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Due to the sharing and open-access characteristics of the wireless medium, wireless sensor networks (WSNs) can be easily attacked by jammers. To mitigate the effects of a jamming attack, one reliable solution is to locate and remove the jammer from the deployed area within the WSN. To realize the jammer’s localization in the WSN, many range-free methods have been proposed. However, most of these methods are sensitive to the distribution of nodes and the parameters of the jammer. For this reason, a jammer location-aware method based on Fibonacci branch search (FBS) is proposed in this article. First, the interference region is estimated by using the interference region mapping service of sensors in wireless sensor networks. Then, the search point is selected in the jamming area and the fitness function is designed according to the average distance from the search point to the boundary sensor. According to the basic branch structure and interactive search rules, the global optimal solution is obtained in the jamming area. Finally, the position of the search point with the best fitness value is used as the estimation of the jammer position. Compared with the existing typical range-free methods, rich simulation experiments demonstrate that the FBS algorithm is superior in the location-aware method for jammers with a higher precision and a lower sensitivity to the distribution of nodes and the parameters of the jammer, respectively.

This study intends to investigate the mechanism by how microRNA (miRNA)-23b alleviates kidney damage in septic rats. Herein, septic rat model, control group and sham-operated model were set up to assess kidney tissue damage. Tissues were extracted from the rats and isolated into cells. Then cells were transfected with plasmids expressing miR-23b followed by analysis of the expression of miR-23b, Smad3, TLR4, HMGB1 and autophagy-related proteins (LC3, beclin-1) by western blot and RT-qPCR. The level of TNF-α, IL-6 and BUN and SCr were elevated in the model group and decreased after upregulation of miR-23b with increased LC3-II, Smad3 and Beclin-1 expression. miR-23b mimic group showed highest miR-23b expression followed by miR-23b NC group and miR-23b inhibitor group. The levels of TLR4, and HMGB1 and positive rate of NF-κBp65 in miR-23b mimic group were significantly lower than those in miR-23b inhibitor group (p < 0.05). Importantly, miR-23b directly targeted Smad3 and inhibited its expression. In conclusion, overexpressed miR-23b induces autophagy by promoting Smad3 expression, alleviates kidney damage in septic rats, and reduces inflammation and inactivates NF-κB signaling pathway.

Substrate concentrations greatly influence bacterial growth and metabolism. However, optimal nitrogen concentrations for anammox bacteria in nitrogen-limited environments remain unclear. Here, we observed enhanced nitrogen metabolism and anabolism of anammox bacteria at low nitrogen conditions. Efficient nitrogen removal was achieved at ammonium and nitrite influent concentration of 30 mg/L under HRT of 1 hr, with an average nitrogen removal rate (NRR) of 0.73 kg N/(m3 ·day) in I-ABR composed of four compartments. The highest anammox activity of 6.25 mmol N/ (gVSS·hr) was observed in the fourth compartment (C4) with the lowest substrate levels (ammonium and nitrite of 11.6 mg/L and 7 mg/L). This could be resulted from the highest expression level of genes involved in nitrogen metabolism in C4, which was 1.49-1.67 times higher than that in other compartments. Besides, the second compartment (C2) exhibited the most active anabolism at ammonium and nitrite of 17 mg/L and 13 mg/L, respectively, which contributed to the most active amino acid synthesis and thus the highest EPS (1.35 times higher) in C2. This enhanced amino acid auxotrophy between anammox bacteria with heterotrophs, and consequently, heterotrophs thrived and competed for nitrite. These results hint at the potential application of anammox process in micro-polluted water. PRACTITIONER POINTS: High nitrogen removal and efficient biomass retention at low nitrogen concentrations under short HRT was achieved in I-ABR. Optimal concentrations for anammox nitrogen removal and anabolism were discussed under low nitrogen concentrations. More active anabolism contributed to enhanced amino acid synthesis and thus higher EPS contents. Low substrate levels led to enhanced expression of genes involved in nitrogen metabolism and thus high anammox activity.

