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The Metaverse relies on advanced machine learning (ML) techniques to facilitate the seamless mapping between the virtual and physical realms. ML-based technologies also enable metaverse service providers (MSPs) to offer a diverse range of intelligent virtual services to metaverse users (MUs). However, it can be challenging for MSPs to collect sufficient metaverse data to train ML models by themselves. As a result, MSPs can be interested in seeking contributions from MUs in both ML data and models. To address these challenges, we propose MetaAICM, a blockchain-based framework that empowers the metaverse through two key components. Firstly, it incorporates a distributed crowdsourcing system that allows MSPs to gather metaverse data and ML models from MUs. Secondly, it features a decentralized marketplace, enabling MUs to proactively collect data and train ML models for sale using their metaverse devices and computing resources. MetaAICM leverages blockchain and smart contracts to achieve decentralization, ensuring security and privacy without relying on a trusted third-party authority or additional trust assumptions between MUs and MSPs. Numerical studies show that MetaAICM offers high processing performance and cost efficiency, while the framework is implemented on top of a consortium blockchain to show its feasibility.

To achieve an adequate tradeoff between range and energy efficiency, LoRaWAN End Nodes (ENs) choose their transmission parameters using an Adaptive Data Rate (ADR) scheme based on the maximum value of previous Signal-to-Noise (SNR) values. However, the ADR only performs well in favorable channel conditions. In fact, if the SNR exhibits high variability, these parameters could be inefficiently set and may negatively affect the Packet Delivery Rate (PDR). Therefore, a link margin could be overestimated to improve the PDR by the ADR algorithm, which may, however, waste the EN’s energy. This paper proposes a novel ADR that does not rely on the past SNR values. Still, our proposed design directly predicts the current SNR and transmission parameters using Machine Learning. Specifically, the underlying Machine Learning models were trained using in-field measurements for six months in Medellín, Colombia, including different environmental variables and their effects on the SNR. Our ADR scheme improved energy consumption by 47.1% with a PDR of 99% and reduced collisions in dense networks up to 9.5% compared with the ADR scheme. Furthermore, we show that our proposed design outperforms some enhanced versions of the ADR scheme proposed in the literature in both energy consumption and collision rate. Finally, our proposed framework enables simple implementation since it runs directly in the ENs, improving the response time compared with the traditional ADR scheme.

The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs.

Abstract The impact of cancer interventions has been conducted in several research due to the significant burden of this non-communicable disease. The interventions that played an important role in the improvement of the patient’s quality of life (QoL) and health-related quality of life (HRQL) can be classified into two main groups: pharmaceutical and non-pharmacological methods. However, studies so far often analyze a specific group of interventions for specific types of cancer. Thus, in this systematic review and meta-analysis, we synthesized the overall impact of cancer interventions on patients’ quality of life in several cancers. In this research, we followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) to search the longitudinal original research on the Web of Science (WOS) database. After that, the Newcastle-Ottawa Scale (NOS) and Jadad Scale were used to assess the quality of non-randomized control trials and randomized control trials, respectively. Then, the characteristics of the included studies were described in the six main fields table and the random effect model with robust estimation was applied to analyze the impact of interventions on the health utility of patients. From the database, 122 longitudinal original research were included in the meta-regression, with most of them having high or fair quality. The European Organization for the research and treatment of cancer scale for quality of life (EORTC-QLQ) was the most used health utility measurement at 65.15%. In the adjusted effect models, the Visual Analogue Scale (VAS) had significant statistics in all models when we compared it with the EQ-5D Scale (p

