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An atomic absorption spectrometric (AAS) method was performed to determine the total, inorganic, and organic arsenic in fish sauce. The total organic arsenic was calculated from the total and inorganic arsenic values quantified using the hydride generation AAS (HG-AAS). Under optimal experimental conditions at the absorbance wavelength of 193.7 nm, the concentration of inorganic arsenic in fish sauce ranged from 0.05 to 1.2 mg/L, with a limit of detection (LOD) of 0.015 mg/L. The detectable total arsenic concentrations varied widely, ranging from 0.03 to 2.5 mg/L with the LOD of as low as 0.01 mg/L. The practical applicability of the method was demonstrated with the recovery in the range from 97 to 102% for inorganic arsenic, and 97 to 101% for organic arsenic. The method was applied to the analysis of commercial products from Nha Trang, Phan Thiet, and Phu Quoc City, Vietnam. The total organic arsenic in fish sauce samples determined by HG-AAS was compared with the results of liquid chromatography-inductively coupled plasma-mass spectrometry (HPLC-ICP/MS). The f-test and t-test showed null hypothesis for acceptable variance and mean at a confidence level of 95%. The results showed that the HG-AAS method had high efficiency, accuracy, and sensitivity in quantifying inorganic and total organic arsenic in fish sauce using simple instrumentation.

The present paper's primary goal is to propose a novel hybrid model with high reliability to predict peak particle velocity (PPV)—a ground vibration evaluation unit in mine blasting. This model is based on the coupling of the multivariate adaptive regression splines (MARS), particle swarm optimization (PSO), and multi-layer perceptron neural networks (MLP). To this end, a strategy of stacking the MARS models was applied. Multiple MARS models were developed first with different hyper-parameters. Subsequently, the outcome predictions from these MARS models were merged as a new data set. The MLP model was then developed based on the newly generated data set, called the MARS–MLP model. To improve the accuracy and reduction of the MARS–MLP model's error, the PSO algorithm was applied in terms of optimization of the MARS–MLP's weights, called the MARS–PSO–MLP model. The proposed MARS–PSO–MLP model was then compared with the stand-alone MARS, MLP, empirical models, and the hybrid PSO–MLP model (without stacking MARS models) using the same data set. The results revealed that the proposed strategies could significantly boost the MARS and MLP models' performance with the PSO algorithm's effective help. The proposed MARS–PSO–MLP model yielded the highest accuracy and reliability with a root-mean-squared error (RMSE) of 1.569, mean absolute error (MAE) of 1.017, and squared-correlation (R2) of 0.902. In comparison, the stand-alone models (i.e., MARS and MLP) and the hybrid model of PSO–MLP provided lower performances with an RMSE of 1.582 to 1.704, MAE of 0.941 to 1.427, and R2 of 0.871 to 0.891. In contrast, poor performance with an RMSE of 5.059, MAE of 3.860, and R2 of 0.127 was found for the empirical model, and it is not a reliable method to predict PPV in this study. This work's findings also indicated that explosive charge per delay, monitoring distance, spacing, powder factor, and burden have significant effects on PPV, the incredibly explosive charge per delay, and monitoring distance. Remarkable, the stemming variable has a minimal impact on PPV, and its role in the modeling of PPV is not exact.

This work describes a rapid, selective, and sensitive method by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to detect melamine (MEL) in milk and dairy products. The optimal conditions of liquid chromatographic separation extraction and mass spectroscopy of MEL have also been examined. The linear range for analyte detected by the method was 0.5÷100.0 ng/mL, with correlation coefficients was 0.999. Mean recoveries of the method in the real samples at three spike levels (low, medium, and high) were within the range of 98.5% ÷102.5% (n =7). LOD, LOQ values of the method were 10 and 30 ng/mL, respectively. The influence of the matrix effect on the accuracy, repeatability, and recovery of the process was insignificant. The proposed method was used to quantify the content of this compound in various real samples, which were collected in Ho Chi Minh City-Vietnam in 2020.

