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Unique lacustrine mixed siliciclastic-carbonate sedimentary rocks accumulated during the deposition of the second member of the Paleogene Funing Formation in the Jinhu and Gaoyou Sags, Subei Basin, which has significant scientific and petroleum exploration value. The reservoir characteristics of the deposits were summarized by means of core observation, petrographic analyses, scanning electron microscope analysis, porosity and permeability tests, carbonate content measurement, and X-ray diffraction clay mineral analyses. These special deposits consisting of siliciclastics and allochems were mainly found in a shallow lake at the front and side of the deltas and also were found in the gravity flow deposits of a deep lake. The results indicate that the lithology and dissolution of early calcite blocks of cement together influence the reservoir performance. From a sedimentological perspective, two types of lacustrine deposits are present in the study area. The first one preserves massive or graded bedding composed of allochems (ooids and intraclasts) and siliciclastics (mainly quartz) of various grain sizes as well as marl; this type of reservoir is poorly sorted. The second one preserves wavy cross-lamination composed of ooids and quartz of similar size; this type of reservoir is well sorted. The diagenesis of the reservoirs in the study area mainly includes compaction, cementation, and dissolution. Reservoirs with massive or graded bedding were impacted by compaction, with current porosity of less than 5%. Reservoirs with wavy cross-lamination could form high-quality reservoirs with porosity of up to 22%, in which the secondary dissolution pores represent the main type of porosity. The early calcite cementation may have inhibited compaction prior to the formation of solution vugs and the formation of high-quality reservoirs.

Unmanned Aerial Vehicles (UAVs) dynamic encirclement is an emerging field with great potential. Researchers often get inspiration from biological systems, either from macro-world like fish schools or bird flocks etc, or from micro-world like gene regulatory networks (GRN). However, most swarm control algorithms rely on centralized control, global information acquisition, and communications among neighboring agents. In this work, we propose a distributed swarm control method based purely on vision and GRN without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purely on their installed omnidirectional vision sensors. A finite-state-machine (FSM) describing the behavioral model of each drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively in an integrated way. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments., This work has been submitted to the IEEE Robotics and Automation Letters (RA-L) for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

In the vanilla federated learning (FL) framework, the central server distributes a globally unified model to each client and uses labeled samples for training. However, in most cases, clients are equipped with different devices and are exposed to a variety of situations. There are great differences between clients in storage, computing, communication, and other resources, which makes unified deep models used in traditional FL cannot fit clients’ personalized resource conditions. Furthermore, a great deal of labeled data is needed in traditional FL, whereas data labeling requires a great investment of time and resources, which is hard to do for individual clients. As a result, clients only have a vast amount of unlabeled data, which goes against the federated learning needs. To address the aforementioned two issues, we propose Semi-HFL, a semi-supervised federated learning approach for heterogeneous devices, which divides a deep model into a series of small submodels by inserting early exit branches to meet the resource requirements of different devices. Furthermore, considering the availability of labeled data, Semi-HFL introduces semi-supervised techniques for training in the above heterogeneous learning process. Specifically, two training phases are included in the semi-supervised learning process, unsupervised learning on clients and supervised learning on the server, which makes full use of clients’ unlabeled data. Through image classification, text classification, next-word prediction, and multi-task FL experiments based on five kinds of datasets, it is verified that compared with the traditional homogeneous learning method, Semi-HFL not only achieves higher accuracies but also significantly reduces the global resource overhead.

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework FLEE , which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by FLEE consume less global computing resource in the inference stage.

In hazardous gas leakage accidents in chemical clusters, quickly and accurately locating the diffusion source via mobile sensors (searcher) can effectively speed up emergency response. While state-of-the-art researches designed kinds of autonomous sensing and searching techniques, e.g. cognitive-based strategies to complete the localization of the leakage source; the current studies cannot meet chemical cluster’s requirements with complex road network constraints and spatial scales. To address this problem, we firstly combine a cognitive search strategy with great theoretical performance (Entrotaxis algorithm) with an intermittent search strategy and propose a fresh searching algorithm, namely Entrotaxis-Turn (ET). The hybrid algorithm can make use of the triggered turn motion to bypass obstacles to avoid falling into a loop, thereby improving the success rate and saving the searching time. Secondly, we extend the ET algorithm to the multi-robot collaboration pattern (Multi-ET algorithm), in which each mobile sensor shares the information collected along its route with each other. To reveal the performance of the proposed algorithms, a simulation scene is built in a typical chemical cluster scene and diffused gas is generated based on the advection-diffusion equation. Through Monte Carlo simulations, the optimal parameter combinations in the ET and Multi-ET algorithm are obtained. Finally, a verification experiment is carried out based on the simulation scene generated by the computational fluid dynamics (CFD) model. Results show that compared with the Entrotaxis algorithm, the efficiency and success rate of the ET algorithm are dramatically improved. Moreover, the Multi-ET algorithm obtains the optimal source searching performance.

