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Predicting the next destination is a key in human mobility behavior modeling, which is significant in various fields, such as epidemic control, urban planning, traffic management and recommendation. To achieve this, one typical solution is designing modules based on RNN to capture their preferences to various locations. Although these RNN-based methods can effectively learn individual’s hidden personalized preferences to her visited places, the interactions among users can only be weakly learned through the representations of locations. Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG2Net), considering the users’ preferences to various places at both individual and collective levels. Specifically, PG2Net concatenates Bi-LSTM and attention mechanism to capture each user’s long-term mobility tendency. To learn population’s group preferences, we utilize spatial and temporal information of the visitations to construct a spatial-temporal dependency module. We adopt a graph embedding method to map users’ trajectory into a hidden space, capturing their sequential relation. In addition, we devise an auxiliary loss to learn the vectorial representation of her next location. Experimental results on two Foursquare check-in datasets and one mobile phone dataset indicate the advantages of our model compared to the state-of-the-art baselines. Source code is available at https://github.com/urbanmobility/PG2Net.

In this paper, the attitude tracking and load relief control problems against wind disturbances and uncertain aerodynamics as well as the engine thrust of launch vehicles are studied. Firstly, a framework of Compensated Acceleration Feedback based Active Disturbance Rejection Control (CAF-ADRC) is established to achieve both desired attitude tracking and load relief performances. In particular, the total disturbance that includes the effects caused by both aero-coefficient perturbations and disturbances is estimated by constructing an Extended State Observer (ESO) to achieve attitude tracking. Furthermore, combined with the normal acceleration due to the engine thrust, the accelerometer measurement is also compensated to enhance the load relief effect. Secondly, the quantitative analysis of ESO and the entire closed-loop system are studied. It can be concluded that the desired attitude tracking and load relief performances can be achieved simultaneously under the proposed approach. Besides, tuning laws of the proposed approach are systematically given, which are divided into ESO, Proportional Derivative (PD) and Compensated Acceleration Feedback (CAF) modules. Moreover, the performances under CAF-ADRC approach can be better than those under CAF based PD (CAF-PD) approach by tuning load relief gain. Finally, the approach presented is applied to a typical control problem of launch vehicles with wind disturbances and parameter uncertainties.

AbstractNeighborhood governance has been the center of attention in urban politics and public policy for many decades. Although wide-ranging literature explores the favorable effects of public participation on neighborhood governance, it is only relatively recently that attention has been directed to the manner in which effective participation develops and the consequent implications for participatory democracy. This article examines how local states encourage public participation in neighborhood governance using social networks in contemporary China. Based on a case study of the Minor Facts program, our findings demonstrate that even when participation mechanisms are introduced by the Party, horizontal networks also play a role in participation. Moreover, we present two forms of social networks and their mechanisms to illustrate how local states strategically mobilize them. These social networks also increase the social status and moral authority of Party members and political elites in ways they find useful for strengthening local government capacity to ensure political compliance. This article provides an important qualitative insight into network-related activators of participatory behaviors in the community.

With the further development of the smart grid, the modern power system has evolved into a cyber-physical power system (CPPS). In order to accurately quantify the resilience of a CPPS under cyber attacks, in this article, a resilience evaluation method for the CPPS considering the whole process of cyber attacks is proposed. First, the calculation model of successful attack probability is proposed based on the Markov node transition probability matrix and depth-first search algorithm. Second, based on the idea of nonsequential Monte-Carlo simulation, a mixed sampling method is proposed to determine the fault occurrence time and repair time of each circuit breaker in the fault set. In order to describe the change process of load after a successful attack, the response model of the power system is proposed using the optimal load reduction model based on the dc power flow. Four resilience evaluation indices for attack risk resistance, system adaptability, repair rate, and comprehensive weighted index are proposed to evaluate the resilience of CPPS considering cyber attacks. Finally, a case study is carried out based on the IEEE RTS 79 system to verify the effectiveness of the proposed resilience evaluation method.