Abstract Background Vietnam and Saudi Arabia have high disease burden of primary hepatocellular carcinoma (HCC). Early detection in asymptomatic patients at risk for HCC is a strategy to improve survival outcomes in HCC management. GALAD score, a serum-based panel, has demonstrated promising clinical utility in HCC management. However, in order to ascertain its potential role in the surveillance of the early detection of HCC, GALAD needs to be validated prospectively for clinical surveillance of HCC (i.e., phase IV biomarker validation study). Thus, we propose to conduct a phase IV biomarker validation study to prospectively survey a cohort of patients with advanced fibrosis or compensated cirrhosis, irrespective of etiologies, using semi-annual abdominal ultrasound and GALAD score for five years. Methods We plan to recruit a cohort of 1,600 patients, male or female, with advanced fibrosis or cirrhosis (i.e., F3 or F4) and MELD ≤ 15, in Vietnam and Saudi Arabia (n = 800 each). Individuals with a liver mass ≥ 1 cm in diameter, elevated alpha-fetoprotein (AFP) (≥ 9 ng/mL), and/or elevated GALAD score (≥ -0.63) will be scanned with dynamic contrast-enhanced magnetic resonance imaging (MRI), and a diagnosis of HCC will be made by Liver Imaging Reporting and Data System (LiRADS) assessment (LiRADS-5). Additionally, those who do not exhibit abnormal imaging findings, elevated AFP titer, and/or elevated GALAD score will obtain a dynamic contrast-enhanced MRI annually for five years to assess for HCC. Only MRI nearest to the time of GALAD score measurement, ultrasound and/or AFP evaluation will be included in the diagnostic validation analysis. MRI will be replaced with an abdominal computed tomography scan when MRI results are poor due to patient conditions such as movement etc. Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced MRI will not be carried out in study sites in both countries. Bootstrap resampling technique will be used to account for repeated measures to estimate standard errors and confidence intervals. Additionally, we will use the Cox proportional hazards regression model with covariates tailored to the hypothesis under investigation for time-to-HCC data as predicted by time-varying biomarker data. Discussion The present work will evaluate the performance of GALAD score in early detection of liver cancer. Furthermore, by leveraging the prospective cohort, we will establish a biorepository of longitudinally collected biospecimens from patients with advanced fibrosis or cirrhosis to be used as a reference set for future research in early detection of HCC in the two countries. Trial registration Name of the registry: ClinicalTrials.gov Registration date: 22 April 2022 Trial registration number: NCT05342350 URL of trial registry record

Abstract Using mango purée from overripe mangoes to produce powders helped to solve agricultural product stagnation. The research investigates the effect of thickening additives, convection drying, and heat pump drying on bioactive compounds such as total phenolic content (TPC), total flavonoid content (TFC), color, and solubility of the final product. The obtained results showed that the mixture (gum arabic and maltodextrin in the ratio 50:50 w/w) at a concentration of 15% gave a good quality powder texture when dried by hot air convection at 55°C with TPC (21.24 ± 1.58 mg GAE/g dry weight [DW]) and TFC (0.34 ± 0.02 mg QE/g DW), respectively. In addition, the product has a high solubility of 64.35%, with the highest pass‐through point of 17.11.

Background and Aims: Premenstrual syndrome (PMS) and Premenstrual dysphoric disorder (PMDD) are two premenstrual disorders that have been reported in many countries and have gradually become predominant concerns The study aimed to determine potential risk factors associated with PMS and PMDD Method: Three hundred two female student participants who were 18 – 45 years old completed a questionnaire including demographic characteristics, lifestyle factors, and a Vietnamese Premenstrual Syndrome Screening Tool (PSST). We then followed up participants during at least two menstrual cycles using the Daily Record of Severity of Problems (DRSP). The PMS and PMDD diagnosis were established using The Carolina Premenstrual Assessment Scoring System (C-PASS), based on the American College of Obstetrics and Gynecology (ACOG) and Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Results: According to the C-PASS, 35 out of 302 students (11.6%; 95%CI: 8.2-15.7%) met the diagnosis of PMS (31 students) or PMDD (4 students). We found that age at menarche (PR = 0.77, 95%CI: 0.63 - 0.96), having negative Rh blood type (PR=4.43, 95%CI: 1.95 to 10.08), being moderately depressed or higher (PR=2.81, 95%CI: 1.24 to 6.36), and consuming caffeine more than three times per week were statistically associated with having PMS/PMDD after adjusting for other variables. Conclusion: The prominent risk factors for PMS and PMDD were negative Rhesus blood type, menarche age, caffeine consumption, and self-reported depression.

Metaverse is expected to rely on massive Internet of Things (IoT) connections so it inherits various security threats from the IoT network and also faces other sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale distributed metaverse, this paper proposes MetaCIDS, a novel collaborative intrusion detection (CID) framework that leverages metaverse devices to collaboratively protect the metaverse. In MetaCIDS, a federated learning (FL) scheme based on unsupervised autoencoder and an attention-based supervised classifier enables metaverse users to train a CID model using their local network data, while the blockchain network allows metaverse users to train a machine learning (ML) model to detect intrusion network flows over their monitored local network traffic, then submit verifiable intrusion alerts to the blockchain to earn metaverse tokens. Security analysis shows that MetaCIDS can efficiently detect zero-day attacks, while the training process is resistant to SPoF, data tampering, and up to 33% poisoning nodes. Performance evaluation illustrates the efficiency of MetaCIDS with 96% to 99% detection accuracy on four different network intrusion datasets, supporting both multi-class detection using labeled data and anomaly detection trained on unlabeled data.