The principal object of this study is blast-induced groundvibration (PPV), which is one of the dangerous side effects of blastingoperations in an open-pit mine. In this study, nine artificial neuralnetworks (ANN) models were developed to predict blast-induced PPV inNui Beo open-pit coal mine, Vietnam. Multiple linear regression and theUnited States Bureau of Mines (USBM) empirical techniques are alsoconducted to compare with nine developed ANN models. 136 blastingoperations were recorded in many years used for this study with 85% ofthe whole datasets (116 blasting events) was used for training and the rest15% of the datasets (20 blasting events) for testing. Root Mean SquareError (RMSE), Determination Coefficient (R2), and Mean Absolute Error(MAE) are used to compare and evaluate the performance of the models.The results revealed that ANN technique is more superior to othertechniques for estimating blast-induced PPV. Of the nine developed ANNmodels, the ANN 7-10-8-5-1 model with three hidden layers (ten neuronsin the first hidden layer, eight neurons in the second layers, and fiveneurons in the third hidden layer) provides the most outstandingperformance with an RMSE of 1.061, R2 of 0.980, and MAE of 0.717 ontesting datasets. Based on the obtained results, ANN technique should beapplied in preliminary engineering for estimating blast-induced PPV inopen-pit mine.

Air overpressure (AOp) is one of the products of blastingoperations in open-pit mines which have a great impact on the environmentand public health. It can be dangerous for the lungs, brain, hearing and theother human senses. In addition, the impact on the surroundingenvironment such as the vibration of buildings, break the glass doorsystems are also dangerous agents caused by AOp. Therefore, it should beproperly controlled and forecasted to minimize the impacts on theenvironment and public health. In this paper, a Lasso and Elastic-NetRegularized Generalized Linear Model (GLMNET) was developed forpredicting blast-induced AOp. The United States Bureau of Mines(USBM) empirical technique was also applied to estimate blast-inducedAOp and compare with the developed GLMNET model. Nui Beo open-pitcoal mine, Vietnam was selected as a case study. The performance indicesare used to evaluate the performance of the models, including Root MeanSquare Error (RMSE), Determination Coefficient (R2), and Mean AbsoluteError (MAE). For this aim, 108 blasting events were investigated with theMaximum of explosive charge capacity, monitoring distance, powderfactor, burden, and the length of stemming were considered as inputvariables for predicting AOp. As a result, a robust GLMNET model wasfound for predicting blast-induced AOp with an RMSE of 1.663, R2 of0.975, and MAE of 1.413 on testing datasets. Whereas, the USBMempirical method only reached an RMSE of 2.982, R2 of 0.838, and MAEof 2.162 on testing datasets.

Blasting is one of the most effective methods for fragmenting rock in quarries. Nevertheless, itsadverse effects are significant, especially blast-induced ground vibration. Field measurement andempirical equations are simple methods to determine and estimate the intensity of blast-induced groundvibration. However, we cannot evaluate the effects of blast-induced ground vibration on the surroundingenvironment based on these outcomes. Therefore, this study explores the relation between seismiccoefficient and rock properties through field measurements and an empirical model for evaluating theeffect of blast-induced ground vibration in open-pit mines. Accordingly, the seismic coefficient (K) isconsidered the main objective in this study. Firstly, it was determined based on the rock properties.Subsequently, an empirical model for estimating blast-induced ground vibration was developed based onfield measurements. This empirical equation was then expanded to determine K to check whether itmatches the determined K by the rock properties. Finally, it was used as the threshold to determine themaximum explosive charged per delay to ensure the safety of the surrounding environment from blastinducedground vibration. For this aim, the Thuong Tan III quarry (in Binh Duong province, Vietnam)was selected as a case study. Fifth-teen blasting events with a total of 75 blast-induced ground vibrationvalues were recorded and collected. An empirical equation for estimating blast-induced ground vibrationwas then developed based on the collected dataset, and K was determined in the range of 539 to 713 forthe Thuong Tan III quarry. Based on the measured blast-induced ground vibrations, developed empiricalmodel, and K values, the Phase 2 software was applied to simulate the effects of blast-induced groundvibration on the stability of slopes as one of the impacts on the surrounding environment. From thesimulation results, we can determine the maximum explosive charged per delay for each type of rock toensure the stability of the slope.