This paper proposes a linear programming approach for onboard optimization of rocket trajectories, motivated by the need for reliable and computationally efficient trajectory optimization methods in real-time applications. To reformulate rocket trajectory optimization problems within a linear programming framework, a polytopal approximation method is developed. The polytopal approximation method approximates the thrust magnitude constraint using a set of artificial variables and linear constraints. Compared with a state-of-the-art approximation method, the polytopal approximation method demonstrates a higher accuracy-to-size ratio, enabling rocket trajectory optimization problems to be cast into linear subproblems without significantly increasing problem sizes. As linear subproblems can be readily solved by linear programming solvers, the proposed trajectory optimization approach inherits the solver’s reliability and computational efficiency, making it potentially useful for critical onboard applications. An ascent trajectory optimization problem is provided as an illustrative example to demonstrate the effectiveness of the proposed linear programming approach.

The path decision problem for a hypersonic vehicle has been recently formulated as it helps to determine a good initial guess for trajectory optimization with no-fly zone constraints. This path decision problem is a hybrid problem. Discrete variables optimize paths and continuous variables optimize dynamics, which consumes costly computation time and is hard to apply in online scenarios. To reduce the computation, instead of directly solving the hybrid problem, we design a heuristics approach taking advantage of the interpretability and flexibility of a graph attention network (GAT). The path decision is modeled by a directed graph and transformed into a GAT training problem, and the resulting GAT can directly output a path in online use. During this work, there are two innovations: 1) GAT customization and 2) offline training. First, we define the mask to express the graph structure, and model the problem-specific decoder process in the GAT, thus meeting the path decision logic of no-fly zones avoidance and ensuring the solutions are feasible. Second, we numerically integrate dynamics by a path-following guidance law, calculate the total control effort as the cost function, and use this cost to train the GAT based on the widely used REINFORCE, thus conforming to dynamics that are practical for hypersonic vehicles. Simulation results illustrate the high-accuracy path decision, much faster calculation, and generalization on no-fly zone layouts and numbers.

Purpose: This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations. Design/methodology/approach: The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference. Findings: The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction. Originality/value: The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

With the increase in intensive cultivation and long-term monoculture cropping, problems caused by continuous cropping (CC) have become increasingly prominent in the forms of replant diseases, soil sickness, and yield penalties. Such CC obstacles are a concern which frequently occur in modern agro-ecosystems and severely hamper normal growth, development, and quality of crop plants. In this review, we analyze studies from 2010 to 2021 on the effects of CC on crop plants and soil conditions constituting CC obstacles. The CC has significant adverse effects on plant growth and development, which mainly manifest in weaker photosynthesis, retarded growth, reduced resistance, and decreased yield and harvest quality. The causes of CC obstacles vary markedly among different crops, and the underlying mechanism depend on soil characteristics. The effects of the soil environment, crop physiological resistance, and auto-toxicity on CC obstacles are summarized. Considering the strategies to reduce the impacts of CC obstacles, the main prevention and control methods for CC obstacles are discussed, including intercropping or rotation, organic fertilization, fallow, biological control, and soil disinfection approaches. In addition, we discuss problems and proposed prospects for future research to improve prevention measures and control of CC obstacles. This review provides reference and guidance for solving this fundamental agronomic production problem.

Antioxidant peptides have been widely reported. However, only a few reports have been published examining the antioxidant peptides derived from Chinese baijiu. In this study, 6 novel peptides derived from Chinese baijiu were identified successfully using high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (HPLC-QTOF-MS) with a concentration of 0.835–24.540 µg/L. The underlying molecular mechanisms were investigated, and their cytoprotective effects were examined against 2,2’-azobis (2-methylpropanimidamidine) dihydrochloride (AAPH)-induced oxidative stress in HepG2 cells. The results showed that these peptides exerted protective effects by suppressing reactive oxygen species (ROS) generation, preventing malondialdehyde (MDA) formation, and upregulating cellular antioxidant enzyme activities (SOD, CAT, and GSH-Px) in a dose-dependent manner. Further experiments proved that these peptides exerted antioxidant effects via Nrf2/ARE-mediated signaling pathway by promoting Nrf2 nuclear translocation, inhibiting ubiquitination, and enhancing transcription capacity of Nrf2 in HepG2 cells. These findings provide the molecular basis for the effects of antioxidant peptides derived from Chinese baijiu, which is important for a deeper understanding of the relationship between human health and moderate drinking.

Enhanced kinetics and stronger adsorption towards LiPSs are obtained due to the CoMo1−xP/Mo heterostructure, which shows a low-capacity decay rate of 0.029% per cycle for lithium-sulfur batteries.

