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Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.

Light olefins are of great importance as chemical raw materials, and ethylene is a crucial symbol to evaluate the development level of petrochemical industry. Catalytic hydrogenation of CO2to light olefins is one of the most vital approaches to utilize CO2with high added valued. InZr/SAPO-34 composite catalyst shows prominent potential in research and application because of their high light olefins selectivity and high stability in CO2hydrogenation. In this study, the effects of different preparation methods of InZr/SAPO-34 composite catalyst for CO2hydrogenation to light olefins were studied in depth. The catalyst prepared by co-precipitation method showed the highest catalytic activity, and the catalyst prepared by sol-gel-precipitation method showed the highest light olefins selectivity. The structure-activity relationship of InZr/SAPO-34 catalysts were revealed by various characterization methods.

With the widespread attention on hydrogen energy, the operation optimization of the coupling system of hydrogen energy and transportation has become a critical issue. Therefore, this study develops a hydrogen-containing energy transportation coupling system for the system collaborative operation framework, operation mode and equipment modelling of the system. Furthermore, a medium- to long-term operation optimization model and a short-term operation optimization model were constructed considering the differences in the operation of coupled systems at different timescales and operating costs as the objective function and power balance and system equipment as constraints. In the medium- and long-term operation optimization model, the planning scenario reduction method is used to reduce the wind power generation scenario. In the short-term operation optimization model, a multivariate uncertainty model is constructed to represent the uncertainty in the coupling system. Subsequently, the solution method of the model is proposed. Finally, a coupled system is simulated to verify the effectiveness of the model. (i) When the initial scene set is 600, the typical scene reduction method using Latin hypercube sampling and the Wasserstein distance can reduce operating costs by 7.60% and 9.49% compared with K-means reduction and K-media reduction methods. (ii) The sensitivity coefficients of hydrogen sales price, electricity sales price and maintenance rate to operating costs are –0.031%, –1.009% and 0.0105%, respectively. (iii) Considering multiple uncertainties can help make optimal decisions based on the overall consideration of disadvantage scenarios, thereby reducing system operating costs.Medium-to-long-term and short-term optimization models are constructed for operation of a hydrogen-containing energy transportation system, considering operation of coupled systems at different timescales. The objective function is based on operating costs with power balance and system equipment as constraints.Graphical Abstract

With the implementation of China’s carbon-peaking and carbon-neutrality strategy, new energy will achieve leapfrog growth. Due to the good economics of distributed new-energy generation, it can not only save users’ own investment, but also help to achieve local consumption of new energy. However, it will also bring about a series of incremental costs to the power grid. This paper first enumerates the concept, development status and scheduling mode of a distributed new-energy storage system. Based on the above, it establishes a new-energy power generation model and an energy storage system charging and discharging model, and proposes a global optimization scheduling model for a distributed new-energy storage system, considering the time-of-use electricity price and taking the lowest total operating cost of the distributed new-energy power generation system as the objective function. Finally, it proposes a distribution network incremental cost analysis model based on the penetration of distributed new energy. The calculation results show that the incremental cost of grid-connected distributed new energy is 1.0849, 1.2585 and 1.3473 yuan/kWh, respectively, which indicates that the global dispatching model can optimize the power consumption structure of a distributed power generation system, and has the function of peak shaving and valley filling, but the incremental cost of the distribution network will also increase.A new-energy power generation model and an energy storage system charging and discharging model use a global optimization scheduling method, considering the time-of-use electricity price and taking the lowest total operating cost as the objective function. The results show the incremental cost of grid-connected distributed new energy.Graphical Abstract

Discovering communities in attributed networks is an important research topic in complex network analysis. Community detection based on multi-objective evolutionary computing (MOEA) models community detection as a multi-objective optimization problem and searches the optimal solutions by simulating the evolution of a biological population. However, the existing multi-objective evolutionary algorithms for community detection faces two challenges: their encoding schemes are designed based on network topology and neglects the information in node attributes; and they are easy to fall into local optimum. In this article, we propose a community detection algorithm empowered by multi-objective evolutionary computing, named ECEVO-MOEA, which conducts edge closeness encoding and embedding vector optimization alternately. On the one hand, the evolution of a biological population is completed by employing a new edge closeness encoding scheme and multiple attribute-aware objective functions. On the other hand, the update of embedding vectors is used to calculate similarity matrix and communities to improve solution quality, avoiding it from early convergence. Experiments on real networks demonstrate that ECEVO-MOEA achieves higher accuracy than the baseline algorithms.

