a pre-trained deep convolutional neural network (CNN) model. (2) We developed a patch-based classification method that can extract specific local features of AH. In this method, patches were generated from GSV images based on auto-detected local features, followed by being labeled as three categories: building patches, vegetation patches, and others. Two deep CNN models were employed to identify deteriorated building façade patches and overgrown vegetation patches, respectively. (3) Individual-level AH were detected by integrating scene classification results and patch classification results in a decision-tree model. Experimental results showed that the F-score of AH was 0.84 in a well-prepared dataset collected from five different Rust Belt cities. The proposed hierarchical deep learning approach effectively improved the accuracy comparing with the traditional scene-based method. In addition, the proposed method was applied to generate an AH map in a new site in Detroit, MI. Our study demonstrated the feasibility of GSV imagery in AH detection and showed great potential to detect AH in a large spatial extent.
•Two common tests for observing OCV performance of NMC and LFP cells are studied.•The temperature, age and relaxation time dependency of the OCV-SoC is investigated.•A parameters and state of charge ...joint estimation method is presented.•The proposed joint estimation method is verified in terms of accuracy and robustness.•The incremental OCV test is recommended to determine the OCV-SoC relationship.
The open circuit voltage (OCV) is of essential importance for accurate estimation of the state of charge (SoC) in lithium-ion battery (LiB). The OCV-SoC relationship is typically predetermined by fitting offline OCV data. Commonly used two OCV tests are compared in few literatures. Moreover, they only focus on the middle SoC region (i.e., 20% and 90%) of LiNiMnCoO2 (NMC) LiBs, the performances of these OCV tests for other battery types and entire SoC region are failed to be addressed. In this paper, the impact of two OCV tests on SoC estimation for NMC and LiFePO4 (LFP) LiBs is investigated at different temperatures and aging stages. A parameter and SoC joint estimation method is introduced, based on an integrated H∞-UKF method. The accuracy and reliability of the proposed method are verified by using two different OCV testing data at various ambient temperatures and aging stages for some commercial NMC and LFP LiBs. The results indicate that the incremental OCV test method results in more accurate SoC estimation than the low current OCV test method, on both NMC and LFP LiBs. Furthermore, to reach equilibrium states and achieve desired SoC estimation accuracy, the relaxation period in the incremental OCV test method needs to be extended at low temperatures.
CRISPR/Cas9 system is a powerful toolbox for gene editing. However, the low delivery efficiency is still a big hurdle impeding its applications. Herein, we report a strategy to deliver Cas9‐sgPlk‐1 ...plasmids (CP) by a multifunctional vehicle for tumor therapy. We condensed CPs on TAT peptide‐modified Au nanoparticles (AuNPs/CP, ACP) via electrostatic interactions, and coated lipids (DOTAP, DOPE, cholesterol, PEG2000‐DSPE) on the ACP to form lipid‐encapsulated, AuNPs‐condensed CP (LACP). LACP can enter tumor cells and release CP into the cytosol by laser‐triggered thermo‐effects of the AuNPs; the CP can enter nuclei by TAT guidance, enabling effective knock‐outs of target gene (Plk‐1) of tumor (melanoma) and inhibition of the tumor both in vitro and in vivo. This AuNPs‐condensed, lipid‐encapsulated, and laser‐controlled delivery system provides a versatile method for high efficiency CRISPR/Cas9 delivery and targeted gene editing for treatment of a wide spectrum of diseases.
A multifunctional vehicle for tumor therapy based on the delivery of Cas9‐sgPlk‐1 plasmids was developed. This AuNP‐condensed, liposome‐encapsulated, and laser‐controlled drug delivery system provides a versatile method for high efficiency CRISPR/Cas9 delivery and targeted gene editing for photothermal treatment of a wide spectrum of diseases (AuNP=gold nanoparticle).
The current research of state of charge (SoC) online estimation of lithium-ion battery (LiB) in electric vehicles (EVs) mainly focuses on adopting or improving of battery models and estimation ...filters. However, little attention has been paid to the accuracy of various open circuit voltage (OCV) models for correcting the SoC with aid of the ampere-hour counting method. This paper presents a comprehensive comparison study on eighteen OCV models which cover the majority of models used in literature. The low-current OCV tests are conducted on the typical commercial LiFePO
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/graphite (LFP) and LiNiMnCoO
2
/graphite (NMC) cells to obtain the experimental OCV-SoC curves at different ambient temperature and aging stages. With selected OCV and SoC points from experimental OCV-SoC curves, the parameters of each OCV model are determined by curve fitting toolbox of MATLAB 2013. Then the fitting OCV-SoC curves based on diversified OCV models are also obtained. The indicator of root-mean-square error (RMSE) between the experimental data and fitted data is selected to evaluate the adaptabilities of these OCV models for their main features, advantages, and limitations. The sensitivities of OCV models to ambient temperatures, aging stages, numbers of data points, and SoC regions are studied for both NMC and LFP cells. Furthermore, the influences of these models on SoC estimation are discussed. Through a comprehensive comparison and analysis on OCV models, some recommendations in selecting OCV models for both NMC and LFP cells are given.
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