The existing industrial ammonia synthesis usually adopts the Haber-Bosch process, which requires harsh conditions of high temperature and high pressure, and consumes high energy. Under this circumstance, photoelectrochemical (PEC) catalysis is regarded as a promising method for N2reduction reaction (NRR), but bears problems of low efficiency and yield. Thus, exploring active catalysts remains highly desirable. In this work, BiVO4@MXene hybrids have been facilely synthesized by a hydrothermal route. The heterojunctions by the in situgrowth of BiVO4onto two-dimensional (2D) MXene greatly increase the NRR efficiency: under photoelectric conditions, the optimized NH3yield is 27.25 µg h − 1cm−2, and the Faraday efficiency achieves 17.54% at −0.8 V relative to the reversible hydrogen electrode (RHE), which are higher than most state-of-the-art NRR (photo) electrocatalysts. The mechanism speculation shows the enhanced light absorption range and the heterojunction formation largely promote the separation and the transfer efficiency of photogenerated carriers, thereby improving the PEC catalytic ability. Therefore, this work provides a hybrid route to combine the advantages of photo and electric catalysis for effective artificial nitrogen fixation.

Two-dimensional MXene with high conductivity has metastable Ti atoms and inert functional groups on the surface, greatly limiting application in surface-related electrocatalytic reactions. A surface-functionalized nitrogen-doped two-dimensional TiO2/Ti3C2Txheterojunction (N-TiO2/Ti3C2Tx) was fabricated theoretically, with high conductivity and optimized electrocatalytic active sites. Based on the conductive substrate of Ti3C2Tx, the heterojunction remained metallic and efficiently accelerated the transfer of Li+and electrons in the electrode. More importantly, the precise regulation of active sites in the N-TiO2/Ti3C2Txheterojunction optimized the adsorption for LiO2and Li2O2, facilitating the sluggish kinetics with a lowest theoretical overpotential in both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Employed as an electrocatalyst in a Li–oxygen battery (Li–O2battery), it demonstrated a high specific capacity of 15 298 mAh g–1and a superior cyclability with more than 200 cycles at 500 mA g–1, as well as the swiftly reduced overpotential. Furthermore, combined with the in situdifferential electrochemical mass spectrometry, ex situRaman spectra, and SEM tests, the N-TiO2/Ti3C2Txheterojunction electrode presented a superior stability and reduced side reaction along with the high performance toward the ORR and OER. It provides an efficient insight for the design of high-performance electrocatalysts for metal–oxygen batteries.

Cholestasis is a severe clinical complication that severely damages the liver. Kidneys are also the most affected extrahepatic organs in cholestasis. The pivotal role of oxidative stress has been mentioned in the pathogenesis of cholestasis-induced organ injury. The activation of the nuclear factor-E2-related factor 2 (Nrf2) pathway is involved in response to oxidative stress. The current study was designed to evaluate the potential role of Nrf2 signaling activation in preventing bile acids-induced toxicity in the liver and kidney. Dimethyl fumarate was used as a robust activator of Nrf2 signaling. Rats underwent bile duct ligation surgery and were treated with dimethyl fumarate (10 and 40 mg/kg). Severe oxidative stress was evident in the liver and kidney of cholestatic animals (P< 0.05). On the other hand, the expression and activity of Nrf2 and downstream genes were time-dependently decreased (P< 0.05). Moreover, significant mitochondrial depolarization, decreased ATP levels, and mitochondrial permeabilization were detected in bile duct-ligated rats (P< 0.05). Histopathological alterations included liver necrosis, fibrosis, inflammation and kidney interstitial inflammation, and cast formation. It was found that dimethyl fumarate significantly decreased hepatic and renal injury in cholestatic animals (P< 0.05). Based on these data, the activation of the cellular antioxidant response could serve as an efficient therapeutic option for managing cholestasis-induced organ injury.