Benefit from multi-scale feature pyramid methods, recently single-stage object detectors have achieved promising accuracy and fast inference speed. However, the majority of existing feature pyramid detection techniques only simply describe complex contextual relationships from different scales. Not only are there no effective modules that adaptively extend appropriate semantic information from deeper layers, but the finer spatial localization cues from lower layers are often ignored. In this paper, we present a Local Enhancement and Bidirectional Feature Refinement Network (LFBFR), which includes two optimization methods to achieve remarkable improvements in detection accuracy. Firstly, to make the backbone more suitable for detection task, we modify the pre-trained classification backbone to mitigate the loss of details in small objects due to consecutive decrease of the image resolution. Then we propose a Bidirectional Feature Refinement Pyramid, which can effectively utilize the inter-channel relationship of higher-level features and fine appearance cues from lower-level features by using the attention residual refinement module and the feature reuse module. Ultimately, to assess the performance of the proposed LFBFR, we design a powerful end-to-end single-stage detector called LFBFR-SSD by embedding it into the framework of SSD. Extensive experiments on the PASCAL VOC and MS COCO verify that our LFBFR-SSD outperforms a lot of state-of-the-art detectors while maintaining a real-time speed.

With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption.

In heterogeneous environments, the Internet of Things (IoT) combined with mobile ad hoc network (MANET), i.e., MANET-IoT network, becomes more attractive to end users and economically successful. However, the introduction of MANET potentially makes the system more prone to attacks due to its lack of centralized management, weak connectivity, and resource constraints. To enhance the network robustness to intentional attacks, the critical nodes of the MANET-IoT network should be firstly identified and then protected. Most of the existing methods for identifying critical nodes usually focus on static networks or a single topology snapshot in dynamic networks without considering the correlation between topology snapshots, which cannot effectively deal with the dynamic changes in the topology of MANET-IoT networks. In this paper, a dynamic critical node identification (DCNI) method is proposed. First, we propose a comprehensive metric to measure the node importance in the topology snapshot. Then, we introduce a sliding time window to filter out the topology snapshots which have a close correlation with the current snapshot, and fuse the importance values of the same node in different topology snapshots. Finally, the critical nodes are selected based on the ranking result of fused importance. Thereafter, the port hopping mechanism could be applied to the critical nodes for enhancing network defense capability. The simulation results show that the proposed method is more effective in identifying critical nodes than existing static methods in MANET-IoT networks, and the port hopping mechanism can improve network defense significantly to denial of service (DoS) attacks.

Important location inference is one of the main content in location big data intelligence mining, and it has important application value in tracking target users and LBA. This paper takes regional ID type location data as input. Aiming at the problem that the current inference methods based on the frequency statistics of the work and rest periods do not take into account the difference in user time activity rules, the accuracy of the inference is poor, and a personal important location inference method based on the time state distribution is proposed. The method first divides the mobile user's work and rest time period, and then estimates the parameters of the state distribution of the user registration time in each base station area. Finally, match the estimated result with the work and rest time period and output the home or work label to complete the inference of personal important locations. The experimental results show that compared with the frequency statistics method, this method has higher accuracy rate and lower parameter estimation error, and it has certain reference significance for the inference of personal important locations with regional ID type location as input.

As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.

Although the niche differentiation of anammox bacteria has been extensively observed in a lab-scale reactor, the inherent mechanism for this ecological phenomenon is still elusive. Here, we combined the long-term reactor operation, genome-centered metagenome, and metatranscriptome analyses to gain insight into the substrate competition and niche differentiation of Candidatus Jettenia and Candidatus Brocadia. After 146 days of operation, we found the anammox bacterial population shifted from Ca. Jettenia to Ca. Brocadia in the immobilization-anaerobic baffled reactor (I-ABR) with the ammonium and nitrite concentrations of 30 mg/L. Importantly, the genome and transcript comparisons of Ca. Jettenia and Ca. Brocadia showed that Ca. Brocadia harbored more complete function in cell chemotaxis, flagellar assembly, and two-component system and more redundant function in nitrite reduction, in which the genes were also highly expressed. Ca. Brocadia out-competed Ca. Jettenia at the mainstream condition. Meanwhile, though the highest biomass concentration led to the highest nitrogen removal rate (NRR) in the first compartment (C1), the competition of Ca. Jettenia and Ca. Brocadia could also affect the NRR of different compartments through affecting the bacterial activity. Substrate competition of anammox bacteria led to higher transcript activity of Ca. Jettenia and Ca. Brocadia in the second (C2) and fourth (C4) compartments, respectively. Further, high transcript activity of Ca. Brocadia led to the higher NRR in C4. A comparison of metabolic potential based on the metagenome-assembled genome adds a different dimension for understanding the discrepantly physiological characteristics and competition of anammox bacteria for wastewater treatment.