Platycodon is a medicinal plant of considerable pharmacological and dietary value. With the growing demand, agricultural production is increasing. However, the continuous cropping significantly causes negative impacts on its yield and quality. In this study, in order to solve the problem of continuous cropping, we evaluated the allelopathic activity of Platycodon and investigated the potential use of activated carbon for mitigating the negative impacts of allelopathic chemicals produced by Platycodon. The sandwich method (method for assaying the allelopathic activity of each part of a plant) was employed to evaluate the allelopathic activity of different parts (leaves, stems, and roots) of Platycodon. The inhibitory effects of various Platycodon plant parts were assessed based on their effects on lettuce seedling growth. At a concentration of 10 mg parts/10 mL agar, the average inhibition rates of Platycodon leaves on the radicle and hypocotyl growth of lettuce were 79.4% and 61.8%, stems 58.0% and 45.7%, and roots 53.4% and 49.3%, respectively. At a concentration of 50 mg parts/10 mL agar, the inhibitory effects were as follows: leaves (91.9%, 72.2%), stems (79.5%, 60.3%), and roots (71.4%, 65.2%). The effect of activated carbon on the adsorption of allelopathic substances was investigated, and the results of the sandwich method with a concentration of 10 mg parts/10 mL agar showed the following growth-inhibitory effects on lettuce seedlings and hypocotyls—roots (27.8%, 25.7%), leaves (13.3%, 25.7%), and stems (9.1%, 13.6%)—in each case showing a significant decrease in the inhibitory activity. The plant box method (method for assaying the allelopathic activity of plant root exudates) was employed to evaluate the activity of Platycodon root exudate. The growth inhibition rates of lettuce radicle and hypocotyls were 45.5% and 18.9%, respectively. The plant box method with addition of activated carbon revealed average rates of promotion of 16.7% and 4.7% on the growth of lettuce seedlings and hypocotyls, respectively. The results of this study demonstrated that activated carbon has a mitigating effect on allelopathic inhibition associated with the different plant parts and root exudation of Platycodon and provide a potential solution for overcoming problems associated with the continuous cropping of Platycodon.

Aims To determine the effectiveness of a mobile application (app) in improving the quality of bowel preparation for colonoscopy.Method An endoscopist-blinded randomised controlled trial enrolled patients who were undergoing a colonoscopy on the same day of bowel preparation. The intervention used a Vietnamese mobile app that provides instructions on bowel preparation while patients in the comparison group received conventional instructions. Outcomes included the Boston Bowel Preparation Scale (BBPS) to assess the quality of bowel preparation and the polyp detection rate (PDR) and adenoma detection rate (ADR).Results The study recruited 515 patients (256 in the intervention group). The median age was 42 years, 50.9% were females, 69.1% high school graduates and higher, and 45.2% from urban area. Patients in the intervention group had higher adherence to instructions (60.9% vs 52.4%, p=0.05) and longer length of taking laxatives (mean difference 0.17 hours, 95% CI 0.06 to 0.27). The intervention did not reduce the risk of poor bowel cleansing (total BBPS

Tomatoes are widely grown vegetables, and farmers face challenges in caring for them, particularly regarding plant diseases. The MobileNet architecture is renowned for its simplicity and compatibility with mobile devices. This study introduces MobileNet as a deep learning model to enhance disease detection efficiency in tomato plants. The model is evaluated on a dataset of 2,064 tomato leaf images, encompassing early blight, leaf spot, yellow curl, and healthy leaves. Results demonstrate promising accuracy, exceeding 0.980 for disease classification and 0.975 for distinguishing between diseases and healthy cases. Moreover, the proposed model outperforms existing approaches in terms of accuracy and training time for plant leaf disease detection.

Abstract Tobacco consumption, as a worldwide problem, is a risk factor for several types of cancer. In Vietnam, tobacco consumption in the form of waterpipe tobacco smoking is common. This prospective cohort study aimed to study the association between waterpipe tobacco smoking and gastric cancer mortality in Northern Vietnam. A total of 25,619 eligible participants were followed up between 2008 and 2019. Waterpipe tobacco and cigarette smoking data were collected; semi-quantitative food frequency and lifestyle questionnaires were also utilized. Gastric cancer mortality was determined via medical records available at the state health facilities. A Cox proportional hazards model was used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI). During 314,992.8 person-years of follow-up, 55 men and 25 women deaths due to gastric cancer were identified. With never-smokers as the reference, the risk of gastric cancer mortality was significantly increased in participants who were ever-smoking (HR = 2.43, 95% CI = 1.35–4.36). The positive risk was also observed in men but was not significantly increased in women. By types of tobacco use, exclusive waterpipe smokers showed a significantly increased risk of gastric cancer mortality (HR = 3.22, 95% CI = 1.67–6.21) but that was not significantly increased in exclusive cigarette smokers (HR = 1.90, 95% CI = 0.88–4.07). There was a significant positive association between tobacco smoking and gastric cancer death for indicators of longer smoking duration, higher frequency per day, and cumulative frequency of both waterpipe and cigarette smoking. Waterpipe tobacco smoking would significantly increase the risk of gastric cancer mortality in the Vietnamese population. Further studies are required to understand the waterpipe tobacco smoking-driven gastric cancer burden and promote necessary interventions.