Mobile Crowdsensing (MCS) is a sensing paradigm that enables large-scale smart city applications, such as environmental sensing and traffic monitoring. However, traditional MCS often suffers from performance degradation due to the limited spatiotemporal coverage of collected data. In this context, Sparse MCS has been proposed, which utilizes data inference algorithms to recover full data from sparse data collected by users. However, existing Sparse MCS approaches often overlook spatiotemporal fractures, where no data is observed either for a sensing subarea across all sensing time slots (temporal fracture), or for a sensing time slot in all sensing subarea (spatial fracture). Such spatiotemporal fractures pose great challenges to the data inference algorithms, as it is difficult to capture the complex spatiotemporal correlations of the sensing data from very limited observations. To address this issue, we propose a Graph- and Attention-based Matrix Completion (GAMC) method for the spatiotemporal fracture data inference problem in Sparse MCS. Specifically, we first pre-fill the general missing values using the classical Matrix Factorization (MF) technique. Then, we propose a neural network architecture based on Graph Attention Networks (GAT) and Transformer to capture complex spatiotemporal dependencies in the sensing data. Finally, we recover the complete data with a projection layer. We conduct extensive experiments on three real-world urban sensing datasets. The experimental results show the effectiveness of the proposed method.

In this article, two types of problems: 1) unconstrained and 2) constrained optimization are solved by developing a class of distributed predefined-time algorithms under dynamic event triggered (DET). First, under the zero-gradient-sum framework and DET-called one-to-one type, the distributed predefined-time algorithm, which contains two time-varying functions, is designed to deal with the unconstrained optimization problem in an initialization-free manner. Furthermore, it is proved in detail that the applied DET function can well exclude Zeno behavior through dynamic threshold variable. Second, the DET-based distributed predefined-time algorithm is applied to the optimization problem with supply–demand balance constraint by introducing an auxiliary variable, in which the algorithm can eliminate the disadvantage that the initial state must meet certain conditions, that is, the initial state can be selected arbitrarily. Different from many existing optimization algorithms, the developed two kinds of algorithms in this work exhibit excellent performance in terms of energy saving, convergence time, and initialization free. Finally, the effectiveness and superiority of the proposed algorithms are verified by three numerical examples.

The intricate and dynamic nature of eye movements serves as a window into the realms of cognition, emotion, and physiological responses. Event detection, in turn, is instrumental in the precise recognition and categorization of these diverse eye movements. Deep learning methods have recently been applied to event detection, yielding promising results. However, the intrinsic multi-scale attributes of events have often been overlooked in existing approaches. To address this, we introduce “GazeUNet”, a novel network based on U-Net and Bi-GRU, which classifies gaze samples into three categories: fixation, saccade, and post-saccadic oscillations. Firstly, multi-scale spatial features are captured using a U-Net model, and then a hierarchical bidirectional gated recurrent unit (Bi-GRU) is employed to extract temporal correlations, followed by classification through fully connected layers. Our results, derived from the analysis of three publicly available datasets, consistently showcase the superiority of the proposed model compared with other state-of-the-art methods across all categories.

Sparse crowdsensing collects data from a subset of the sensing area and infers data for unsensed areas, reducing data collection costs. Previous works have primarily focused on independently collecting and inferring single types of data. However, real-world scenarios often involve multiple types of data that can complement each other by providing missing spatiotemporal distribution information. In this paper, we fully consider both intra-data correlations among data of the same type and inter-data correlations among data of different types, enabling collaborative execution of various tasks. In addition, we enhance the adaptability in practical application scenarios by utilizing real-time collected sparse data to guide task execution. For this purpose, we propose a multi-task adaptive budgeting framework for online sparse crowdsensing, called MTAB-SC. This framework consists of three parts: training data updating, data inference, and data collection. First, we propose a multi-task data updating method to keep models up-to-date. Second, we design a data inference network for multi-task data joint inference. Finally, to allocate suitable budgets for each task and facilitate collaborative data collection across multiple tasks, we propose an Adaptive Budgeting for Collaborative Data Collection model (AB-CoDC). The effectiveness of our proposals is demonstrated through extensive experiments on two real-world datasets.