Wind speed is one of the important monitoring parameters to ensure mine safety. The commonly used mine wind speed sensor can be divided into ultrasonic type, impeller type and hot wire type according to the design principle. The accuracy of the latter two methods is not high enough because they are susceptible to steam and coal dust in mines. The ultrasonic wind speed sensor has better measuring accuracy. In this paper, ultrasonic wind speed sensor, ultrasonic sending and receiving circuit combined with ADC sampling function of the chip, to obtain the received ultrasonic amplitude change data, statistics of ultrasonic waveform changes caused by blowing, according to the number of changes in the wind speed frequency, and then calculate the real-time wind speed.

The coordination and unification of Yin and Yang are the basis of normal human life activities. Along with the age growth and aging of the body, women will suffer from menopausal syndrome during menopause. In addition to the significant changes in the genital system, there are also pathological manifestations in estrogen target points including bone, nerve and cardiovascular systems, due to the imbalance of Yin and Yang. Besides the insufficiency of estrogen, the main cause of menopausal syndrome is the changes in the response of target organs to estrogen. In other words, the biological effects mediated by estrogen receptor(ER) alpha and beta subtypes in target cells are often different or even opposite; the changes of expression level and ratio of ERα and ERβ are also important causes for the abnormal estrogenic effects in target organs and the imbalance of Yin and Yang of the body. Therefore, on one hand, the therapeutic mechanism of drugs is ER-mediated estrogenic effect. On the other hand, the drugs have a regulatory effect on ER subtype expression in target cells and Yin-Yang state in target organs and even organisms, so as to cause further changes in the response of target cells to estrogen or estrogenic components, and exert its therapeutic effects. This paper reviews the pharmacological mechanism of gynecological traditional Chinese medicine in harmonizing Yin and Yang in estrogen-positive target cells and the clinical efficacy in the following aspects, including estrogen and its mechanism, the estrogenic effect of ER in traditional Chinese medicine and the mechanism of ER subtype in balancing Yin and Yang and mediating and regulating the main target tissues in menopausal syndrome treatment.

Security risk assessment is an important part of network security operation. The existing risk assessment methods are mainly divided into qualitative assessment, quantitative assessment and comprehensive assessment. This paper summarizes the advantages and disadvantages of related methods through analyzing, and provides certain assistance for the assessors to choose methods when implementing assessment.

Background Breast cancer is the most frequently diagnosed malignancy among women and the second leading cause of cancer death worldwide. Among which nuclear estrogen receptor (nER) negative breast cancer is always with much poor prognosis. Recently, membrane G protein coupled estrogen receptor (GPER), a newly recognized estrogen receptor has been documented to take essential part in the development and treatment of breast cancer. The present study was designed to investigate the anti nER negative breast cancer effect of cryptotanshinone (CPT), an important active compound of traditional Chinese medicine Danshen and its possible molecular pathway. Methods The following in vitro tests were performed in nER negative but GPER positive breast cancer SKBR-3 cells. The effect of CPT on cell proliferation rate and cell cycle distribution was evaluated by MTT cell viability test and flow cytometry assay respectively. The role of PI3K/AKT pathway and the mediated function of GPER were tested by western blot and immunofluorescence. Technique of gene silence and the specific GPER agonist G-1 and antagonist G-15 were employed in the experiments to further verify the function of GPER in mediating the anticancer role of CPT. Results The results showed that proliferation of SKBR-3 cells could be blocked by CPT in a time and dose dependent manner. CPT could also exert antiproliferative activities by arresting cell cycle progression in G1 phase and down regulating the expression level of cyclin A, cyclin B, cyclin D and cyclin-dependent kinase 2 (CDK2). The antiproliferative effect of CPT was further enhanced by G-1 and attenuated by G-15. Results of western blot and immunofluorescence showed that expression of PI3K and p-AKT could be downregulated by CPT and such effects were mediated by GPER which were further demonstrated by gene silence test. Conclusion The current study showed that the antiproliferative action of CPT on SKBR-3 cells was realized by inhibition of GPER mediated PI3K/AKT pathway. These findings provide further validation of GPER serving as useful therapeutic target.

