System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems

This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency (RF) chain to transmit antenna connections can be switched off for energy saving. By explicitly considering the effect of each connection on the required power for baseband and RF signal processing, we describe the total power consumption in a sparsity form of the analog precoding matrix. However, these sparsity terms and sparsity-modulus constraints of the analog precoding make the system energy-efficiency maximization problem non-convex and challenging to solve. To tackle this problem, we first transform it into a subtractive-form weighted sum rate and power problem. A compressed sensing-based re-weighted quadratic-form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints. We then exploit alternating minimization of the mean-squared error to solve the equivalent problem where the digital precoding vectors and the analog precoding matrix are updated sequentially. The energy efficiency upper bound and a heuristic algorithm are also examined for comparison purposes. Numerical results confirm the superior performances of the proposed algorithm over benchmark energy-efficiency hybrid precoding algorithms and heuristic ones.Comment: submitted to TGC

n-Gram-based text compression

We propose an efficient method for compressing Vietnamese text using n-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it into n-grams and then encodes them based on n-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Each n-gram is encoded by two to four bytes accordingly based on its corresponding n-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to build n-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.Web of Scienceart. no. 948364

Clinical Epidemiological Characteristics and Risk Factors for Severity of SARS-CoV-2 Pneumonia in Pediatric Patients: A Hospital-Based Study in Vietnam

Introduction Coronavirus disease (COVID-19) is an infectious disease caused by SARS-CoV-2, which can cause organ failure in several organs, cardiac problems, or acute respiratory distress syndrome (ARDS). Identifying clinical epidemiological characteristics and risk factors for complications of COVID-19 allows clinicians to diagnose and treat promptly. Objectives This study aims to describe the clinical epidemiological characteristics of COVID-19 and assess risk factors for the severity of SARS-CoV-2 pneumonia in children treated at Haiphong Children\u27s Hospital. Methods A descriptive cross-sectional study was conducted in Haiphong Children\u27s Hospital, Haiphong, Vietnam, for one year, from January 1, 2022, to December 31, 2022. Results In our study, 540 children were evaluated; the male-to-female ratio was 1.48/1; the median age was 23 months (IQR=6-74); Children aged under one year accounted for the highest proportion (n=202; 37.4%); 40 (7.4%) children had underlying illnesses. The number of admitted patients diagnosed with COVID-19 peaked in February 2022. Regarding severity, 380 (70.4%) cases were mild, 136 (25.2%) were moderate, only 24 (4.4%) cases were severe, and no children died. Common symptoms were fever in 483 (89.4%), coughing in 399 (73.9%), and tachypnea in 163 (30.2%) children. Laboratory features: white blood cell count, platelet count, serum CRP, and coagulation test showed little change. Around 116 (21.5%) had lymphopenia and 148 (27.4%) had pneumonia. Patients under one year were approximately 1.64 times more likely to experience pneumonia complications from COVID-19 than those without such a history (OR=1.64, 95%CI = 1.12 - 2.41, p=0.0112). Patients with underlying conditions were approximately 2.08 times more likely to experience pneumonia complications from COVID-19 compared to those without such conditions (OR=2.08, 95%CI =1.08 - 4.02, p=0.0289). Conclusion In COVID-19 pediatric patients, the severity of the disease was mild to moderate without any mortality. Children aged under one year accounted for the highest proportion of all COVID-19 patients. This study found that age under one year and underlying illnesses are related to pneumonia in COVID-19 pediatric patients

Differential diagnosis of dna viruses related to reproductive disorder on sows by multiplex-pcr technique

The newly emerged diseases caused by ASFV and PCV3 and their confirmed prevelance in Vietnam whereas most of available common commercial methods such as ELISA or realtime PCR designed for detecting single pathogen per reaction, highlighted a necessity for another diagnostic method to simultaneously detect and differentiate DNA viruses that are related to reproductive failures in sow herds including PCV2, PCV3, PPV, ASFV. In this communication, a diagnostic multiplex-PCR (mPCR) was established with pathogen-specific primers selected from previous studies and another set of primers designed for COX1 gene serving as an internal amplification control (IAC). The predicted products of PCV2, PCV3, PPV, ASFV and IAC were 702 bp, 223 bp, 380 bp, 278 bp and 463 bp, respectively. After optimization, the mPCR functioned specifically at 62°C. Results revealed the consistent detection limit at 100 copies/gene/reaction. In application, 185 serum samples from sows were used to examine the presence of the related pathogens. mPCR results showed that the mono-infection rate of PCV2, PCV3, PPV, and ASFV was 0% (0/185), 40% (74/185), 28.1% (52/185), and 48.1% (89/185), respectively. Regarding coinfection rate, the data indicated that coinfections of 2, 3 and 4 pathogens were 20%, 8.1% and 0% accordingly. In conclusion, the mPCR assay was successfully established and ready to serve for diagnosis of PCV2, PCV3, PPV and ASFV infection in reality with high specificity and sensitivity. It is a good contribution to a better understanding of the epidemiology of these diseases in swine

Multi-Agent Reinforcement Learning for Joint Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-infrastructure (V2I) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of fast channel variant in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, who acts as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the V2I sum-rate and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm achieves a close performance compared to that of the well-known exhaustive search algorithm.Comment: 6 pages, 4 figure

Energy-Efficient Design for Downlink Cloud Radio Access Networks

This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.This work is supported in part by an ECR-HDR scholarship from The University of Newcastle, in part by the Australian Research Council Discovery Project grants DP170100939 and DP160101537, in part by Vietnam National Foundation for Science and Technology Development under grant number 101.02-2016.11 and in part by a startup fund from San Diego State University

User Selection Approaches to Mitigate the Straggler Effect for Federated Learning on Cell-Free Massive MIMO Networks

This work proposes UE selection approaches to mitigate the straggler effect for federated learning (FL) on cell-free massive multiple-input multiple-output networks. To show how these approaches work, we consider a general FL framework with UE sampling, and aim to minimize the FL training time in this framework. Here, training updates are (S1) broadcast to all the selected UEs from a central server, (S2) computed at the UEs sampled from the selected UE set, and (S3) sent back to the central server. The first approach mitigates the straggler effect in both Steps (S1) and (S3), while the second approach only Step (S3). Two optimization problems are then formulated to jointly optimize UE selection, transmit power and data rate. These mixed-integer mixed-timescale stochastic nonconvex problems capture the complex interactions among the training time, the straggler effect, and UE selection. By employing the online successive convex approximation approach, we develop a novel algorithm to solve the formulated problems with proven convergence to the neighbourhood of their stationary points. Numerical results confirm that our UE selection designs significantly reduce the training time over baseline approaches, especially in the networks that experience serious straggler effects due to the moderately low density of access points.Comment: submitted for peer review

Synthesis, Biological Evaluation, and Molecular Modeling Studies of 1-Aryl-1H-pyrazole-Fused Curcumin Analogues as Anticancer Agents

Addressing the growing burden of cancer and the shortcomings of chemotherapy in cancer treatment are the current research goals. Research to overcome the limitations of curcumin and to improve its anticancer activity via its heterocycle-fused monocarbonyl analogues (MACs) has immense potential. In this study, 32 asymmetric MACs fused with 1-aryl-