This paper investigates the feasibility of applying the broad learning system (BLS) to realize a novel TakagiSugenoKang (TSK) neuro-fuzzy model, namely a broad learning-based dynamic fuzzy inference system (BL-DFIS). It not only improves the accuracy and interpretability of neuro-fuzzy models but also solves the challenging problem that models are incapable of determining the optimal architecture autonomously. BL-DFIS firstly accomplish a TSK fuzzy system under the framework of BLS, in which extreme learning machine auto-encoder (ELM-AE) is employed to obtain feature representation in a fast and analytical way, and an interpretable linguistic fuzzy rule (ILFR) is integrated into the enhancement node to ensure the high interpretability of the system. Meanwhile, the extended-enhancement unit (EEU) is designed to achieve the first-order TSK fuzzy system. On the other hand, a dynamic incremental learning algorithm with internal pruning and updating mechanism is developed for the learning of BL-DFIS, which enables the system to automatically assemble the optimal structure to obtain a compact rule base and an excellent classification performance. Experiments on benchmark datasets demonstrate that the proposed BL-DFIS can achieve a better classification performance than some state-of-the-art nonfuzzy and neuro-fuzzy methods, simultaneously using the most parsimonious model structure.

Surface-enhanced Raman scattering (SERS) technique based on surface plasmon resonance has been considerably investigated in recent years due to its superior sensitivity in the detection of organic or biological molecules at trace levels. However, most research usually focuses on artificial architectures as SERS substrates that always have a complex and expensive micro-nanofabrication process. The high cost of masks for SERS substrates becomes a key obstacle for the widespread commercialization of SERS technology. In this paper, a biomimetic SERS substrate composed of silver-coated nanopillar arrays on the top of a cicada wing was advanced to overcome these challenges as both substrates and masks. Benefiting from the high near-field plasmon resonance coupling at the limited space among neighboring nanopillars, a dramatically increased SERS signal can be achieved using rhodamine 6G (R6G) as a model molecule. Encouragingly, the analytical enhancement factor of the order of more than 10

Fueled by the advances in the Internet of Things, and the growing capacity of smart mobile devices at the edge of the Internet, we have witnessed a growing trend in research and development for edge computing and edge storage, which extends the abilities of single mobile device on the edge through on-demand collaboration among multiple geographically distributed mobile devices. In this paper, we address several technical challenges that are unique for collaborative storage at the edge due to unique characteristics of mobile devices. First, we formalize the collaborative storage problem as an optimization problem. Second, we design an Acceleration Algorithm for Collaborative Storage, called A2CS, based on the architecture of Alternating Direction Method of Multipliers (ADMM). Specifically, we use the Nesterov's Acceleration strategy and the step size rules in the process of updating variables and determining the optimal speed of convergence. We develop a novel collaborative storage policy in order to guide the whole lifecycle of collaborative storage. Finally, we conduct a series of experiments for acceleration performance analysis and validation. We show that A2CS delivers a better convergence performance compared with ADMM and ADMM-OR, and a better utility performance compared with Average Distribution Strategy and Distance Preferred Distribution Strategy.

Research on multiple Unmanned Aerial Vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications such as smart cities and precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This paper is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a Multi-UAV Cooperative Path Optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle avoidance selection strategy with two mechanisms in mind: 1) When some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance; 2) When all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas-Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3D simulation experiment environment based on UE4 platform, and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs, but also has satisfactory formation flexibility and surveillance accuracy.

Low temperatures restrict the growth of the grapevine industry. The DREB transcription factors are involved in the abiotic stress response. Here, we isolated the VvDREB2A gene from Vitis vinifera cultivar ‘Zuoyouhong’ tissue culture seedlings. The full-length VvDREB2A cDNA was 1068 bp, encoding 355 amino acids, which contained an AP2 conserved domain belonging to the AP2 family. Using transient expression in leaves of tobacco, VvDREB2A was localized to the nucleus, and it potentiated transcriptional activity in yeasts. Expression analysis revealed that VvDREB2A was expressed in various grapevine tissues, with the highest expression in leaves. VvDREB2A was induced by cold and the stress-signaling molecules H2S, nitric oxide, and abscisic acid. Furthermore, VvDREB2A-overexpressing Arabidopsis was generated to analyze its function. Under cold stress, the Arabidopsis overexpressing lines exhibited better growth and higher survival rates than the wild type. The content of oxygen free radicals, hydrogen peroxide, and malondialdehyde decreased, and antioxidant enzyme activities were enhanced. The content of raffinose family oligosaccharides (RFO) also increased in the VvDREB2A-overexpressing lines. Moreover, the expression of cold stress-related genes (COR15A, COR27, COR6.6, and RD29A) was also enhanced. Taken together, as a transcription factor, VvDREB2A improves plants resistance to cold stress by scavenging reactive oxygen species, increasing the RFO amount, and inducing cold stress-related gene expression levels.

