Analysis of Age of Information in Non-terrestrial Networks

Non-terrestrial networks (NTN), particularly low Earth orbit (LEO) satellite networks, have emerged as a promising solution to overcome the limitations of traditional terrestrial networks in the context of next-generation (6G) wireless systems. In this paper, we focus on analyzing the timeliness of information delivery in NTN through the concept of Age of Information (AoI). We propose an on-off process to approximate the service process between LEO satellites and a source node located on the Earth's surface. By utilizing stochastic geometry, we derive a closed-form expression for the time-average AoI in an NTN. This expression also applies to on-off processes with one component following an exponential distribution while the other has its probability density function supported on a bounded interval. Numerical results validate the accuracy of our analysis and demonstrate the impact of source status update rate and satellite constellation density on the time-average AoI. Our work fills a gap in the literature by providing a comprehensive analysis of AoI in NTN and offers new insights into the performance of LEO satellite networks

Coverage Analysis for Cellular-Connected Random 3D Mobile UAVs with Directional Antennas

This letter proposes an analytical framework to evaluate the coverage performance of a cellular-connected unmanned aerial vehicle (UAV) network in which UAV user equipments (UAV-UEs) are equipped with directional antennas and move according to a three-dimensional (3D) mobility model. The ground base stations (GBSs) equipped with practical down-tilted antennas are distributed according to a Poisson point process (PPP). With tools from stochastic geometry, we derive the handover probability and coverage probability of a random UAV-UE under the strongest average received signal strength (RSS) association strategy. The proposed analytical framework allows to investigate the effect of UAV-UE antenna beamwidth, mobility speed, cell association, and vertical motions on both the handover probability and coverage probability. We conclude that the optimal UAV-UE antenna beamwidth decreases with the GBS density, and the omnidirectional antenna model is preferred in the sparse network scenario. What's more, the superiority of the strongest average RSS association over the nearest association diminishes with the increment of GBS density.Comment: 5 pages, 5 figures, submitted to IEEE Wireless Communications Letter

On Safeguarding Privacy and Security in the Framework of Federated Learning

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding data leakage from the UEs, thereby preserving privacy and security to some extend. However, even if raw data are not disclosed from UEs, individual's private information can still be extracted by some recently discovered attacks in the FL architecture. In this work, we analyze the privacy and security issues in FL, and raise several challenges on preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to illustrate the discussed issues and possible solutions.Comment: This paper has been accepted by IEEE Network Magazin

Aptamer-conjugated, fluorescent gold nanorods as potential cancer theradiagnostic agents

Funding for this project was provided by the ERC grant StG242991.GNRs are emerging as a new class of probes for theradiagnostic applications thanks to their unique optical properties. However, the achievement of proper nanoconstructs requires the synthesis of highly pure GNRs with well-defined aspect ratio (AR), in addition to extensive surface chemistry modification to provide them with active targeting and, possibly, multifunctionality. In this work, we refined the method of the seed mediated growth and developed a robust procedure for the fabrication of GNRs with specific AR. We also revealed and characterized unexplored aging phenomena that follow the synthesis and consistently alter GNRs' final AR. Such advances appreciably improved the feasibility of GNRs fabrication and offered useful insights on the growth mechanism. We next produced fluorescent, biocompatible, aptamer-conjugated GNRs by performing ligand exchange followed by bioconjugation to anti-cancer oligonucleotide AS1411. In vitro studies showed that our nanoconstructs selectively target cancer cells while showing negligible cytotoxicity. As a result, our aptamer-conjugated GNRs constitute ideal cancer-selective multifunctional probes and promising candidates as photothermal therapy agents.Publisher PDFPeer reviewe

Ionic high-pressure form of elemental boron

Boron is an element of fascinating chemical complexity. Controversies have shrouded this element since its discovery was announced in 1808: the new 'element' turned out to be a compound containing less than 60-70 percent of boron, and it was not until 1909 that 99-percent pure boron was obtained. And although we now know of at least 16 polymorphs, the stable phase of boron is not yet experimentally established even at ambient conditions. Boron's complexities arise from frustration: situated between metals and insulators in the periodic table, boron has only three valence electrons, which would favour metallicity, but they are sufficiently localized that insulating states emerge. However, this subtle balance between metallic and insulating states is easily shifted by pressure, temperature and impurities. Here we report the results of high-pressure experiments and ab initio evolutionary crystal structure predictions that explore the structural stability of boron under pressure and, strikingly, reveal a partially ionic high-pressure boron phase. This new phase is stable between 19 and 89 GPa, can be quenched to ambient conditions, and has a hitherto unknown structure (space group Pnnm, 28 atoms in the unit cell) consisting of icosahedral B12 clusters and B2 pairs in a NaCl-type arrangement. We find that the ionicity of the phase affects its electronic bandgap, infrared adsorption and dielectric constants, and that it arises from the different electronic properties of the B2 pairs and B12 clusters and the resultant charge transfer between them.Comment: Published in Nature 453, 863-867 (2009

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises. Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i.e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$-random scheduling strategy, where $K$ ($1<K<N$) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop the corresponding convergence bound of the loss function in this case and the $K$-random scheduling strategy can also retain the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the designs on various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels

Kids Into Health Careers: A Rural Initiative

Abstract Purpose: To describe a project that introduces middle school and high school students living in Pennsylvania’s rural geographic regions to nursing careers through outreach extended to students regardless of gender, ethnicity, or socioeconomic status. Method: The authors employed many strategies to inform students about careers in nursing. The methods included: working with guidance counselors, participating in community health fairs, taking part in school health career fairs, collaborating with Area Health Education Centers, serving on volunteer local education advisory boards, developing a health careers resource guide, and establishing a rural health advisory board. Findings: Developing developmentally appropriate programs may have the potential to pique interest in nursing careers in children of all ages, preschool through high school. Publicity is needed to alert the community of kids into health care career programs. Timing is essential when planning visits to discuss health care professions opportunities with middle and high school students. It is important to increase the number of high school student contacts during the fall months. Targeting high school seniors is particularly important as they begin the college applications process and determine which school will best meet their educational goals. Conclusions: Outcome measures to determine the success of health career programs for students in preschool through high school are needed. Evaluation methods will be continued over the coming years to assess effectiveness

Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: "Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?". Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.Comment: 13 pages, 12 figure

Breastfeeding Education Support Tool for Baby (BEST4Baby): Feasibility, Acceptability, and Preliminary Impact of an mHealth Supported Breastfeeding Peer Counselor Intervention in rural India

Objective: To evaluate the feasibility of an mHealth-supported breastfeeding peer counselor intervention implemented in rural India and the preliminary impact of the intervention on maternal breastfeeding behaviors, including exclusive breastfeeding (EBF). Methods: In this quasi-experimental pilot study, participants received either the intervention plus usual care (n = 110) or usual care alone (n = 112). The intervention group received nine in-home visits during and after pregnancy from peer counselors who provided education about and support for EBF and other optimal infant feeding practices and were aided with an mHealth tool. The control group received routine prenatal and postnatal health education. Progress notes and surveys were used to assess feasibility. Logistic regression models were used for between-group comparisons of optimal infant feeding outcomes, including EBF for 6 months. Results: The intervention was delivered as intended, maintained over the study period, and had high acceptability ratings. There were statistically significant differences in all outcomes between groups. The intervention group had a significantly higher likelihood of EBF at 6 months compared to the control group (adjusted odds ratio 3.57, 95% confidence interval 1.80–7.07). Conclusion: Integration of mHealth with community-based peer counselors to educate women about EBF is feasible and acceptable in rural India and impacts maternal breastfeeding behaviors