This article presents a limited amplitude-disturbance observer (LADOB) as a means to counteract transient disturbance dynamics in a nonlinear control system with backlash. The core principle of this method involves directly constraining the linear output amplitude of the observer using a saturation function. This approach yields a high-bandwidth disturbance observer while ensuring the stability. By virtue of its rapid response, the LADOB effectively mitigates backlash spikes induced by speed reversal. Additionally, the saturation function within the LADOB control system addresses limit cycle oscillations stemming from backlash dynamics. An investigation is conducted to determine the optimal balance between stability and antidisturbance performance by examining the relationship between the saturation threshold and the system's bandwidth. Experimental validation is carried out on a belt-drive turntable, effectively demonstrating the efficacy of the proposed method in attenuating nonlinear backlash.

This paper studies the cluster consensus of multi-agent systems (MASs) with objective optimization on directed and detail balanced networks, in which the global optimization objective function is a linear combination of local objective functions of all agents. Firstly, a directed and detail balanced network is constructed that depends on the weights of the global objective function, and two kinds of novel continuous-time optimization algorithms are proposed based on time-invariant and time-varying objective functions. Secondly, by using fixed-time stability theory and convex optimization theory, some sufficient conditions are obtained to ensure that all agents’ states reach cluster consensus within a fixed-time, and asymptotically converge to the optimal solution of the global objective function. Finally, two examples are presented to show the efficacy of the theoretical results.

This article is concerned with the exact controllability of a linear stochastic game-based control system (SGBCS) with time-variant random coefficients. Some necessary conditions and sufficient conditions for the exact terminal controllability of the SGBCS are obtained. Then, it is proved that the exact controllability of the SGBCS, the nonsingularity of a generalized version of the Gramian matrix, the validity of an observability inequality for the adjoint equation, and the unique solvability of a family of minimization problems are all equivalent. An admissible control for the regulator is constructed in closed form, which can steer the macrostate from any existing initial vector to any desired terminal random variable.

In order to provide more efficient and reliable power services than the traditional grid, it is necessary for the smart grid to accurately predict the electric load. Recently, recurrent neural networks (RNNs) have attracted increasing attention in this task because it can discover the temporal correlation between current load data and those long-ago through the self-connection of the hidden layer. Unfortunately, the traditional RNN is prone to the vanishing or exploding gradient problem with the increase of memory depth, which leads to the degradation of predictive accuracy. Many RNN architectures address this problem at the expense of complex internal structures and increased network parameters. Motivated by this, this article proposes two adaptive modularized RNNs to tackle the challenge, which can not only solve the gradient problem effectively with a simple architecture, but also achieve better performance with fewer parameters than other popular RNNs.

An acceleration measurement-based disturbance observer (DOB) control method is proposed to capture and eliminate transient and dramatic dynamic disturbance torque in the flexible belt-drive servo instrumentation. The essence of this method is to incorporate the acceleration difference between the motor side and the load side to implement a DOB in the dual-rate control loop. The acceleration difference is sensitive to the rate variation caused by the motor-load large span distribution. The input signal of the motor inner loop with the high bandwidth is equivalently processed to obtain the estimated motor-side acceleration. However, the observed acceleration disturbance suffers from low-frequency drift and noise accumulation, resulting in system instability under integral characteristics. Therefore the $H_{\infty }$ principle is used to ensure stronger stability and the compensator is bandpass-corrected to enhance the anti-disturbance performance in a limited bandwidth. The proposed method is experimentally validated to effectively suppress the disturbance torque after obtaining an optimal trade-off between robust stability and anti-disturbance. The error spikes and buffeting in the flexible transmission chain are further reduced compared with the conventional methods.