ObjectivesTo evaluate the practice of TB care among physicians at private facilities.MethodsA cross-sectional study was conducted using questionnaires on knowledge, attitude, and practice related to TB care. The responses to these scales were used to explore latent constructs and calculate the standardized continuous scores for these domains. We described the percentages of participant's responses and explored their associated factors using multiple linear regression.ResultsA total of 232 physicians were recruited. The most important gaps in practice included requesting chest imaging to confirm TB diagnosis (~80%), not testing HIV for confirmed active TB cases (~50%), only requesting sputum testing for MDR-TB cases (65%), only requesting follow-up examination at the end of the treatment course (64%), and not requesting sputum testing at follow-up (54%). Surgical mask was preferred to N95 respirator when examining TB patients. Prior TB training was associated with better knowledge and less stigmatizing attitude, which were associated with better practice in both TB management and precautions.ConclusionThere were important gaps in knowledge, attitude, and practice of TB care among private providers. Better knowledge was associated with positive attitude towards TB and better practice. Tailored training may help to address these gaps and improve the quality of TB care in the private sector.

The gray abalone mushroom is a rich source of amino acids and carbohydrate compounds, with some biological activities and antioxidants. Nowadays, the variety of food additives on the market such as sodium glutamate (E621), sodium guanylate (E627), or sodium insonate (E631), could probably cause negative effects for people’s health. This study used gray abalone mushrooms to create naturally derived seasoning products without using flavor enhancers and synthetic compounds. The gray abalone mushroom was pretreated and dried at 60°C until attaining the moisture content of

AimThe study aimed to determine potential risk factors associated with Premenstrual Syndrome and Premenstrual Dysphoric Disorder.MethodsThree hundred two female student participants who were 18-45 years old completed a questionnaire including demographic characteristics, lifestyle factors, and a Vietnamese Premenstrual Syndrome Screening Tool. We then followed up participants during at least two menstrual cycles using the Daily Record of Severity of Problems. The Premenstrual Syndrome and Premenstrual Dysphoric Disorder diagnosis was established using The Carolina Premenstrual Assessment Scoring System, based on the American College of Obstetrics and Gynecology and Diagnostic and Statistical Manual of Mental Disorders.ResultsAccording to the Carolina Premenstrual Assessment Scoring System, 35 out of 302 students (11.6%; 95%CI: 8.2-15.7%) met the diagnosis of PMS (31 students) or PMDD (4 students). We found that age at menarche (PR = 0.77, 95%CI: 0.63-0.96), having negative Rh blood type (PR = 4.43, 95%CI: 1.95 to 10.08), being moderately depressed or higher (PR = 2.81, 95%CI: 1.24 to 6.36), and consuming caffeine more than three times per week were statistically associated with having Premenstrual Syndrome or Premenstrual Dysphoric Disorder after adjusting for other variables.ConclusionThe prominent risk factors for Premenstrual Syndrome and Premenstrual Dysphoric Disorder were negative Rhesus blood type, menarche age, caffeine consumption, and self-reported depression.

Abstract Objective To compare the predictive values of base excess (BE), lactate and pH of admission arterial blood gas for 72-h mortality in patients with multiple trauma. Methods This was a secondary analysis based on a publicly shared trauma dataset from the Dryad database, which provided the clinical data of 3669 multiple trauma patients with ISS > = 16. The records of BE, lactate, pH and 72-h prognosis data without missing values were selected from this dataset and 2441 individuals were enrolled in the study. Logistic regression model was performed to calculate the odds ratios (ORs) of variables. Area under the curve (AUC) of receiver operating curve (ROC) was utilized to evaluate the predictive value of predictors for 72 h in-hospital mortality. Pairwise comparison of AUCs was performed using the Delong’s test. Results The statistically significant correlations were observed between BE and lactate (r = − 0.5861, p

Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellín, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R2 of 0.917.

