Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking for Disaster Response

In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction Algorithm Coupled with a Packet Forwarding Algorithm for Drone-To-Ground Network Establishment. We Specifically Present Two Novel Drone Location-Based Solutions (I.e., Heuristic Greedy, and Learning-Based) in Our Packet Forwarding Approach to Support Application Requirements. These Requirements Involve Improving Connectivity (I.e., Optimize Packet Delivery Ratio and End-To-End Delay) Despite Environmental Obstacles, and Improving Efficiency (I.e., by Lower Energy Use and Time Consumption) Despite Energy Constraints. We Evaluate Our Scheme with State-Of-The-Art Networking Algorithms in a Trace-Based DRM FANET Simulation Testbed Featuring Rural and Metropolitan Areas. Results Show that Our Strategy overcomes Obstacles and Can Achieve 81-To-90% of Network Connectivity Performance Observed under No Obstacle Conditions. in the Presence of Obstacles, Our Scheme Improves the Network Connectivity Performance by 14-To-38% While Also Providing 23-To-54% of Energy Savings in Rural Areas; the Same in Metropolitan Areas Achieved an Average of 25% Gain When Compared with Baseline Obstacle Awareness Approaches with 15-To-76% of Energy Savings

Antagonism of miR-21 Reverses Epithelial-Mesenchymal Transition and Cancer Stem Cell Phenotype through AKT/ERK1/2 Inactivation by Targeting PTEN

BACKGROUND: Accumulating evidence suggested that epithelial-mesenchymal transition (EMT) and cancer stem cell (CSC) characteristics, both of which contribute to tumor invasion and metastasis, are interrelated with miR-21. MiR-21 is one of the important microRNAs associated with tumor progression and metastasis, but the molecular mechanisms underlying EMT and CSC phenotype during miR-21 contributes to migration and invasion of breast cancer cells remain to be elucidated. METHODOLOGY/PRINCIPAL FINDINGS: In this study, MDA-MB-231/anti-miR-21 cells were established by transfected hsa-miR-21 antagomir into breast cancer MDA-MB-231 cells. EMT was evaluated by the changes of mesenchymal cell markers (N-cadherin, Vimentin, and alpha-SMA), epithelial cell marker (E-cadherin), as well as capacities of cell migration and invasion; CSC phenotype was measured using the changes of CSC surface markers (ALDH1 and CD44), and the capacity of sphereforming (mammospheres). We found that antagonism of miR-21 reversed EMT and CSC phenotype, accompanied with PTEN up-regulation and AKT/ERK1/2 inactivation. Interestingly, down-regulation of PTEN by siPTEN suppressed the effects of miR-21 antagomir on EMT and CSC phenotype, confirming that PTEN is a target of miR-21 in reversing EMT and CSC phenotype. The inhibitors of PI3K-AKT and ERK1/2 pathways, LY294002 and U0126, both significantly suppressed EMT and CSC phenotype, indicating that AKT and ERK1/2 pathways are required for miR-21 mediating EMT and CSC phenotype. CONCLUSIONS/SIGNIFICANCE: In conclusion, our results demonstrated that antagonism of miR-21 reverses EMT and CSC phenotype through targeting PTEN, via inactivation of AKT and ERK1/2 pathways, and showed a novel mechanism of which might relieve the malignant biological behaviors of breast cancer

Intelligent orchestration of computation and networking for drone swarm applications

[EMBARGOED UNTIL 5/1/2024] Drone swarms that are equipped with high-resolution sensors and video cameras can benefit many applications (e.g., disaster response management, smart farming, traffic control). This requires sensor localization and access to edge computation and networking resources to provide environmental situational awareness in the applications. Orchestration of the computation and networking resources in practice are performed in isolation, and do not sufficiently account for factors such as limited battery life of drones that impacts geographical area coverage in application missions. Moreover, prior works focus mainly on computation offloading, and do not account for the factors that impact drone swarm communication such as intermittent network link failures and environmental obstacles (e.g., physical buildings, strong winds) that impact video transmission, data transfers and thus disrupt the application mission plans. To address above knowledge gaps, this innovative research dissertation aims to investigate a trans-formative co-design of localizing, computation offloading, and control networking in drone swarms that serve diverse application missions. The research goal is to first develop awareness models in terms of application environments with sensors and drone mobility, which can then guide the creation of edge computation and networking resource orchestration algorithms in an intelligent and joint manner. To this end, specific novel research objectives include: (i) develop awareness models for system awareness (energy consumption), environment awareness (physical obstacle, wind), and mobility awareness (ground sensor location, drone trajectory) in drone swarm application scenarios; (ii) create intelligent computation offloading and control networking algorithms to leverage the awareness models to ensure optimal scheduling of data processing tasks across edge, cloud and network resources to meet application mission requirements; and (iii) implement intelligent algorithms in a Drone Swarm Localizing, Computation Offloading, Control and Networking framework viz., "DroneCOCoNet" to validate the integration benefits in drone-aided applications such as disaster response management and smart farming. Evaluation shows that our intelligent orchestration strategies build with the system, environment, and mobility models benefits: i) computation offloading procedure which achievement at least 240 percent higher median episode reward than baseline non-intelligent scheme, and ii) the adaptive on the network and video properties with at least 91 percent of throughput performance of the oracle baseline approach with at least 32 dB value (good video quality as perceived by users) for PSNR after transmission. This dissertation also includes the development of drone-network testbeds in terms of trace-based, learning-based, and scientific workflow supported. In addition to this, the application use cases on how drone swarms can benefit in various scenarios, i.e., disaster management, last-mile parcel delivery, and smart farming are also addressed.Includes bibliographical references

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks

Estimating the Sizes of Populations at High Risk for HIV: A Comparison Study

<div><p>Objectives</p><p>Behavioral interventions are effective strategies for HIV/AIDS prevention and control. However, <i>implementation</i> of such strategies relies heavily on the accurate estimation of the high-risk population size. The multiplier method and generalized network scale-up method were recommended to estimate the population size of those at high risk for HIV by UNAIDS/WHO in 2003 and 2010, respectively. This study aims to assess and compare the two methods for estimating the size of populations at high risk for HIV, and to provide practical guidelines and suggestions for implementing the two methods.</p><p>Methods</p><p>Studies of the multiplier method used to estimate the population prevalence of men who have sex with men in China published between July 1, 2003 and July 1, 2013 were reviewed. The generalized network scale-up method was applied to estimate the population prevalence of men who have sex with men in the urban district of Taiyuan, China.</p><p>Results</p><p>The median of studies using the multiplier method to estimate the population prevalence of men who have sex with men in China was 4–8 times lower than the national level estimate. Meanwhile, the estimate of the generalized network scale-up method fell within the range of national level estimate.</p><p>Conclusions</p><p>When high-quality existing data are not readily available, the multiplier method frequently yields underestimated results. We thus suggest that the generalized network scale-up method is preferred when sampling frames for the general population and accurate demographic information are available.</p></div

Multiplier estimate and network scale-up estimates compared to the national level estimate.

<p>Multiplier estimate and network scale-up estimates compared to the national level estimate.</p

Reported number compared to the actual percentage of the population size.

<p>Reported number compared to the actual percentage of the population size.</p