The study aimed to illuminate the role of G protein coupled estrogen receptor( GPER) and its mediated PI3 K/AKT signaling pathway in cryptotanshinone( CPT) induced apoptosis of breast cancer SKBR-3 cells,which is GPER positive and ER negative.The apoptosis rate of SKBR-3 cells was tested by Annexin V-FITC/PI staining and apoptosis effector caspase-3 was determined by Western blot. The key proteins in PI3 K/AKT signaling pathway mediated by GPER were detected by Western blot and immunofluorescence technique. Meanwhile,the agonist G1 and antagonist G15 of GPER and antagonist LY294002 of PI3 K were employed in the test to further clarify the effect of GPER and PI3 K/AKT pathway. The results indicated that the apoptosis rate was increased from 4. 7% to46. 1% and 69. 0% after treatment with 0,5,10 μmol·L~(-1) CPT for 48 h( P0. 01). The expression of PI3 K,AKT and p-AKT were inhibited( P0. 05 or P0. 01),while caspase-3 level increased obviously after treatment with CPT( P0. 01). Importantly,inhibitory effect of PI3 K/AKT signaling pathway by CPT was further enhanced by G1 and attenuated by G15. LY294002 also induced a further inhibition of expression of AKT and p-AKT. The mean fluorescence intensity of AKT and p-AKT could be decreased by CPT. Furthermore,CPT could downregulate GPER expression in SKBR-3 cells( P0. 01),which could be inhibited by G1 and enhanced by G15.In conclusion,CPT could induce the apoptosis of ER negative and GPER positive breast cancer SKBR-3 cells and the molecular mechanism is related to its regulatory effect of GPER and its mediated PI3 K/AKT signaling pathway.

Identifying critical nodes from the point of view that destructiveness equals to criticality, the traditional identification methods usually consider the degree of damage but does not analyze the type of damage. The critical nodes identified have a certain universality, but the pertinence is not strong. In this paper, a critical node identification method based on the analysis of network partition for Ad Hoc network is proposed. Firstly, the Kernighan-Lin algorithm is applied to group the nodes in the network, and then the boundary nodes between different groups are selected from the grouping results. Finally, the boundary nodes are re-screened by combining the node’s degree value to select the nodes that can lead to the partition of the network. The experimental results show that the failure of identified critical nodes can directly lead to network partitioned into multiple subnets, destroy the connectivity of the network seriously.

Green pepper automatic picking has been a long-standing challenge in agriculture due to the similar color between green peppers and green leaves. To tackle this intractable problem, we tried to distinguish between them by using hyperspectral information as prior knowledge. As our core insight, a novel optical filter was designed as a pre-processing tool to find valuable wavelengths where peppers differ a lot from leaves. To this end, firstly, the parameters of the optical filter were learned by end-to-end training with a neural network for pixel-wise hyperspectral input. Secondly, the learned optical filter was applied to hyperspectral data to obtain filtering RGB images, which will be sent to further segmentation framework. Thereby, a two-stage method for green pepper segmentation was proposed, and promising results were achieved owing to the incorporation of the optical filter.

Vidarabine (ARA) was one of the earliest marine-related compounds to be used clinically for antiviral therapy, however, its fast metabolism is the main defect of this drug. To overcome this, we designed and synthesized a group of phosphamide-modified ARA compounds using ProTide technology. With a phosphamide modification, these compounds could become the substrate of specific phospholipase enzymes expressed in the liver. Among all 16 synthesized compounds, most showed stronger activity against herpes simplex virus type 1 (HSV-1) than ARA (EC50 of approximately 10 μM). The top three compounds were compound 2 (EC50 = 0.52 ± 0.04 μM), compound 6 (EC50 = 1.05 ± 0.09 μM) and compound 15 (EC50 = 1.18 ± 0.08 μM) (about 2 times higher than Sp type compound 2). This study provides evidence for use of the phosphamide modification, which could give ARA higher activity and liver cell targeting.

To investigate if steady-state resonant solution exist for any system of weakly interacting waves in a dispersive medium, a trio is considered in the Bretherton equation based on the homotopy analysis method (HAM). Time-independent spectrum was found when all components were travelling in the same direction. Within the trio, the amplitude of longer component is larger than that of shorter one. As the difference of wave number between components in trio increases or the nonlinearity of whole system increases, the amplitudes of all components tends to increase simultaneously. These findings are helpful to enrich and deepen our understanding about resonant solutions in any dispersive medium, especially for a two-dimensional scenario.

Topology is the basis of connectivity analysis and critical nodes identification for Ad Hoc network. The existing methods of topology inference are divided into two types: cooperative and non-cooperative. The cooperative inference methods are usually based on the premise of network access. It usually needs to send a large number of detection packets, and the operation cost is large. The non-cooperative inference methods are usually based on the analysis of a large number of detection data, but the data acquisition is difficult and takes a long time. From the view of coverage, this paper inferred the physical topology of Ad Hoc network with critical transmission distance under the condition that the location information and the number of nodes, the size of the deployment area are known. The simulation results on Exata demonstrate the feasibility of the inference method.