ObjectivesThe coronavirus disease 2019 (COVID-19) pandemic has disrupted the practice of gastrointestinal (GI) endoscopy units and may increase the risk of digestive disorders. We described the situational changes in GI endoscopy and peptic ulcer disease (PUD) proportion during COVID-19 in Vietnam and examined the associated factors.MethodsA retrospective ecological study was conducted on data of Hanoi Medical University Hospital, Vietnam. The number of upper GI endoscopy and the proportion of GI emergency endoscopy and PUD were compared between 2019 and 2020 by month (January to June). Log-binomial regression was used to explore associated factors of GI emergency endoscopy and PUD.ResultsThe number of endoscopies decreased remarkably during the nationwide social distancing in April 2020. Compared to April 2019, the proportion in April 2020 of both GI emergency endoscopy [4.1 vs. 9.8%, proportion ratio (PR) 2.39, 95% CI 2, 2.87], and PUD [13.9 vs. 15.8%; PR, 1.14; 95% CI, 1.01, 1.29] was significantly higher. In log-binomial models, the proportion of GI emergency endoscopy was higher in April 2020 compared to April 2019 (adjusted PR, 2.41; 95% CI, 2.01, 2.88). Male sex and age of ≥50 years were associated with an increased PUD and GI emergency conditions.ConclusionThe proportion of both GI emergency endoscopy and PUD was significantly higher during the time of the state of emergency due to the ongoing COVID-19 pandemic in 2020 when compared to 2019 at the same health facility in Vietnam. The findings suggest that healthcare delivery reforms during the era of an emerging pandemic are required to reduce digestive disorders, in particular, and chronic diseases in general.

In this paper, we propose the joint interference cancellation, fast fading channel estimation, and data symbol detection for a general interference setting where the interfering source and the interfered receiver are unsynchronized and occupy overlapping channels of different bandwidths. The interference must be canceled before the channel estimation and data symbol detection of the desired communication are performed. To this end, we have to estimate the Effective Interference Coefficients (EICs) and then the desired fast fading channel coefficients. We construct a two-phase framework where the EICs and desired channel coefficients are estimated using the joint maximum likelihood-maximum a posteriori probability (JML-MAP) criteria in the first phase; and the MAP based data symbol detection is performed in the second phase. Based on this two-phase framework, we also propose an iterative algorithm for interference cancellation, channel estimation and data detection. We analyze the channel estimation error, residual interference, symbol error rate (SER) achieved by the proposed framework. We then discuss how to optimize the pilot density to achieve the maximum throughput. Via numerical studies, we show that our design can effectively mitigate the interference for a wide range of SNR values, our proposed channel estimation and symbol detection design can achieve better performances compared to the existing method. Moreover, we demonstrate the improved performance of the iterative algorithm with respect to the non-iterative counterpart.

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

Considered as a key technology in 5G networks, mobile edge computing (MEC) can support intensive computation for energy-constrained and computation-limited mobile users (MUs) through offloading various computation and service functions to the edge of mobile networks. In addition to MEC, wireless heterogeneous networks will play an important role in providing high transmission capacity for MUs in 5G, where wireless backhaul is a cost-effective and viable solution to solve the expensive backhaul deployment issue. In this paper, we consider a setting, where MUs can offload their computations to the MEC server through a small cell base station (SBS), the SBS connects to the macro BS through a wireless backhaul, and computation resource at the MEC server is shared among offloading MUs. First, we formulate a joint optimization problem with the goal of minimizing the system-wide computation overhead. This is a mixed-integer problem and hard to derive the optimal solution. To solve this problem, we propose to decompose it into two subproblems, namely the offloading decision subproblem and the joint backhaul bandwidth and computation resource allocation subproblem. An algorithm, namely JOBCA, is proposed to obtain a feasible solution to the original problem by solving two subproblems iteratively. Finally, numerical results are conducted to verify the performance improvement of the proposed algorithm over two baseline algorithms and the close performance of the proposed algorithm compared with the centralized exhaustive search.

ObjectiveFamilial transmission can possibly influence the infection and treatment of Helicobacter pylori. This study aimed to describe the prevalence of H. pylori infection and outcomes of eradication treatment among Vietnamese patients who live in the same households.MethodsWe conducted a prospective cohort study of Vietnamese household members with upper gastrointestinal complaints. Participants received esophagogastroduodenoscopy and H. pylori testing. The H. pylori-positive patients were treated and asked to return for follow-up within 4 months. To explore factors associated with H. pylori infection at baseline, we performed multilevel logistic regression to account for the clustering effect of living in the same households. To explore factors associated with eradication failure, we used Poisson regression with robust variance estimation to estimate the risk ratio.ResultsThe prevalence of H. pylori infection was 83.5% and highest among children ConclusionH. pylori infection was common among people living in the same households. Eradication success for H. pylori was higher for the tetracycline sequential regimen. More research should be focused on how family factors influence H. pylori infection and on eradication treatment.