Perfluorooctanoic acid (PFOA) is a widely distributed persistent organic pollutant in the environment. In recent years, various advanced oxidation processes (AOP) have been explored to degrade PFOA in aqueous solution, but the roles of reactive oxygen species (ROS) such as hydroxyl radical (·OH), superoxide anion radical (O2·−), and hydroperoxide anion (HO2−) on PFOA degradation were not clear or with even controversial conclusions. Here, we investigated three AOP systems, including the catalytic hydrogen peroxide process, the electro-Fenton system, and the anodic oxidation process, capable of producing different ROS, for PFOA degradation. Essentially no fluoride ion was detected after the reaction if only homogeneous free ·OH, O2·−, and HO2− were present in the system indicating their ineffectiveness in PFOA degradation. Further experiments demonstrated that valence band holes (h+) and adsorbed hydroxyl radicals (·OH) on the anode surface governed the electrochemical mineralization of PFOA. In addition, the simultaneous TOC removal and defluorination by anodic oxidation degradation using a boron-doped diamond (BDD) electrode implied that the intermediates of PFOA degradation were rapidly decomposed on the electrode surface. This research provides insights on the role of ROS during PFOA degradation by AOP technologies.

In view of the fact that the types of tasks display an upward trend as the growth of service demands, and the different types of tasks arrive at the system without regularity. Moreover, the resources deployed in cloud are insufficiency to be flexibly provisioned in the face of obvious workload fluctuations. In this paper, we present a method of elastic resource provisioning using date clustering in cloud service platform. The framework of method consists of three core components. In workload classification, we propose a clustering ensemble method, which utilizes a novel distance decision-making method to obtain the final results. Our method can effectively partition the arriving tasks into several clusters based on similarity among tasks. For each cluster, we forecast the amount of tasks arriving at next moment by prediction model based on time-series to provide reference for the follow-up resource provisioning. Afterwards, an energy-saving resource provisioning method is designed to dynamically provide resources for tasks in each cluster. We implement the experiments and the results show that our method achieves 92.3%, 91.2%, and 3679.2 kWh respectively in terms of guarantee ratio, resource utilization and total energy consumption, which demonstrates the effectiveness of proposed method for dynamic resource provisioning.

Because use and dosing of mood stabilizers (MSs) to treat bipolar disorder (BD) patients in Asia are not well documented, we examined prevalence and clinical correlates of treatment of Asian BD patients with relatively high doses of MSs.We conducted a pharmacoepidemiological survey across 13 Asian countries and territory in the Research on Asian Psychotropic Prescription Patterns Consortium. Mood stabilizer doses were converted to lithium carbonate equivalents (Li-eq milligrams per day). We compared relatively high (900 Li-eq mg/day) versus lower MS doses by bivariate comparisons, followed by multivariable linear regression to identify factors associated with higher MS doses.Among 1647 participants, MS dose averaged 584 (confidence interval, 565-603 Li-eq mg/d). Preliminarily, the 13.1% of the subjects given greater than 900 mg/d versus those given lower doses were younger, male, currently hospitalized, not currently depressed, and reported lifetime suicidal ideation; they also received relatively high doses of antipsychotics, received electroconvulsive treatment within the previous 12 months, and had greater ratings of tremors and sedation. By linear regression modeling, the mean proportion given high doses of MS was associated significantly and independently with higher doses of antipsychotics, younger age, male sex, hospitalized, more years of illness, country, higher body mass index, recent electroconvulsive treatment, and being in illness remission.Relatively high doses of MSs for BD are prevalent, but vary markedly among Asian countries, and are particularly likely among young males, ill for many years, and given high doses of antipsychotics or ECT. These characteristics allow better identification of patient profiles that can guide treatment of BD patients.

Drug association (DDIs) prediction is also called drug interaction prediction, which refers to the interactions between drug and drug that lead to unexpected side effects when two or more drugs are taken simultaneously or successively. Previous studies on DDIs prediction using methods such as molecular representation and network embedding were extremely complex, expensive and time-consuming, and were limited in acquiring rich neighborhood information about drug entities and their surroundings during the forecasting process. A drug linkage prediction method based on knowledge graphs and hybrid neural networks was proposed based on the deficiencies of the above methods. This method is mainly based on methods such as knowledge graphs, graph convolutional network, Convolutional-BiLSTM network and attention mechanisms to solve the limitations in acquiring rich neighborhood information about KG entities during the forecasting process. It transforms drug linkage prediction research into a link prediction problem and views drug relationships with known interactions as edges in the interaction graph. It can effectively discover interactions of unknown drugs; meanwhile, performance comparisons are performed with existing DDIs prediction methods. The results show that higher performance is achieved in terms of indicators such as ACC and F1 values, which validate the effectiveness of the model. Finally, future directions in this field are proposed based on an analysis and summary of challenges faced by current DDIs predictions.