Blasting is an essential task in open-pit mines for rock fragmentation. However, its dangerous side effects need to be accurately estimated and controlled, especially ground vibration as measured in the form of peak particle velocity (PPV). The accuracy for estimating blast-induced PPV can be improved by hybrid artificial intelligence approach. In this study, a new hybrid model was developed based on Hierarchical K-means clustering (HKM) and Cubist algorithm (CA), code name HKM-CA model. The HKM clustering hybrid technique was used to separate data according to their characteristics. Subsequently, the Cubist model was trained and developed on the clusters generated by HKM. Empirical technique, the benchmark algorithms [random forest (RF), support vector machine (SVM), classification and regression tree (CART)], and single CA model were also established for benchmarking the HKM-CA model. Root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE) were the key indicators used for evaluating the model performance. The results revealed that the proposed HKM-CA model was a powerful tool for improving the accuracy of the CA model. Specifically, the HKM-CA model yielded a superior result with an RMSE of 0.475, R2 of 0.995, and MAE of 0.373 in comparison to other models. The proposed HKM-CA model has the potential to be used for predicting blast-induced PPV on-site to control undesirable effects on the surrounding environment.

Relevance of the study is explained by the need to build a large amount of underground traffic arteries in the complex structural rock masses of Vietnam. The solution of the problem can serve as a basis for optimizing the processes of construction of underground multipurpose facilities. Purpose of the study is to determine regularities between technological stresses due to blasting workings in a new tunnel and keeping of construction and rocks in the existing tunnel to optimize the parameters of the tunnel poling boards. The most important task of the study is to obtain quantitative parameters of stress of the rock mass by simulating the conditions of a tunnel under specified conditions using the finite element method and taking into account that the explosion pressure during expansion of the auxiliary tunnel before the design section is fixed on the contour of the designed tunnel. The subject of the research is the Hai Van transport tunnel in the North-South Vietnam road. Results and discussion. The methodology and results of numerical modeling using the Phase 2 program for the conditions of construction of the main and auxiliary tunnels are given. The parameters of the distribution of stresses and strains in the vicinity of the auxiliary and main tunnels with varying explosive pressure are obtained. The quantitative values of safe explosive pressure for poling boards in the elements of the system of both tunnels were obtained. The safety parameters of blasting workings are recommended (according to the stability factor of poling boards). Conclusions. Technogenic stresses generated by blasting workings in the rock mass that encloses building sites do not destroy the poling boards of a tunnel, if they do not exceed the value specified for these conditions; they are technologically regulated by choosing rational blasting parameters at any stage of construction.

Surgical site infection (SSI), mainly caused by Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), is considered the most frequent complication in a surgical patient. Globally, surgical site infection accounts for 2.5%-41.9% and even higher rates in developing countries. SSI affects not only the patient’s health but also the development of society. Like previous reports, a surgical suture increases the hazard of SSI due to its structure. The antibacterial suture is the most effective solution to decrease the SSI. Due to some unique properties, nano-zinc oxide (ZnO NPs) is one of the promising antibacterial agents for coating on the suture. In this study, we aim to synthesize the ZnO NPs using Piper betle leaf extract and used it to coat the suture. The effect of synthesis parameters on the size and morphology of ZnO NPs was studied as well. The UV-Vis spectrum indicated the formation of ZnO NPs with λ max at around 370 nm. The volume of leaf extract plays a role in controlling the size and morphology of zinc oxide nanoparticles. The average particle size of as-synthesized ZnO NPs was around 112 nm with a hexagonal and spherical shape. Other than that, the results proved that ZnO NPs performed a high antibacterial activity against S. aureus and E. coli with its antibacterial effectiveness up to 5 days. The ZnO NP-coated sutures also exhibited a high performance on bacterial inactivation. With key findings, this study made a tremendous contribution to lowering the burden on medical services in terms of medical treatment cost in developing countries.

Nowadays, construction material quarries in Dong Nai Province are exploiting with large quarrying depth, and the annual output could reach to tens of million cubic meters. The blasting frequency could be reached to hundreds of times, so the frequency is the major reason decreasing the cohesion of rock mass. Therefore, the surrounding area of blasting holes is broken, especially the area next to the final border where bench slope angle is not implemented as that of design stage, as well as the back break, also causes fractures on the bench slope, resulting in instability and unsafety due to falling rock. In this paper, the author also wants to introduce the pre blasting and the method to define blasting parameters to increase the stabilization of Slopes in Tan Cang quarry NO.1 in Vietnam.

