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This paper presents a reliable solution to enhance the electron transfer and stability of screen-printed carbon electrodes (SPCEs) for the direct detection of pathogenic bacteria. A nanocomposite of silver nanoparticles (AgNPs) and reduced graphene oxide (rGO) was used to modify the SPCEs. Herein, the nanocomposite was synthesized via a hydrothermal method and then characterized by physicochemical methods. The electron transfer rate and electrochemical properties of the AgNP-rGO nanocomposite-modified SPCEs were investigated using cyclic voltammetry (CV) and electrochemical impedance spectroscopy. Measurements were performed for the detection of Salmonella bacteria without any labels. Results showed that the nanocomposite firmly adhered to the surfaces of the SPCEs, led to an increase of approximately 160% in the peak current, and decreased the charge transfer resistance to 0.45 kΩ. Electrochemical stability was found in 30 CV cycles. The modified SPCEs could detect Salmonella bacteria directly at concentrations of 10–105 CFU/mL, with a limit of detection (LoD) of as low as 22 CFU/mL. A possible mechanism was proposed to explain the enhanced electron transfer on the surface and the stability of the AgNP-rGO nanocomposite-modified SPCEs. The biosensor showed high stability, cost-effectiveness, and simplicity for the direct detection of pathogenic bacteria.

Rickettsioses are diseases caused by intracellular Gram-negative bacteria of the Rickettsiaceae family and transmitted through the bite of infected ticks or mites. To investigate the clinical and subclinical characteristics and prognostic severe factors of the disease caused by Rickettsiaceae. A prospective, descriptive cross-sectional study was conducted at Department of Infectious Diseases of two military hospitals in Northern Vietnam from May 2013 to June 2019, in which 88 adult febrile patients caused by (50 patients) or spp. (38 patients) were enrolled. We recorded information regarding epidemiological characteristics (age, geography, residence, occupation), medical history, clinical and subclinical findings, life-threatening complications during treatment, outcomes and some factors predicting serious life-threatening complications in a case record form. Scrub typhus (ST) patients had eschar (70%), skin-conjunctiva congestion (60%) and lymphadenopathy (44%). patients had a higher rate of maculopapular rash (39.5%), no ulcers and no lymphadenopathy detected. The majority of patients had elevated PCT >0.05 ng/µL and increase in liver enzymes and thrombocytopenia. Major prognostic factors for severe complications included diffuse infiltrates on lung X-ray (OR: 19.5; p = 0.014), coarse crackles (OR: 18; p = 0.016), respiratory rate ≥25 cycles/minute (OR: 18; p = 0.016), shortness of breath (OR: 7.44; p = 0.003), pleural fluid (OR: 4.3; p = 0.035) and increase in AST ≥ 200 UI/l (OR: 4.42; p = 0.012). The PCT value is able to distinguish between the two groups with quite high reliability (the area under the ROC curve is 0.75). Eschar and peripheral lymphadenopathy were two valuable clinical symptoms for the diagnosis of scrub typhus and distinguishing 2 groups of diseases. Respiratory distress, increase in AST ≥ 200 UI/l and level of PCT were used as major prognostic factors in patients with Rickettsiaceae.

Despite strict surveillance, still causes life-threatening invasive meningococcal disease (IMD). The study aimed to describe the prevalence, clinical and subclinical features, and treatment outcomes of IMD among young soldiers of the Vietnam People's Army. A prospective, population-based surveillance study was conducted in all Vietnamese military hospitals from January 2014 to June 2021. The presence of was confirmed by PCR or culture from blood or/and CSF. Epidemiological indices (incidence, serogroups, and distribution of cases by length of service), medical history, clinical and sub-clinical features, and treatment outcomes were documented and analyzed. There were 69 IMD cases (91% serogroup B) documented, mainly in conscripts (91%). The highest annual incidence was 3.33/100,000 soldiers per year. Of these cases, 44% were meningitis (n=30), 19% septicemia (n=13), and 38% meningococcemia (n=26). The most common clinical symptoms were neck stiffness (61 cases, 88%), petechial rash (51%), and shock (20 cases, 29%). Laboratory findings showed leukocytosis in 96% of IMD cases, PCT >0.05 (ng/mL) in 100%, elevated leukocyte count (>1,000/mm3) in 71%, and high protein >1 g/L in 70%. The overall mortality rate was 9%. Two cases were found to be resistant to ceftriaxone. Prognostic factors of severity included petechial rash (OR = 9.82, p < 0.001), septicemia (OR = 5.83, p < 0.001), meningococcemia (OR = 6.22, p < 0.001), low platelet count, prolonged prothrombin time; high PCT (AUC = 0.84, p < 0.001), and increased creatinine (AUC = 0.86, p < 0.001). IMD remains a health threat in the armed forces in Vietnam, especially among new recruits. To the best of our knowledge, this is the first study in Vietnam describing ceftriaxone resistance in and suggests the need to reconsider standard empiric therapy for IMD.

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world. 3D models of the real world have been increasingly popular with numerous applications, such as digital twins and smart factory management. In this study, 3D models of mining engineering structures were built based on the CityGML standard. For collecting spatial data, the two most popular geospatial technologies, namely UAV-SfM and TLS were employed. The accuracy of the UAV survey was at the centimeter level, and it satisfied the absolute positional accuracy requirement of creating all levels of detail (LoD) according to the CityGML standard. Therefore, the UAV-SfM point cloud dataset was used to build LoD 2 models. In addition, the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent, therefore, point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models. The resulting 3D CityGML models include 39 buildings at LoD 2, and two mine shafts with hoistrooms, headframes, and sheave wheels at LoD 3.