Bike-sharing systems have been deployed globally. One of the key issues for high-quality bike-sharing systems is to rebalance city-wide stations to maintain bike availability. Traditional strategies, such as repositioning bikes by trucks and volunteers based on historical riding records, usually operate in fixed paths and limited capacities, lacking the flexibility to cope with the highly dynamic and context dependent riding demands, and usually suffer from high costs and long delays. In this work, we propose RedPacketBike, an incentive-driven, crowd-based station rebalancing framework to effectively recruit participants from hybrid fleets (e.g., volunteer riders and hired trucks) based on the accurate forecast of bike demand leveraging deep learning techniques. First, we propose a spatiotemporal clustering method to extract bike demand hotspots from fluctuating bike usage data. Then, we build a context-aware deep neural network named BikeNet to forecast the trends of bike demand hotspots, simultaneously modeling the spatial correlations by graph convolution networks (GCN), the temporal dependencies by long short-term memory networks (RNN), and the contextual factors by autoencoders (AE). Finally, we propose a reinforcement-learning-based method to find optimal station rebalancing schemes by generating station rebalancing tasks with an integer linear programming (ILP) algorithm and allocating tasks to participants from hybrid fleets with dynamic incentive designs and reward expectations. Experiments using real-world bike-sharing system data collected from Citi Bike in New York City and Mobike in Xiamen City validate the performance of our framework, achieving a demand forecast error below 4.171 measured in MAE, and a 17.2% improvement of station availability by simulations with real-world parameter settings, outperforming the state-of-the-art baselines.

Online gathering large-scale heterogeneous tasks and multi-skilled participant can make the tasks and participants to be shared in real time. However, their online gathering will bring many intractable objective requirements, which makes task-participant matching become extremely complex. To cope well with the gathering, we design a hierarchy tree and time-series queue to organize tasks and participants. The data structures we designed can effectively meet all requirements that are brought due to tasks and participants gathering online. In addition, based on the designed data structures, we study online large-scale heterogeneous task allocation problem from three aspects: the computing pattern, the tree creation method, and the extension of matching strategy. Our best method (TsPY) is based on parallel computing in the computing pattern, adopts time first and then space in the tree creation method, and increases the short-distance first strategy in the matching strategy. Finally, we conducted detailed experiments under the conditions of different participant geographical distributions (i.e., uniform distribution, Gaussian distribution, and check-in empirical distribution), different sensing methods (i.e., participatory sensing and opportunistic sensing), and different recommendation methods (i.e., point recommendation and trajectory recommendation). The experimental results show that TsPY has a good performance in multiple indicators such as algorithm running time, task-participant matching rate, participant travel distance, and redundant tasks removed. Compared with serial computing, parallel computing can reduce the algorithm running time by more than 66% on average in our experimental environment. Compared with space first and then time, creating a tree based on time first and then space can increase task-participant matching rate by more than 13% on average. Increasing the short-distance first strategy can reduce the participant travel distance by more than 4% on average.

This article considers the prescribed-time optimization problem of multiagent systems (MASs) under two types of control protocols, namely prescribed-time continuous protocol and its event-triggered extension. Firstly, the two-stage prescribed-time continuous protocol is designed to optimize the global cost function, which is the sum of all local objective cost functions. The event-triggered mechanism is then combined with continuous protocol from an energy saving perspective. Moreover, it is proved in detail that using the event-triggered function will not happen Zeno behavior. Secondly, to avoid using the global gradient information, the prescribed-time optimization protocols are designed based on the idea of zero-gradient-sum. Different from the existing finite/fixed-time optimization results, one of the advantages of the proposed protocols is that the settling time can be independent of the initial state and system parameters. Finally, the correctness and validity of theoretical results are proved by numerical examples.

Natural disasters cause extensive damages to urban cities, demanding authorities to take urgent and effective measures to restore normalcy on transportation. During a disaster, restoring road transportation in a timely manner for rescue, supply and also prevent the risk of road accidents due to obstacles is of vital importance. Traditional post-disaster road obstacle work relies on a manual investigation which is time-consuming and labor-intensive. Predicting road risks can provide decision support for emergency management departments, reducing the damage caused by disasters. In this paper, we propose a three-phase framework for predicting road risks post-disaster leveraging heterogeneous urban data. Firstly, We use a clustering algorithm to extract and classify urban road networks based on the floating car data. Then we extract the spatiotemporal features of the urban roads. Through social network data, we collect historical risk-prone data using the crowdsensing method. To address the challenges of the small amount of labeled data, we train our model based on self-training. We verified the validity of this model by using a real dataset in Xiamen island which proves that our model accurately predicts road risk with precision and recall both more than 85% respectively.

Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’ daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city’s global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity.

We study a general kind of partial information nonzero sum two-player stochastic differential games, where the state variable is governed by a stochastic differential equation and the control domain of each player can be non-convex. Moreover, the control variables of both players can enter the diffusion coefficients of the state equation. We establish Pontryagin’s maximum principle for open-loop Nash equilibria of the game. Then, a verification theorem is obtained for Nash equilibria when the control domain is convex. Finally, the theoretical results are applied to studying a linear-quadratic game.

Despite the great success of deep neural networks in medical lesion detection tasks, their adaptation to CT images of ovarian cancer is known to be difficult, and the most important reason is the lack of ovarian cancer datasets. A commonly accepted reason for this deficiency is that few people are willing to disclose CT imaging data due to privacy concerns, despite the fact that ovarian cancer is the first most prevalent cancer among women. In this work, we propose a method for generating synthetic high-resolution medical images using generative adversarial networks. The method addresses the instability of previous work in generating work on small-scale datasets by a simple and effective technique to acquire prior knowledge and achieve multi-scale gradient flow. We show that the proposed method still achieves good results even when synthesised on small-scale data, and with FID scores better than the current top-performing networks (GANs).

This paper investigates the event-based dynamic output feedback control for networked control system with actuator failures. A novel sum-based discrete event-triggered mechanism (SDETM) is proposed, whose triggering condition includes not only the current sample of system states but also previous samples. With the proposed SDETM, a dynamic output feedback controller (DOFC) is developed under a general networked control system (NCS) structure. We consider a general system structure where two network channels are involved, and the proposed SDETM is employed to both networks to extensively save network resources. A novel stability criteria is established for the closed-loop system based on the Lyapunov-Krasovskii functional method. By using the Cone-complimentarity Linearization (CCL) approach, sufficient conditions are derived to co-design the DOFC and triggering parameters. The effectiveness and advantage of the proposed method is verified by a satellite control system.

Missing data estimation (MDE) for time series is a crucial issue concerned with various applications based on the Internet of Things (e.g. sparse mobile crowdsensing). Although many approaches have been proposed to address this issue, they are either insufficiently considered or computationally expensive for data correlations. Motivated by this, an echo state network (ESN) with bidirectional-feedback connections is first proposed to skillfully encode temporal, cross-domain, and lagging correlations into the high-dimensional state of the reservoir. Meanwhile, the output weight (the only unknown parameter of the network) can be trained quickly by reservoir computing to reduce computation overhead. On this basis, an improved version with multiple reservoirs is designed to further integrate data correlations from information flows in different directions. Finally, a general process for MDE based on ESN architecture is developed for popularization and application. Experimental results of various missing rates under different missing mechanisms show that the proposed models perform better than the current methods in estimation accuracy.

A kind of linear-quadratic Stackelberg games with the multilevel hierarchy driven by both Brownian motion and Poisson processes is considered. The Stackelberg equilibrium is presented by linear forward-backward stochastic differential equations (FBSDEs) with Poisson processes (FBSDEPs) in a closed form. By the continuity method, the unique solvability of FBSDEPs with a multilevel self-similar domination-monotonicity structure is obtained.

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.

Searching for highly efficient oxygen reduction reaction (ORR) electrocatalysts for fuel cell technology, in which the crystal structure plays a powerful role in regulating the electrocatalysis, is urgent yet challenging. Herein, we have explored the active and stable Pd–Se alloy electrocatalysts with controlled phase toward alkaline ORR. The phase-controlled Pd–Se nanoparticles (NPs) show interesting phase-dependent electrocatalytic performance, in which the Pd17Se15NPs/C exhibits much better ORR performance than its counterpart, Pd7Se4NPs/C, and the commercial Pd/C and Pt/C. Based on the detailed analysis, Pd in Pd17Se15possesses more Se atom coordination and a higher valence state, thus providing a stronger capacity for the absorption of oxygenated species. DFT further reveals more charge transfer from the Pd17Se15surface to the *OOH intermediate, which is the reason for the activity enhancement.