ObjectiveTo test the following hypothesis: the ratio of shock index to pulse oxygen saturation can better predict the mortality of emergency trauma patients than shock index.Methods1723 Patients of trauma admitted to the Emergency Department of the First Affiliated Hospital of Soochow University from 1 November 2016 to 30 November 2019 were retrospectively evaluated. We defined SS as the ratio of SI to SPO2, and the mortality of trauma patients in the emergency department as end-point of outcome. We calculated the crude HR of SS and adjusted HR with the adjustment for risk factors including sex, age, revised trauma score (RTS) by Cox regression model. ROC curve analyses were performed to compare the area under the curve (AUC) of SS and SI.ResultsThe crude HR of SS was: 4.31, 95%CI (2.89-6.42) and adjusted HR: 3.01, 95%CI(1.86-4.88); ROC curve analyses showed that AUC of SS was higher than that of shock index (SI), and the difference was statistically significant: 0.69, 95%CI(0.55-0.83) vs 0.65, 95%CI (0.51-0.79), P = 0.001.ConclusionThe ratio of shock index to pulse oxygen saturation is good predictor for emergency trauma patients, which has a better prognostic value than shock index.

Efficient content caching plays a crucial role in quality of service enhancement and congestion mitigation of the backhaul and core networks for the fifth-generation (5G) wireless network, which must support a large amount of multimedia and video content. Wireless network virtualization provides a novel paradigm shift in 5G system design which enables to better utilize network resources, rapid development of new services, and reduce the operation cost. Harmonized deployment of a content caching strategy in the virtualized wireless network environment, however, requires a suitable radio resource allocation framework to realize the great benefits of these technologies. In this paper, we study the joint resource allocation and content caching problem which aims to efficiently utilize the radio and content storage resources in the highly congested backhaul scenario. In this design, we minimize the maximum content request rejection rate experienced by users of different mobile virtual network operators in different cells, which results in a mixed-integer non-linear program. We solve this difficult optimization problem by proposing a bisection-search-based algorithm that iteratively optimizes the resource allocation and content caching placement. We further propose a low-complexity heuristic algorithm which achieves moderate performance loss compared to the bisection-search based algorithm. Extensive numerical results confirm the efficacy of our proposed framework which significantly reduces the maximum request outage probability compared with other benchmark algorithms.

Existing detectors are based on using the similarity of pixels or blocks to locate noisy pixels. Alternatively, this paper presents an approach that investigates the similarity property among sequences of pixels to establish a new reference sequence-to-sequence similarity (RSSS) impulse noise detector. Then, to harness the advantages of this new RSSS detector in high detection accuracy, a new unified image denoising algorithm (RSSS-I) is introduced to remove different types of impulse noise. In this approach, the RSSS detector locates the impulse noise, and then, three different median filters remove the detected impulse noise in a cascade framework. In this framework, an existing weighted median filter is utilized as the domain filter, and two new directional mean and “extreme”median filters are applied as the post-filter. Experimental results show the benefits of this cascade framework in improving the performance. Comparison results demonstrate that the RSSS-I outperforms several existing methods for the ability to accurately locate the positions of noise, retain edge information, inhibit residual artifacts from occurring, and generating denoised images with better quality.

Abstract Background Bloodstream infections (BSIs) are associated with high morbidity and mortality worldwide. However their aetiology, antimicrobial susceptibilities and associated outcomes differ between developed and developing countries. Systematic data from Vietnam are scarce. Here we present aetiologic data on BSI in adults admitted to a large tertiary referral hospital for infectious diseases in Hanoi, Vietnam. Methods A retrospective study was conducted at the National Hospital for Tropical Diseases between January 2011 and December 2013. Cases of BSI were determined from records in the microbiology department. Case records were obtained where possible and clinical findings, treatment and outcome were recorded. BSI were classified as community acquired if the blood sample was drawn ≤48 h after hospitalization or hospital acquired if >48 h. Results A total of 738 patients with BSI were included for microbiological analysis. The predominant pathogens were: Klebsiella pneumoniae (17.5%), Escherichia coli (17.3%), Staphylococcus aureus (14.9%), Stenotrophomonas maltophilia (9.6%) and Streptococcus suis (7.6%). The overall proportion of extended spectrum beta-lactamase (ESBL) production among Enterobacteriaceae was 25.1% (67/267 isolates) and of methicillin-resistance in S. aureus (MRSA) 37% (40/108). Clinical data was retrieved for 477 (64.6%) patients; median age was 48 years (IQR 36–60) with 27.7% female. The overall case fatality rate was 28.9% and the highest case fatality was associated with Enterobacteriaceae BSI (34.7%) which accounted for 61.6% of all BSI fatalities. Conclusions Enterobacteriaceae (predominantly K. pneumoniae and E. coli) are the most common cause of both community and hospital acquired bloodstream infections in a tertiary referral clinic in northern Vietnam.