Combining nanomaterials with complementary properties in a well-designed structure is an effective tactic to exploit multifunctional, high-performance materials for the energy conversion and storage. Nonprecious metal catalysts, such as cobalt oxide, with superior activity and excellent stability to other catalysts are widely desired. Nevertheless, the performance of CoO nanoparticles as an electrode material were significantly limit for its inferior conductivity, dissolution, and high cohesion. Herein, we grow ultrafine cobalt monoxide to decorate the interlayer and surface of the Ti3C2Txnanosheets via a hydrothermal method companied by calcination. The layered MXenes act as the underlying conductive substrate, which not only increase the electron transfer rate at the interface but also greatly improve the electrochemical properties of the nanosized CoO particles by restricting the aggregation of CoO. The resulting CoO/Ti3C2Txnanomaterial is applied as oxygen electrode for lithium-oxygen battery and achieves more than 160 cycles and first cycle capacity of 16,220 mAh g−1at 100 mA g−1. This work paves a promising avenue for constructing a bi-functional catalyst by coupling the active component of a transition metal oxide (TMO) with the MXene materials in lithium-oxygen battery.

An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller.

The development of high-performance, low-cost, and long-lasting electrocatalysts for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is urgently needed for effective electrochemical water splitting. In the present study, an engineering process was employed to prepare “Lewis base-hungry” amorphous–crystalline nickel borate–nickel sulfide (Ni3(BO3)2–Ni3S2) heterostructures, which exhibited unprecedentedly high electrocatalytic activity toward both OER and HER in alkaline media. The optimal Ni3(BO3)2–Ni3S2/nickel foam (Ni3(BO3)2–Ni3S2/NF) electrode displayed an ultralow overpotential of only −92 and +217 mV to reach the current density of 10 mA cm–2for HER and OER, respectively. When the Ni3(BO3)2–Ni3S2/NF electrode was used as both the anode and cathode for overall water splitting, a low cell voltage of 1.49 V was needed to achieve the current density of 10 mA cm–2, which was superior to the performance of most noble metal-free electrocatalysts. Results from density functional theory calculations showed that the Lewis base-hungry sites in the heterostructures effectively enhanced the chemisorption of hydrogen and oxygen intermediates, a critical step in HER and OER electrocatalysis. Results from this study highlight the significance of rational design and engineering of heterostructured materials for the development of high-efficiency electrocatalysts.

Ni/Ti3C2TxMXene hybrid is used as cathode material of lithium-oxygen battery, which shows good cycle and specific capacity performance.

The new material Polypyrrole–Mo3S13(abbr.Mo3S13–Ppy) is a new material prepared by ion-exchange between Ppy-NO3and (NH4)2Mo3S13. The Mo3S13–Ppy was designed to exhibit strong selectivity for Ag+and highly toxic Hg2+in mixtures with other ions. It displays an apparent selectivity ranking of Hg2+> Ag+≥ Co2+, Ni2+, Cu2+, Zn2+, Cd2+, and Pb2+. The strong affinity of Mo3S13–Ppy for Ag+and Hg2+was confirmed with extremely high distribution coefficients (Kd) (∼107mL/g) and remarkable removal efficiencies (>99.99%), resulting in <1 ppb concentrations of these ions. Furthermore, Mo3S13–Ppy achieved excellent separation selectivity for Ag+from Cu2+(even at a high Cu2+/Ag+ratio, the molar ratio of 867 and mass ratio of 500) because of the special structure of Mo3S132–and its component Mo4+and (S2)2–. This is promising for the direct extraction of low-grade silver from copper-rich minerals. The maximum Ag uptake capacity of 408 mg/g is redox-based and surprisingly involves the deposition of large, millimeter sized, metallic silver (Ag0) crystals on the surface of Mo3S13–Ppy.

Lattice strain plays a critical role in controlling catalytic performance. However, few experiments or theoretical works have been reported regarding the strain–activity relationship in the hydrogen evolution reaction (HER). Herein, we have prepared platinum–skin-truncated octahedra with the formula CoPtx. Among the prepared nanocatalysts of varied composition, the CoPt2nanocatalyst exhibits an extremely small overpotential of 17 mV in acid solution at −10 mA cm–2, which is obviously superior to commercial Pt/C (30 mV). Furthermore, the overpotential of the CoPt2nanocatalyst is only 20 mV in alkaline solution, which is about a third of that of commercial Pt/C (53 mV). The experiments and density functional theory calculations reveal a “volcano”-type relationship for the strain-HER catalytic activity relationship for the first time. The theoretical calculated “volcano” relationship gives a clearer scope of the best strain toward further investigation on higher HER activity.

