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Residue concentrations of heavy metals, including As, Cd, Cr, Cu, Ni, Pb, and Zn, were determined in bottom ash, fly ash, and particulate matter (PM10) samples collected from five municipal incinerators in northern Vietnam to assess their occurrence, distribution characteristics, and potential risks. Concentrations and profiles of heavy metals are presented, showing the dominance of Zn in all types of samples. Highly volatile elements (Cd, Pb, and Zn) were found at elevated proportions in PM10 but not fly ash. The large difference in the heavy metal profiles could be explained by the variation of input raw materials, the absence of an appropriate cycle for the material feeding process, and post-combustion technology applied. Mass balance of heavy metals in the bottom ash, fly ash, and PM10 varied significantly between the investigated incinerators, largely due to the difference in incineration technology and air pollution control system. Emission factors and annual emissions were also estimated, indicating the highest value and amount in bottom ash, followed by PM10 and fly ash. Our results are among the first studies reporting contents and emissions of toxic elements in incinerated solid wastes in Vietnam.

Nowadays mobile activity recognition (AR) has been creating great potentials in many applications including mobile healthcare and context-aware systems. Human activities could be detected based on sensory data that are available on today’s smart phone. In this study, we consider mobile phones as an independent device since sending the data to central server can generate privacy issues. Furthermore, applying AR on mobile phone does not only require an effective accuracy rate but also the lowest power consumption. Normally, an AR model learnt from acceleration data of a specific person is distributed to other people to recognize the same activities instead of generating different models individually. This work often cannot create accurate results on the prediction in broad range of participants. Moreover, such AR model also has to allow each user to update his new activities independently. Therefore, we propose an algorithm that integrates Support Vector Machine classifier and K-medoids clustering method to resolve completely the demand.

Smart environments established by the development of mobile technology have brought vast benefits to human being. However, authentication mechanisms on portable smart devices, particularly conventional biometric based approaches, still remain security and privacy concerns. These traditional systems are mostly based on pattern recognition and machine learning algorithms, wherein original biometric templates or extracted features are stored under unconcealed form for performing matching with a new biometric sample in the authentication phase. In this paper, we propose a novel gait based authentication using biometric cryptosystem to enhance the system security and user privacy on the smart phone. Extracted gait features are merely used to biometrically encrypt a cryptographic key which is acted as the authentication factor. Gait signals are acquired by using an inertial sensor named accelerometer in the mobile device and error correcting codes are adopted to deal with the natural variation of gait measurements. We evaluate our proposed system on a dataset consisting of gait samples of 34 volunteers. We achieved the lowest false acceptance rate (FAR) and false rejection rate (FRR) of 3.92% and 11.76%, respectively, in terms of key length of 50 bits.

Nowadays mobile activity recognition (AR) has been creating great potentials in many applications including mobile healthcare and context-aware systems. Human activities could be detected based on sensory data that are available on today's smart phone. In this study, we consider mobile phones as an independent device since sending the data to central server can generate privacy issues. Furthermore, applying AR on mobile phone does not only require an effective accuracy rate but also the lowest power consumption. Normally, an AR model learnt from acceleration data of a specific person is distributed to other people to recognize the same activities instead of generating different models individually. This work often cannot create accurate results on the prediction in broad range of participants. Moreover, such AR model also has to allow each user to update his new activities independently. Therefore, we propose an algorithm that integrates Support Vector Machine classifier and K -medoids clustering method to resolve completely the demand.

Fall injury is one of the biggest risks to health and well-being of the elderly especially in independent living because falling accidents may cause instant death. There are many research interests aimed to detect fall incidents. Fall detection is envisioned critical on ICT-assisted healthcare future. In addition, mobile battery is currently another serious problem in which performance feasibility is considered as a standard to verify an effective method. In this paper, the authors study forward fall detection method from mobile phone perspective using accelerometer only without sacrificing accuracy to save energy. Using peak threshold algorithm in axes of mobile accelerometer, transition from activity of daily living (ADL) to forward fall event is recognized. In collected templates, Dynamic Time Warping (DTW) was applied to compute difference among them with new unlabeled samples. Results implemented on mobile phone easily show the feasibility of the method hence contribute significantly to fall detection in healthcare.

Genetic factors play an important role in the development of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS). However, few genetic association studies related to these disorders have been performed with Vietnamese subjects. In this study, the potential associations of single nucleotide polymorphisms (SNPs) with T2DM and MetS in a Kinh Vietnamese population were investigated. A study with 768 subjects was conducted to examine the associations of four SNPs (rs266729, rs1501299, rs3774261, and rs822393) primarily with T2DM and secondarily with MetS. The TaqMan SNP genotyping assay was used to determine genotypes from subjects' DNA samples. After statistical adjustment for age, sex, and body mass index, the SNP rs266729 was found to be associated with increased risk of T2DM under multiple inheritance models: codominant (OR = 2.30, 95% CI = 1.16-4.58), recessive (OR = 2.17, 95% CI = 1.11-4.26), and log-additive (OR = 1.32, 95% CI = 1.02-1.70). However, rs1501299, rs3774261, and rs822393 were not associated with risk for T2DM. Additionally, rs266729, rs3774261, and rs822393 were statistically associated with MetS, while rs1501299 was not. Haplotype analysis showed a strong linkage disequilibrium between the SNP pairs rs266729/rs822393 and rs1501299/rs3774261, and the haplotype rs266729(G)/rs822393(T) was not statistically associated with MetS. The results show that rs266729 is a lead candidate SNP associated with increased risk of developing T2DM and MetS in a Kinh Vietnamese population, while rs3774261 is associated with MetS only. Further functional characterization is needed to uncover the mechanism underlying the potential genotype-phenotype associations.