We consider the resource allocation for the virtualized OFDMA uplink cloud radio access network (C-RAN), where multiple wireless operators (OPs) share the C-RAN infrastructure and resources owned by an infrastructure provider (InP). The resource allocation is designed through studying tightly coupled problems at two different levels. The upper-level problem aims at slicing the fronthaul capacity and cloud computing resources for all OPs to maximize the weighted profits of OPs and InP considering practical constraints on the fronthaul capacity and cloud computation resources. Moreover, the lower-level problems maximize individual OPs' sum rates by optimizing users' transmission rates and quantization bit allocation for the compressed I/Q baseband signals. We develop a two-stage algorithmic framework to address this two-level resource allocation design. In the first stage, we transform both upper-level and lowerlevel problems into corresponding problems by relaxing underlying discrete variables to the continuous ones. We show that these relaxed problems are convex and we develop fast algorithms to attain their optimal solutions. In the second stage, we propose two methods to round the optimal solution of the relaxed problems and achieve a final feasible solution for the original problem. Numerical studies confirm that the proposed algorithms outperform two greedy resource allocation algorithms and their achieved sum rates are very close to sum rate upper-bound obtained by solving relaxed problems. Moreover, we study the impacts of different parameters on the system sum rate, performance tradeoffs, and illustrate insights on a potential system operating point and resource provisioning issues.

Hybrid analog/digital precoding architectures are a low-complexity alternative for fully digital precoding in millimeter-wave (mmWave) MIMO wireless systems. This is motivated by the reduction in the number of radio frequency and mixed signal hardware components. Hybrid precoding involves a combination of analog and digital processing that enables both beamforming and spatial multiplexing gains in mmWave systems. This paper develops hybrid analog/digital precoding and combining designs for mmWave multiuser systems, based on the mean-squared error (MSE) criteria. In the first design with the analog combiners being determined at the users, the proposed hybrid minimum MSE (MMSE) precoder is realized by minimizing the sum-MSE of the data streams intended for the users. In the second design, both the hybrid precoder and combiners are jointly designed in an iterative manner to minimize a weighted sum-MSE cost function. By leveraging the sparse structure of mmWave channels, the MMSE precoding/combining design problems are then formulated as sparse reconstruction problems. An orthogonal matching pursuit-based algorithm is then developed to determine the MMSE precoder and combiners. Simulation results show the performance advantages of the proposed precoding/combining designs in various system settings.

We consider the joint WiFi offloading, admission control, and network management for the integrated WiFi and OFDMA-based cellular network. Specifically, we propose a quality-of-service (QoS) and mobility-aware admission control scheme that efficiently offloads macrocell traffic to WiFi and integrates a novel bandwidth borrow-return strategy while guaranteeing QoS requirements for users. These QoS constraints are explicitly modeled considering the throughput performance of carrier sense multiple access with collision avoidance scheme in the IEEE 802.11 WLAN and the fractional frequency reuse in the downlink cellular network. Then, we develop an analytical model to derive the blocking probabilities for calls in different service areas of a cell. Based on this analysis, we study the joint base station switching, power control, and traffic offloading problem for energy minimization under QoS constraints. We consider the time-varying call traffic and develop an algorithm to find the optimal solution of the problem. Numerical results are presented to demonstrate the performance of the proposed cross-layer resource management framework in the integrated network.

This paper investigates the problem of joint multiuser admission control and beamforming optimization for multiple input single-output heterogeneous networks (HetNets). Considered is a HetNet where multiple newly deployed femtocell base stations (FBSs) have the coverage overlapped with that of an existing macrocell base-station (MBS). The design objective is to serve as many femto users (FUEs) as possible at their quality-of-service (QoS) requirements while maintaining the QoS requirements at the macro user (MUE). This paper then proposes three algorithmic schemes to perform multiuser admission control and beamforming optimization based on the levels of coordination between the MBS and FBSs. In Scheme I with full MBS-FBS coordination, a joint optimization framework is presented, and two solution approaches are proposed to determine the admission control and beamforming design for the FUEs in a centralized manner. In Scheme II with limited MBS-FBS coordination, a distributed algorithm that allows each FBS to unilaterally determine its admission control and beamforming strategy is proposed. This algorithm requires a certain coordination from the MBS by setting a limit on the amount of cross-tier intercell interference (ICI) that can be generated by each FBS. The convergence of the proposed distributed algorithm to a fixed point, where the QoS at the MUEs and the admitted FUEs is guaranteed, is then proved. Finally, in Scheme III without any MBS-FBS coordination, a joint multiuser admission control and zero-forcing beamforming design is then proposed. In particular, each FBS distributively admits its own FUEs while suppressing its cross-tier ICI to the MUEs, which effectively maintains the QoS at these MUEs. The simulation results show comparable performances between the distributed algorithms and the centralized ones in terms of the number of FUEs served and the network power usage.