In practical combat scenarios, Hypersonic Glide Vehicles (HGV) face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint (SPSDHC). To address this problem, this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning. The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization. The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory. The terminal miss of HGV in the case of evading two missiles is analyzed, which means that the three-player optimal evasion strategy is a linear combination of two one-to-one strategies. Then, a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy. The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target, which ensures that the HGV satisfies the homing constraint. Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy, which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.

Due to the complex heterogeneity in different cancer types, the heterodimeric strategy has been intensively practiced to improve the effectiveness of tumor diagnostics. In this study, we developed a series of novel 18F-labeled biotin/FAPI-conjugated heterobivalent radioligands ([18F]AlF-NSFB, [18F]AlF-NSFBP2, and [18F]AlF-NSFBP4), synergistically targeting both fibroblast activation protein (FAP) and biotin receptor (BR), to enhance specific tumor uptake and retention. The in vitroand in vivobiological properties of these dual-targeting tracers were evaluated, with a particular focus on positron emission tomography imaging in A549 and HT1080-FAP tumor-bearing mice. Notably, in comparison to the corresponding FAP-targeted monomer [18F]AlF-NSF, biotin/FAPI-conjugated heterodimers exhibited a high uptake in tumor and prolong retention. In conclusion, as a proof-of-concept study, the findings validated the superiority of biotin/FAPI-conjugated heterodimers and the positive influence of biotin and linker on pharmacokinetics of radioligands. Within them, the bispecific [18F]AlF-NSFBP4holds significant promise as a candidate for further clinical translational studies.

Recognizing the significance of SPECT in nuclear medicine and the pivotal role of fibroblast activation protein (FAP) in cancer diagnosis and therapy, this study focuses on the development of 99mTc-labeled dimeric HF2with high tumor uptake and image contrast. The dimeric HF2was synthesized and radiolabeled with 99mTc in one pot using various coligands (tricine, TPPTS, EDDA, and TPPMS) to yield [99mTc]Tc-TPPTS-HF2, [99mTc]Tc-EDDA-HF2, and [99mTc]Tc-TPPMS-HF2dimers. SPECT imaging results indicated that [99mTc]Tc-TPPTS-HF2exhibited higher tumor uptake and tumor-to-normal tissue (T/NT) ratio than [99mTc]Tc-EDDA-HF2and [99mTc]Tc-TPPMS-HF2. Notably, [99mTc]Tc-TPPTS-HF2exhibited remarkable tumor accumulation and retention in HT-1080-FAP and U87-MG tumor-bearing mice, thereby surpassing the monomeric [99mTc]Tc-TPPTS-HF. Moreover, [99mTc]Tc-TPPTS-HF2 achieved acceptable T/NT ratios in the hepatocellular carcinoma patient-derived xenograft (HCC-PDX) model, which provided identifiable contrast and imaging quality. In conclusion, this study presents proof-of-concept research on 99mTc-labeled FAP inhibitor dimers for the visualization of multiple tumor types. Among these candidate compounds, [99mTc]Tc-TPPTS-HF2showed excellent clinical potential, thereby enriching the SPECT tracer toolbox.

The stimulator of interferon genes (STING) is pivotal in mediating STING-dependent type I interferon production, which is crucial for enhancing tumor rejection. Visualizing STING within the tumor microenvironment is valuable for STING-related treatments, yet the availability of suitable STING imaging probes is limited. In this study, we developed [18F]AlF-ABI, a novel 18F-labeled agent featuring an amidobenzimidazole core structure, for positron emission tomography (PET) imaging of STING in B16F10 and CT26 tumors. [18F]AlF-ABI was synthesized with a decay-corrected radiochemical yield of 38.0 ± 7.9% and radiochemical purity exceeding 97%. The probe exhibited a nanomolar STING binding affinity (KD= 35.6 nM). Upon administration, [18F]AlF-ABI rapidly accumulated at tumor sites, demonstrating significantly higher uptake in B16F10 tumors compared to CT26 tumors, consistent with STING immunofluorescence patterns. Specificity was further validated through in vitrocell experiments and in vivoblocking PET imaging. These findings suggest that [18F]AlF-ABI holds promise as an effective agent for visualizing STING in the tumor microenvironment.