Recently, digital mines (DM) have plays an increasingly important role in ensuring the safety, effective management and operation of mining activities. One of essential pillars for digital transformation in mining is Building Information Model (BIM), which is also one of the first steps to implement Digital Twin. Additionally, using BIM integrated with Terrestrial Laser Scanning (TLS) and adopting digital technology can provide powerful assistance in life cycle of mining. In this study, integrating BIM and TLS point clouds, which is the basic framework of DM, was applied to build 3D mining information model as well as assessing safety of Nui Beo underground coal mining in Vietnam. Industry Foundation Classes (IFC) standard is used to define the mining information model based on TLS point clouds providing geometric and attributive information of the structures. The 3D model was built for important structures of the mine, including the mine shaft, and tunnels based on TLS point cloud. Additionally, the safety assessment of rail tracks in the mine shaft was performed by comparing the TLS point cloud of rail tracks and their as-designed BIM models. The results showed that the integration of BIM and TLS is a practical solution for the information management and safety assessment of underground coal mines.

Background Dengue remains a major global health concern; while most infections are self-limiting, a subset progresses to severe disease with substantial mortality. Updated data on severe dengue in hospitalized adults, particularly those managed in tertiary referral settings, remain limited. This study aimed to characterize the clinical features, laboratory abnormalities, and outcomes of adults with WHO-defined severe dengue admitted to two tertiary referral hospitals in northern Vietnam. Methods We conducted a prospective observational cohort study of adults with WHO-defined severe dengue admitted to two tertiary referral hospitals in northern Vietnam between 2020 and 2024. Demographic, clinical, laboratory, and outcome data were systematically collected, and survivors were compared with non-survivors to identify factors associated with mortality. Results A total of 104 adults with severe dengue were enrolled, of whom 31 (30%) died. Non-survivors were significantly older than survivors (median age 62 vs 45 years) and more frequently had hypertension (48% vs 23%; p  = 0.011) and diabetes mellitus (45% vs 11%; p  < 0.001). Shock occurred in 81% of non-survivors compared with 15% of survivors ( p  < 0.001). Fatal cases showed more frequent multi-organ failure and metabolic derangements, including acidosis with hyperlactatemia, renal dysfunction, hyperammonemia, coagulopathy, hypoalbuminemia, and elevated cardiac troponin I. In multivariable Firth logistic regression, shock (adjusted odds ratio [aOR] 8.59, 95% CI 2.38–36.27; p  < 0.001) and cumulative organ dysfunction burden (aOR 2.69 per additional organ/system, 95% CI 1.57–5.36; p  < 0.001) were independently associated with in-hospital mortality. Organ dysfunction burden also stratified time-to-event outcomes (log-rank p  < 0.001). Conclusions In this tertiary referral cohort of adults with WHO-defined severe dengue in northern Vietnam, mortality was high and was mainly associated with older age, comorbidities, shock, metabolic failure, and multi-organ dysfunction; shock plus cumulative organ dysfunction provided a practical risk-stratification signal.

Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets. Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment. Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings. Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings. Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.

The WNC algorithm is an adaptive MFP which is developed in order to improve the capability of locating underwater objects and counter the fluctuation of various unmatched envinronment. However, the effectivity of WNC algorithm in high noise, interference envinronments has some drawbacks. This paper presents the result of the combination LMS and WNC for the purpose of reducing the interference's effect on locating objects and enhancing the quality of object resolution. The simulation results show that LMS-WNC improved the probability of detecting, locating the object having the signal with low SNR comparing to WNC algorithm.

Herein, Fe-doped C3N4 high-performance photocatalysts, synthesized by a facile and cost effective heat stirring method, were investigated systematically using powder X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM) and Brunauer–Emmett–Teller (BET) surface area measurement, X-ray photoelectron (XPS), UV–Vis diffusion reflectance (DRS) and photoluminescence (PL) spectroscopy. The results showed that Fe ions incorporated into a g-C3N4 nanosheet in both +3 and +2 oxidation states and in interstitial configuration. Absorption edge shifted slightly toward the red light along with an increase of absorbance in the wavelength range of 430–570 nm. Specific surface area increased with the incorporation of Fe into g-C3N4 lattice, reaching the highest value at the sample doped with 7 mol% Fe (FeCN7). A sharp decrease in PL intensity with increasing Fe content is an indirect evidence showing that electron-hole pair recombination rate decreased. Interestingly, Fe-doped g-C3N4 nanosheets present a superior photocatalytic activity compared to pure g-C3N4 in decomposing RhB solution. FeCN7 sample exhibits the highest photocatalytic efficiency, decomposing almost completely RhB 10 ppm solution after 30 min of xenon lamp illumination with a reaction rate approximately ten times greater than that of pure g-C3N4 nanosheet. This is in an agreement with the BET measurement and photoluminescence result which shows that FeCN7 possesses the largest specific surface area and low electron-hole recombination rate. The mechanism of photocatalytic enhancement is mainly explained through the charge transfer processes related to Fe2+/Fe3+ impurity in g-C3N4 crystal lattice.

Rapid industrialization has led to the pollution of soil and water by various types of contaminants. Heavy metals (HMs) are considered the most reactive toxic contaminants, even at low concentrations, which cause health problems through accumulation in the food chain and water. Remediation using conventional methods, including physical and chemical techniques, is a costly treatment process and generates toxic by-products, which may negatively affect the surrounding environment. Therefore, biosorption has attracted significant research interest in the recent decades. In contrast to existing methods, bacterial biomass offers a potential alternative for recovering toxic/persistent HMs from the environment through different mechanisms for metal ion uptake. This review provides an outlook of the advantages and disadvantages of the current bioremediation technologies and describes bacterial groups, especially extremophiles with biosorbent potential for heavy metal removal with relevant examples and perspectives.