The environmental risk controllability assessment system and its method of controlling volatile organic compounds (VOCs) during remediation of contaminated sites are established in this article based on soil vapor extraction (SVE) technology. According to the properties of VOCs and the technical and operational characteristics of the site remediation process, the environmental risk controllability index system includes environmental risk identification, risk source analysis, and risk assessment. Environmental risk management during site remediation was focused on technical control methods and engineering control technologies. Specifically, acceptance based on risk management was suitable for low-risk levels such as RRI3 and RRI4. Furthermore, control methods for high-level risk (RRI1 or RRI2) could be developed along with transformation and control, combined with the necessary emergency risk plan.

The PaaS platform based on cloud computing technology provides a large number of professional and flexible services in the fields of intelligent voice and computer vision. The large-scale application of 5G technology enables the software and hardware of the Internet of Things to use the PaaS service to improve the intelligence level of the system and reduce development costs. However, the problem of service invocation and service selection in the process of combining the IoT system with multiple PaaS platforms directly affects the interaction between the IoT perception layer and the application layer. This paper proposes a 5G multi-PaaS-oriented IoT dual middleware model. The unified communication protocol DMCP is designed. DMCP implements unified parsing of messages based on variable-length data items, which reduces the protocol conversion between the perception layer terminal device and multiple PaaS platforms. Complexity and high communication efficiency. Designed the PaaS service selection algorithm DMPS, DMPS comprehensively according to the equipment demand preferences and the overall performance of PaaS services, to achieve accurate service selection in the multi-PaaS platform at the application layer. Finally, examples and comparative tests verify the feasibility and effectiveness of the proposed model, protocol and algorithm.

Ubiquitous power Internet of things is an important direction of the development of smart grid, and distribution Internet of things is the application of ubiquitous power Internet of things in the field of distribution. As the terminal unit of state perception, intelligent decision-making and control subject in the distribution Internet of things, intelligent distribution and transformation terminal is the foundation to realize the distribution Internet of things. In order to solve the problems of complex software architecture of traditional distribution and transformation terminal, which is not conducive to expansion, maintenance and transplantation, Microservice + container technology is adopted as the software architecture of intelligent distribution and transformation terminal, which reduces the complexity of development, improves the efficiency of operation and maintenance, and meets the diverse and rapidly changing service requirements of distribution network.

Since sea clutter samples are often contaminated by high-intensity outliers, target detection in nonhomogeneous sea clutter environments is challenging. An adaptively truncated clutter-statistics-based variability index constant false-alarm-rate (TSVI-CFAR) detector of Gaussian clutter in synthetic aperture radar (SAR) imagery is proposed. The proposed method is designed to improve the CFAR detection performance in a heterogeneous multitarget environment. TSVI-CFAR consists of three stages, i.e., clutter truncation, statistical parameter estimation, and CFAR detection. In the clutter truncation stage, the high-intensity outliers in the background window are eliminated through an adaptive threshold-based clutter truncation method. In the statistical parameter estimation stage, the parameters can be accurately estimated through the maximum-likelihood estimator. In the detection stage, the clutter background is categorized using the adaptively truncated clutter. Different types of CFAR detection methods are applied to the pixels under test of the categorized backgrounds. TSVI-CFAR has a higher detection rate and a low observed false alarm rate. The effectiveness of the proposed algorithm is demonstrated using Gaofen-3 SAR data and Envisat-ASAR data.

Exata has excellent performance in wireless communication, but in the process of Ad Hoc network simulation, it can not realize the inference and visualization of network topology. In this paper, we analyze the AODV protocol, and use the information contained in the RREP message to realize the inference of the network topology. The experimental results prove the feasibility of the method.

Critical nodes have global significance for the network, the existing methods on the identification of critical nodes in Ad-hoc network usually based on the premise of topology is known, considering that there exists a link if the two nodes are in the other’s communication range, so the topology we get at last is relatively complex, but there are many links that aren’t exist in real which leading the identification of critical nodes with deviation. For this problem, in the paper we used Delaunay triangulation to optimize the network topology, based on identifying virtual backbone nodes by the unified connected dominating set, we measured the importance of the nodes by the average betweenness centrality. Experimental results show that the method proposed can identify the critical nodes in Ad-hoc networks effectively.