Lost motion is utilized to characterize the transmission accuracy of gear reducers and is commonly evaluated through the hysteresis curve method. In examinations of lost motion for large and medium-sized reducers, the default practice is the application of the equal torque gradient loading method. Nonetheless, for small-sized reducers, this approach is deemed inappropriate due to the constraints of the servo motor loading resolution. This study reveals that employing a larger unit torque for equal torque gradient loading can modify the shape of the hysteresis curve and influence the assessment of lost motion. As a result, this paper introduces two innovative loading techniques, namely equal position gradient loading and uniform speed loading, to address the limitations of equal torque gradient loading and reduce the demands on testing equipment. Subsequent experiments validate the issues associated with equal torque gradient loading, confirm the effectiveness of the two new loading techniques, and yield more comprehensive hysteresis curves. It is important to note that all three loading methods are influenced by the loading rate, but the two novel methods can mitigate the effects of loading rate dependency.

Amorphous nanomaterials have drawn extensive attention owing to their unique features, while amorphization on noble metal nanomaterials still remains formidably challenging. Herein, we demonstrate a universal strategy to synthesize amorphous Pd-based nanomaterials from unary to quinary metals through the introduction of phosphorus (P). The amorphous Pd-based nanoparticles (NPs) exhibit generally promoted oxygen reduction reaction (ORR) activity and durability compared with their crystalline counterparts. Significantly, the quinary P-PdCuNiInSn NPs, benefiting from the amorphous structure and multimetallic component effect, exhibit mass activities as high as 1.04 A mgPd–1and negligible activity decays of 1.8% among the stability tests, which are much better than values for original Pd NPs (0.134 A mgPd–1and 28.4%). Experimental and theoretical analyses collectively reveal that the synergy of P-induced amorphization and the expansion of metallic components can considerably lower the free energy changes in the rate-determined step, thereby explaining the positive correlation with the catalytic activity.

A high-precision testing bench is required for accurately assessing the dynamic Transmission error (TE) of precision reducers. However, the relationship between the testing bench’s accuracy and deviations in testing conditions has not been determined, leading to an inaccurate evaluation of its metrological performance. In this study, we examined the accuracy characteristics of the testing bench. We analyzed the structure of the bench and identified the main error sources that affect its accuracy, including mechanical system deviation, sensor measurement deviation, and testing condition deviation. In addition, we studied the mechanism of these error sources and proposed a method to convert drive speed deviation and load torque deviation into angle deviation by using a three-dimensional characterization model of lost motion. Based on the expression of TE, we proposed an evaluation method for the accuracy of the testing bench. Furthermore, we presented the methods for calculating the error sources and proposed a method for determining the TE of adjacent units based on the specific coupling structure. Finally, we conducted practical tests to validate the effectiveness of the proposed method in evaluating the accuracy characteristics of the testing bench. We found that the relative test error between quasi-static TE and dynamic TE is small, and more accurate test results are obtained when applying an appropriate load.

Pore structure is the primary control factor of pore fluid distribution and flow capacity. It is an important content in reservoir evaluation. The capillary pressure curve is a major method to obtaining pore structure characteristics. The NMR T2spectrum reflects pore size distribution. Both of them have a connection. Investigating methods of converting the NMR T2spectrum into a capillary pressure curve is the key step to use nuclear magnetic resonance (NMR) logging data to quantitatively characterize the pore structure. The pore throat size distribution obtained by the NMR T2spectrum and capillary pressure curve has fractal features. However, studies about the relationship between the capillary pressure curve and NMR T2spectrum by the fractal characteristic have not been reported. This paper studies the relationship between the capillary pressure curve and NMR T2spectrum by the fractal characteristic and proposes a method of converting the NMR T2spectrum to the capillary pressure curve. To fully consider the influence of the pore structure, the dimension coefficient is set to be a variable that is related to the pore structure. The proposed model is verified by 20 rock samples in Nanpu sag, China by comparing the new method of calculate capillary pressure curves and experimental capillary pressure curves. Results show that the new method improves the accuracy of the conversion method between the NMR T2spectrum and capillary pressure curve in the complex pore structure reservoirs. This research provides a new way to enrich pore structure characteristic parameters for high-precision reservoir evaluation and has certain guidance and reference significance for the research work of petrophysics, reservoir evaluation, and NMR logging data application.