In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network. We explicitly consider the pilot contamination effect during the channel estimation in the SEE maximization problem, which aims to optimize the power allocation, the number of activated antennas, and the pilot assignment. To tackle the SEE maximization problem, we transform it into a subtractive form, which can be solved more efficiently. In particular, we develop an iterative algorithm to solve the transformed problem where optimization of power allocation and number of antennas is performed, and then pilot assignment optimization is conducted sequentially in each iteration. To tackle the first sub-problem, we employ a successive convex approximation (SCA) technique to attain a solvable convex optimization problem. Moreover, we propose a novel iterative low-complexity algorithm based on the Hungarian method to solve the pilot assignment sub-problem. We also describe how the proposed solution approach can be useful to address the sum rate (SR) maximization problem. In addition to the algorithmic developments, we characterize the optimal structure of both SEE and SR maximization problems. The numerical studies are conducted to illustrate the convergence of the proposed algorithms, impacts of different parameters on the SR and SEE, and significant performance gains of the proposed solution compared the conventional design.

In this paper, we propose an adaptive medium access control (MAC) protocol for full-duplex (FD) cognitive radio networks in which FD secondary users (SUs) perform channel contention followed by concurrent spectrum sensing and transmission, and transmission only with maximum power in two different stages (called the FD sensing and transmission stages, respectively) in each contention and access cycle. The proposed FD cognitive MAC (FDC-MAC) protocol does not require synchronization among SUs, and it efficiently utilizes the spectrum and mitigates the self-interference in the FD transceiver. We develop a mathematical model to analyze the throughput performance of the FDC-MAC protocol, where both half-duplex (HD) transmission and FD transmission modes are considered in the transmission stage. Then, we study the FDC-MAC configuration optimization through adaptively controlling the spectrum sensing duration and transmit power level in the FD sensing stage. We prove that there exists optimal sensing time and transmit power to achieve the maximum throughput, and we develop an algorithm to configure the proposed FDC-MAC protocol. Extensive numerical results are presented to illustrate the optimal FDC-MAC configuration and the impacts of protocol parameters and the self-interference cancellation quality on the throughput performance. Moreover, we demonstrate the significant throughput gains of the FDC-MAC protocol with respect to the existing HD MAC and single-stage FD MAC protocols.

To evaluate fever burden as an independent predictor for prognosis of traumatic brain injury (TBI).This retrospective study involved 355 TBI patients with Glasgow Coma Scale (GCS) ≤14, who presented at the emergency department of our hospital between November 2010 and October 2012. At 6 months follow-up, patients were divided into 5 groups based on Glasgow Outcome Scale (GOS) and dichotomized to GOS score (high (4 to 5) vs. low (1 to 3)). The relationship between fever burden and GOS was assessed.Fever burden increased as GOS scores decreased from 5 to 2, except for score 1 of GOS, which corresponded to a significant lower fever burden. Following dichotomization, patients in the high GOS group were younger, and showed less abnormal pupil reactivity (P

BACKGROUND: Serum albumin and prealbumin are both negative acute-phase reactants, and usually at low levels in stress. We aim to determine their predictive values for poor outcome of traumatic brain injury (TBI). METHODS: A total of 326 patients of TBI were enrolled and followed-up by telephone 6 months after discharge. They were divided into a favorable group (GOS: 3 to 5) and an unfavorable group (GOS: 1 to 2). Serum albumin and prealbumin were measured from vein blood within 24 h after admission. RESULTS: Ninety one (27.9%) patients were with poor outcome (GOS: 1 to 2). The unfavorable group had lower albumin and prealbumin (P

The power allocation problem for multiuser wireless networks is considered under the assumption of amplify-and-forward cooperative diversity. Specifically, optimal centralized and distributed power allocation strategies with and without minimum rate requirements are proposed. We make the following contributions. First, power allocation strategies are developed to maximize either (i) the minimum rate among all users or (ii) the weighted-sum of rates. These two strategies achieve different throughput and fairness tradeoffs which can be chosen by network operators depending on their offering services. Second, the distributed implementation of the weighted-sum of rates maximization-based power allocation is proposed. Third, we consider the case when the requesting users have minimum rate requirements, which may not be all satisfied due to the limited-power relays. Consequently, admission control is needed to select the number of users for further optimal power allocation. As such a joint optimal admission control and power allocation problem is combinatorially hard, a heuristic-based suboptimal algorithm with significantly reduced complexity and remarkably good performance is developed. Numerical results demonstrate the effectiveness of the proposed approaches and reveal interesting throughput-fairness tradeoff in resource allocation.