Neighboring optimal guidance, a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory, is widely used in the aerospace field due to its high computational efficiency and low resource usage. For more advanced scenarios, the existing methods still have a problem that the guidance accuracy and optimality will seriously degrade when the actual state largely deviates from the nominal trajectory. This is mainly caused by the approximate description of the first-order conditions in terms of total flight time and nonlinear constraints. To address this problem, a higher-order neighboring optimal guidance method is proposed. First, a novel total flight time updating strategy, together with a normalized time scale, is presented that transforms the optimal problem with free total flight time into a more tractable optimal problem with fixed total flight time. Then, using the vector partial derivative method, a higher-order approximation is adopted, instead of the first-order approximation, to accurately describe the nonlinear dynamical and terminal constraints, thus obtaining a polynomially constrained quadratic optimal problem. Finally, to numerically solve the polynomially constrained quadratic optimal problem, a Newton-type iterative algorithm based on the orthogonal decomposition is designed. Through the iterative solution within each guidance period, the corrections to control quantities and total flight time are generated. The proposed method is applied to a launch vehicle orbital injection problem, and simulation results show that it achieves high accuracy of orbital injection and optimality of performance index.

This work presents L1/L2 two-parameter regularization as an efficient technique for the identification of light oil in the two-dimensional (2D) nuclear magnetic resonance (NMR) spectra of tight sandstone reservoirs. A 2D NMR T2-T1 distribution model containing light oil, natural gas, and formation water is constructed. 2D NMR echo trains are obtained by means of the multiwaiting time Carr, Purcell, Meiboom, and Gill pulse sequence. A detailed analysis of the ill-posed characteristics to obtain 2D NMR spectrum is given using a Picard curve. The identification abilities of L1/L2 two-parameter regularization and three other techniques are compared in detail. The paper demonstrates that even if the signal-to-noise ratio (SNR) is around 100, it is still very difficult to obtain the 2D NMR spectrum. Light oil cannot be distinguished using Tikhonov regularization and truncated singular value decomposition. Both L1/L2 two-parameter regularization and L1 norm regularization can identify light oil, while the identification ability of L1/L2 two-parameter regularization is higher than that of L1 norm regularization, especially when the SNR is very low. L1/L2 two-parameter regularization can be used as an effective technique to identify light oil from formation water in the 2D NMR spectra of tight sandstone reservoirs.

MOFs present potential application in electrocatalysis. The structure–activity of the Ni-MOFs with different morphologies, nanowires, neurons, and urchins is systemically investigated. The Ni-MOFs were controllably synthesized via the facile solvothermal method. Among them, the Ni-MOF nanowires are endowed with the highest electrocatalytic activity due to the unique structure, more exposed active sites, lower charge transfer resistance, and the fast and direct electron transfer in 1D structures. The typical morphology of the Ni-MOF nanowires is ca. 10 nm in diameter and several micrometers in length. When employed as an electrocatalyst in urea oxidation reaction, it exhibits a lower overpotential than and superior stability to the Ni-MOFs with other morphologies. Ni-MOF nanowires require a potential of ∼0.80 V (vs Ag/AgCl) to obtain 160 mA cm–2. In addition, after continuous electrocatalyzing for 3600 s at 0.40 V (vs Ag/AgCl), the current density retention of Ni-MOF nanowires could still reach more than 60% (>12 mA cm–2), which demonstrates Ni-MOF nanowires as promising electrocatalysts for urea oxidation.

The design of low-cost yet high-efficiency electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) over a wide pH range is highly challenging. We now report a hierarchical co-assembly of interacting MoS2and Co9S8nanosheets attached on Ni3S2nanorod arrays which are supported on nickel foam (NF). This tiered structure endows high performance toward HER and OER over a very broad pH range. By adjusting the molar ratio of the Co:Mo precursors, we have created CoMoNiS-NF-xycomposites (x:ymeans Co:Mo molar ratios ranging from 5:1 to 1:3) with controllable morphology and composition. The three-dimensional composites have an abundance of active sites capable of universal pH catalytic HER and OER activity. The CoMoNiS-NF-31 demonstrates the best electrocatalytic activity, giving ultralow overpotentials (113, 103, and 117 mV for HER and 166, 228, and 405 mV for OER) to achieve a current density of 10 mA cm–2in alkaline, acidic, and neutral electrolytes, respectively. It also shows a remarkable balance between electrocatalytic activity and stability. Based on the distinguished catalytic performance of CoMoNiS-NF-31 toward HER and OER, we demonstrate a two-electrode electrolyzer performing water electrolysis over a wide pH range, with low cell voltages of 1.54, 1.45, and 1.80 V at 10 mA cm–2in alkaline, acidic, and neutral media, respectively. First-principles calculations suggest that the high OER activity arises from electron transfer from Co9S8to MoS2at the interface, which alters the binding energies of adsorbed species and decreases overpotentials. Our results demonstrate that hierarchical metal sulfides can serve as highly efficient all-pH (pH = 0–14) electrocatalysts for overall water splitting.