Ovarian cancer is one of the most common cancers in female genital organs. Since ovarian cancer is associated with plenty of pathological features, most of the computational diagnostic methods can classify the type of ovarian cancer, but cannot accurately detect the locations. In this work, we propose a novel deep learning model architecture, named AT-Cascade R-CNN. In our model, attention mechanism is firstly involved into Cascade R-CNN. We use attention-based activation function in the backbone network to achieve nonlinear activation. Data experiments are conducted on dataset collected from the affiliated hospital of Qingdao University. After training, our model performs better than baseline Cascade R-CNN and Cascade R-CNN+CBAM in detecting the lesions. The MAP of our model reaches 93.2%, which is better than some state-of-the-art deep learning models. It realises accurate detection of ovarian cancer lesions for the first time, and accurately classifies the lesions.

The leishmaniases, caused by Leishmaniaspecies of protozoan parasites, are neglected tropical diseases with millions of cases worldwide. Current therapeutic approaches are limited by toxicity, resistance, and cost. N-Myristoyltransferase (NMT), an enzyme ubiquitous and essential in all eukaryotes, has been validated via genetic and pharmacological methods as a promising anti-leishmanial target. Here we describe a comprehensive structure–activity relationship (SAR) study of a thienopyrimidine series previously identified in a high-throughput screen against LeishmaniaNMT, across 68 compounds in enzyme- and cell-based assay formats. Using a chemical tagging target engagement biomarker assay, we identify the first inhibitor in this series with on-target NMT activity in leishmania parasites. Furthermore, crystal structure analyses of 12 derivatives in complex with Leishmania majorNMT revealed key factors important for future structure-guided optimization delivering IMP-105 (43), a compound with modest activity against Leishmania donovaniintracellular amastigotes and excellent selectivity (>660-fold) for LeishmaniaNMT over human NMTs.

LaNiO3coated LiNi0.5Co0.2Mn0.3O2(NCM523) materials were successfully synthesized via a sol-gel method. Morphological measurements indicate that LaNiO3layer with ∼10 nm thickness was coated uniformly on parent NCM523 materials. The capacity retention of LaNiO3-coated NCM523 after 50 cycles is higher than bare substrate. Meanwhile, the coated samples also show improved rate capability. The favorable LaNiO3layer endows decreased charge transfer impedance, leading to enhanced electrochemical performance.LaNiO3coated LiNi0.5Co0.2Mn0.3O2materials were successfully synthesized via a sol-gel method. The coated LiNi0.5Co0.2Mn0.3O2shows improved cyclability under higher voltage (4.5 V) and rate performance. Such enhancement of electrochemical performance could result from LaNiO3with good electrical conductivity and stability acting as a protective layer to inhibit unfavorable side reactions.

We introduce the concept of cyber-physical-social-mediated communication (CPS-C) and analyze why CPS-C is better than pure cyber-mediated communication for two popular applications: mobile social networks and mobile crowd sensing. CPS-C utilizes its unique characteristics (i.e., cyber-physical synchronization, human intelligence, and physical displacement) to bring benefits, including natural boundary of information exposure, tangible interaction, targeting receivers on the fly, decentralization, and piggybacking. As a result, energy efficiency and user experience are improved. We highlight the existence of human-machine intelligence in the communication process, which has rarely been addressed.

Crowdsensing is becoming a hot topic because of its advantages in the field of smart city. In crowdsensing, task allocation is a primary issue which determines the data quality and the cost of sensing tasks. In this paper, on the basis of the sweep covering theory, a novel coverage metric called 't-sweep k-coverage' is defined, and two symmetric problems are formulated: minimise participant set under fixed coverage rate constraint (MinP) and maximise coverage rate under participant set constraint (MaxC). Then based on their submodular property, two task allocation methods are proposed, namely double greedy (dGreedy) and submodular optimisation (SMO). The two methods are compared with the baseline method linear programming (LP) in experiments. The results show that, regardless of the size of the problems, both two methods can obtain the appropriate participant set, and overcome the shortcomings of linear programming.