is a commensal bacteria species that can cause various illnesses, from mild skin infections to severe diseases, such as bacteremia. The distribution and antimicrobial resistance (AMR) pattern of varies by population, time, geographic location, and hospital wards. In this study, we elucidated the epidemiology and AMR patterns of isolated from a general hospital in Vietnam. This was a cross-sectional study. Data on all infections from 2014 to 2021 were collected from the Microbiology department of Military Hospital 103, Vietnam. Only the first isolation from each kind of specimen from a particular patient was analyzed using the Cochran-Armitage and chi-square tests. A total of 1130 individuals were diagnosed as infection. Among them, 1087 strains were tested for AMR features. Most patients with infection were in the age group of 41-65 years (39.82%). isolates were predominant in the surgery wards, and pus specimens were the most common source of isolates (50.62%). was most resistant to azithromycin (82.28%), erythromycin (82.82%), and clindamycin (82.32%) and least resistant to teicoplanin (0.0%), tigecycline (0.16%), quinupristin-dalfopristin (0.43%), linezolid (0.62%), and vancomycin (2.92%). Methicillin-resistant (MRSA) and multidrug-resistant (MDR) were prevalent, accounting for 73.02% and 60.90% of the total strains respectively, and the strains isolated from the intensive care unit (ICU) had the highest percentage of multidrug resistance (77.78%) among the wards. These findings highlight the urgent need for continuous AMR surveillance and updated treatment guidelines, particularly considering high resistance in MRSA, MDR strains, and ICU isolates. Future research focusing on specific resistant populations and potential intervention strategies is crucial to combat this rising threat.

Medication adherence plays a crucial role in effectively managing hypertension, a significant public health concern, especially in regions like Central Vietnam. This study aimed to assess medication adherence levels among hypertensive patients in primary care settings and explore the factors influencing adherence within this specific population. We conducted a cross-sectional study to evaluate medication adherence and its determinants among individuals with hypertension in Central Vietnam. Medication adherence was assessed using the 5-item version of the Medication Adherence Report Scale self-report. We collected data on the demographics, medical history, lifestyle, hypertension knowledge, along with the patient beliefs and perceptions about hypertension. Logistic regression analysis was employed to identify the key factors associated with their medication adherence. Our study revealed that only half of the hypertensive patients adhered to their prescribed medication regimens. Several factors significantly influenced their medication adherence, including age, ethnicity, educational level, home blood pressure monitoring, healthy diet, time since hypertension diagnosis, hypertension knowledge, and patient beliefs. According to the logistic regression analysis, a healthy diet and patient beliefs emerged as primary predictors of medication adherence. Patients who strongly believed in the necessity of medication demonstrated better adherence, while concerns about overuse and harm were linked to lower adherence levels. This study highlighted the suboptimal levels of medication adherence among hypertensive patients in primary care settings in Central Vietnam. It underscored the urgent need for tailored interventions to address this issue. For the sake of better medication adherence, healthcare providers were suggested to prioritize patient education, address patient beliefs and concerns about medication, and promote the practice of home blood pressure monitoring.

Heart failure (HF) is a global health burden, with high hospitalization and mortality rates. Timely diagnosis is crucial in the management of HF. While noninvasive imaging techniques generally assess changes in the left atrium (LA) or left ventricle (LV) alone, the application of physiological connections between these chambers as echocardiographic parameters, such as the left atrioventricular coupling index (LACI), may be valuable as an early marker of HF. We conducted a cross-sectional study in which 1145 Vietnamese individuals were selected and 160 subjects met the inclusion criteria. These participants were divided into patient and healthy control groups, with 60 HF patients. LACI levels were significantly greater in HF patients, especially those with preserved ejection fraction (HFpEF), than in those with reduced ejection fraction (HFrEF) and controls (59.16 ± 17.94% vs. 41.28 ± 15.27% and 13.15 ± 3.92%, respectively). The LACI showed strong diagnostic value for HFpEF, with an area under the curve (AUC) of 0.951 and an optimal threshold of 33.07 (sensitivity: 97.1%, specificity: 87.3%). Multivariate analysis confirmed LACI as an independent predictor of HFpEF (OR = 1.144, 95% CI 1.087–1.205). Overall, LACI has emerged as an accessible, promising tool for diagnosing HFpEF.

A genome‐wide association study (GWAS) is a powerful tool in investigating genetic contribution, which is a crucial factor in the development of complex multifactorial diseases, such as type 2 diabetes mellitus. Type 2 diabetes mellitus is a major healthcare burden in the Western Pacific region; however, there is limited availability of genetic‐associated data for type 2 diabetes in Southeast Asia, especially among the Kinh Vietnamese population. This lack of information exacerbates global healthcare disparities. In this study, 997 Kinh Vietnamese individuals (503 with type 2 diabetes and 494 controls) were prospectively recruited and their clinical and paraclinical information was recorded. DNA samples were collected and whole genome genotyping was performed. Standard quality control and genetic imputation using the 1000 Genomes database were executed. A polygenic risk score for type 2 diabetes was generated in different models using East Asian, European, and mix ancestry GWAS summary statistics as training datasets. After quality control and genetic imputation, 107 polymorphisms reached suggestive statistical significance for GWAS (≤5 × 10−6) and rs11079784 was one of the potential markers strongly associated with type 2 diabetes in the studied population. The best polygenic risk score model predicting type 2 diabetes mellitus had AUC = 0.70 (95% confidence interval = 0.62–0.77) based on a mix of ancestral GWAS summary statistics. These data show promising results for genetic association with a polygenic risk score estimation in the Kinh Vietnamese population; the results also highlight the essential role of population diversity in a GWAS of type 2 diabetes mellitus.