Ultrathin two-dimensional metal–organic frameworks (2D MOFs) have the potential to improve the performance of Li–O2batteries with high O2accessibility, open catalytic active sites, and large surface areas. To obtain highly efficient cathode catalysts for aprotic Li–O2batteries, a facile ultrasonicated method has been developed to synthesize three kinds of 2D MOFs (2D Co-MOF, Ni-MOF, and Mn-MOF). Contributing from the inherent open active sites of the Mn–O framework, the discharge specific capacity of 9464 mAh g–1is achieved with the 2D Mn-MOF cathode, higher than those of the 2D Co-MOF and Ni-MOF cathodes. During the cycling test, the 2D Mn-MOF cathode stably operates more than 200 cycles at 100 mA g–1with a curtailed discharge capacity of 1000 mAh g–1, quite longer than those of others. According to further electrochemical analysis, we observe that the 2D Mn-MOF outperforms 2D Ni-MOF and Co-MOF due to a superior oxygen reduction reactions and oxygen evolution reactions activity, in particular, the efficient oxidation of both LiOH and Li2O2. The present study provides new insights that the 2D MOF nanosheets can be well applied as the Li–O2cells with high energy density and long cycling life.

The development of novel and highly efficient bifunctional electrocatalysts for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is an ongoing challenge. The Cr3+cation has a special electronic configuration (t32ge0g), which facilitates charge transfer and electron capture. However, Cr-based materials applied on water-splitting electrocatalysis is still a research void up to now. Herein, a novel amorphous γ-CrOOH was developed as a bifunctional electrocatalyst toward overall water splitting for the first time. It shows extraordinary HER activity with an ultralow overpotential of only 149 mV at 50 mA cm–2. Meantime, there is a small overpotential of 334 mV at 50 mA cm–2for the OER. Importantly, the bifunctional electrocatalyst for overall water-splitting electrocatalysis can work with a cell voltage of merely 1.56 V at 10 mA cm–2. Amorphous γ-CrOOH has effectively enhanced the intrinsic electrochemical activity via density functional theoretical calculations. Therefore, this work not only provides a new method for preparation of amorphous γ-CrOOH but also expands the types of catalysts for water splitting.

A novel (Ce-Mg) co-promoted CaO sorbent with multi-dimensional through-hole structure was fabricated by a hydrothermal template method based on natural limestone, to address the evident decline of CO2adsorption performance during circulation. A trend of first increasing and then stabilizing was achieved for CO2long-term durability of the as-prepared sorbent by the hydrothermal recrystallization process. The CO2absorption capacity of the (Ce-Mg) co-promoted tubular sorbents exceeded that of the reference sample without hydrothermal template treatment by ∼ 400% after 30 cycles. 97% of the initial CO2capacity can be maintained at the 30th cycle. This significant CO2uptake capacity can be attributed to the multiple synergistic interactions yielded by incorporating g-C3N4nanotube template with (Ce-Mg) co-doping. A bidirectional gas diffusion channel was constructed based on the inner-outer multi-dimensional connection structure of g-C3N4, which allowed the sorbent to maintain the prominent specific surface area and pore volume during cycles. After 30 cycles, the specific surface area of the limestone-derived sorbents can still remain at 16.31 m2/g. Furthermore, a double vacancy mechanism that can facilitate the gas transport through both the product layer and the surface layer of CaO grains caused by the co-doping of (Ce-Mg) was proposed, which can also promote the electron transport between CaO and CO2. This synergistic strategy proved useful for improving the CO2uptake performance and cycle durability of CaO sorbents in practical applications.