Background Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes and causes of blindness in developed countries. Our study was designed to identify immune-related genes involved in the progression of proliferative diabetic retinopathy (PDR). Methods The “GSE102485” dataset of neovascular membrane samples (NVMs) from type 1 and 2 diabetes mellitus patients was downloaded from the Gene Expression Omnibus database. Functional enrichment analyses, protein–protein interaction network (PPI) construction, and module analysis of immune pathways in NVMs and controls were conducted via Gene Set Enrichment Analysis and Metascape. Results The significantly upregulated hallmark gene sets in DR2 and DR1 groups were involved in five immune pathways. Only CCR4, CXCR6, C3AR1, LPAR1, C5AR1, and P2RY14 were not previously reported in the context of PDR molecular pathophysiology. Except for P2RY14, all of the above were upregulated in retinal samples from experimental diabetes mouse models and human retina microvascular endothelial cells (HRMECs) treated with high glucose (HG) by quantitative Real Time Polymerase Chain Reaction (qRT-PCR). Conclusion The genes identified herein provide insight into immune-related differential gene expression during DR progression.

Low-resistivity pay have been found throughout the world, the identification and characterization of low-resistivity pay is very challenging and important for the development of oil fields. The genesis of the low-resistivity oil pay is complex, and the logging response characteristics are variable. The weak difference in resistivity between the oil pay and the adjacent water pay makes it difficult to identify kinds of fluids by resistivity log analysis, which reduces the overall exploration benefit of the oilfield. Therefore, it is very important to study the genesis and identification technology of the low-resistivity oil pay. In this paper, we first analyzed the core experimental results such as X-ray diffraction scanning electron microscope, mercury intrusion, phase permeability, nuclear magnetic resonance, physical properties, electric petrophysical experiment, micro-CT technology and rock wettability, etc. Comprehensive analysis of the reservoir characteristics shows that the development of low-resistivity oil pays in the study area is controlled by irreducible water saturation and high gamma ray sandstone. The complicated pore structure and rock hydrophilicity are the factors that lead to the increase of irreducible water saturation. Then, the salinity of formation water and the invasion of drilling fluid also have a certain influence on the change of reservoir resistivity. According to the controlling factors of the low- resistivity oil pay, we extract the sensitive parameters to the logging response, amplify the difference between oil and water pay, and use the AC-RILD, SP-PSP, GR*GR*∆SP-RILD and(RILM-RILD)/RILD—RILD cross-plots, etc. Various methods such as cross-plots method, overlap method and movable water analysis are mutually constrained to identify low-resistivity oil pays. In the case study, the comprehensive application of the above identification flow path can effectively improve the accuracy of fluid recognition step by step. It provides reference for identifying more low-resistivity reservoirs with similar geological conditions.

The spectra of generalized Cayley graphs of finite abelian groups are investigated in this paper. For a generalized Cayley graph [Formula: see text] of a finite group [Formula: see text], the canonical double covering of [Formula: see text] is the direct product [Formula: see text]. In this paper, integral generalized Cayley graphs on finite abelian groups are characterized, using the characterization of the spectra of integral Cayley graphs. As an application, the integral generalized Cayley graphs on [Formula: see text] and [Formula: see text] are investigated, where [Formula: see text] and [Formula: see text] are odd prime numbers.

The multiple unmanned aerial vehicles (UAVs) system is highly sought after in the fields of emergency rescue and intelligent transportation because of its strong perception and extensive coverage. Formulating a reasonable task scheduling scheme is essential to raising the task execution efficiency of the system. However, the dynamics of task arrival and the heterogeneity of UAV performance make it more difficult for multiple UAVs to complete the tasks. To address these issues, this paper focuses on the multi-UAV scheduling problem and proposes a method of rolling-inspired scheduling for emergency tasks by heterogeneous UAVs (RISE). In order to ensure that emergency tasks can be allocated to UAVs in a real-time manner, a task grouping strategy based on a density peaks (DP) clustering algorithm is designed, which can quickly select UAVs with matching performance for the tasks arriving at the system. Furthermore, an optimization model with multiple constraints is constructed, which takes the task profit and UAV flight cost as the objective function. Next, we devise a rolling-based optimization mechanism to ensure that the tasks with shorter deadlines are executed first while maximizing the objective function, so as to obtain the optimal task execution order for each UAV. We conduct several groups of simulation experiments, and extensive experimental results illustrate that the number of tasks successfully scheduled and the utilization rate of UAVs by RISE are superior to other comparison methods, and it also has the fastest running time. It further proves that RISE has the capability to improve the completion rate of emergency tasks and reduce the flight cost of multiple UAVs.