A selective, sensitive, and rapid method by using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) for the determination of histamine in fish and fish sauce was developed. The optimal conditions of liquid chromatographic separation and mass spectroscopy of histamine have also been investigated. The linear ranges of the method were 20.0 ÷ 1000 ng/mL, and the corresponding correlation coefficient was 0.9993. Mean recoveries of the analyte at three spike levels (low, medium, and high) were within the range of 98.5% ÷ 102.5% (n = 7). The limit of detection (LOD) and limit of quantification (LOQ) values were 3.83 and 11.50 ng/mL for the fish sauce sample and 4.71 and 14.12 ng/mL for the fish sample, respectively. The influence of the matrix effect on the accuracy, repeatability, and recovery of the method was negligible. The recommended method was applied to determine the content of this substance in 21 fish sauce samples and 4 kinds of fish samples, which were collected from Ho Chi Minh City, Vietnam, in 2019.

In this scientific report, a new technique of artificial intelligence which is based on k-nearest neighbors (KNN) and particle swarm optimization (PSO), named as PSO-KNN, was developed and proposed for estimating blast-induced ground vibration (PPV). In the proposed PSO-KNN, the hyper-parameters of the KNN were searched and optimized by the PSO. Accordingly, three forms of kernel function of the KNN were used, Quartic (Q), Tri weight (T), and Cosine (C), which result in three models and abbreviated as PSO-KNN-Q, PSO-KNN-T, and PSO-KNN-C models. The valid of the proposed models was surveyed through comparing with those of benchmarks, random forest (RF), support vector regression (SVR), and an empirical technique. A total of 152 blasting events were recorded and analyzed for this aim. Herein, maximum explosive per blast delay (W) and the distance of PPV measurement (R), were used as the two input parameters for predicting PPV. RMSE, R2, and MAE were utilized as performance indicators for evaluating the models’ accuracy. The outcomes instruct that the PSO algorithm significantly improved the efficiency of the PSO-KNN-Q, PSO-KNN-T, and PSO-KNN-C models. Compared to the three benchmarks models (i.e., RF, SVR, and empirical), the PSO-KNN-T model (RMSE = 0.797, R2 = 0.977, and MAE = 0.385) performed better; therefore, it can be introduced as a powerful tool, which can be used in practical blasting for reducing unwanted elements induced by PPV in surface mines.

Blasting is one of the cheapest and effective methods for breaking rock mass in open-pit mines. However, its side effects are not small such as ground vibration (PPV), air overpressure, fly rock, back break, dust, and toxic. Of these side effects, blast-induced PPV is the most dangerous for the human and surrounding environment. Therefore, evaluating and accurately forecasting blast-induced PPV is one of the most challenging issues facing open-pit mines today. In this paper, a series of artificial neural network models were applied to predict blast-induced PPV in an open-pit coal mine of Vietnam; 68 blasting events were used in this study for development of the ANN models. Of the whole dataset, 80% (approximately 56 observations) were used for the training process, and the rest of 20% (12 observations) were used for the testing process. Five ANN models were developed in this study with the difference in the number of hidden layers. The ANN 2-5-1; ANN 2-8-6-1; ANN 2-5-3-1; ANN 2-8-6-4-1; and ANN 2-10-8-5-1 models were considered in this study. An empirical technique was also conducted to estimate blast-induced PPV and compared to the constructed ANN models. For evaluating the performance of the models, root-mean-squared error (RMSE) and determination coefficient (R2) were used. The results indicated that the ANN 2-10-8-5-1 model (10 neurons in the first hidden layer, 8 neurons in the second hidden layer, and 5 neurons for the third hidden layer) yielded a superior performance over the other models with an RMSE of 0.738 and R2 of 0.964. In contrast, the empirical performed poorest performance with an RMSE of 2.670 and R2 of 0.768. This study is a new approach to predict blast-induced PPV in open-cast mines aim to minimize the adverse effects of blasting operations on the surrounding environment.

A rapid, sensitive, and selective method for determination of thorium based on the complex withortho-ester tetra-azophenylcalix[4]arene (TEAC) was described. In the presence of pH of 4–6, TEAC-Th(IV) complex is extracted from an acidic aqueous solution into chloroform layer. The absorbance intensity of complex was measured by UV-Vis spectrometer at 525 nm and the molar absorptivity was found to be 2.4 × 104. Beer’s law was obeyed in the range of 1.0 to 25 × 10−5 M thorium(IV). The effects of pH, TEAC concentration, and shaking time were also studied. The tolerance limits for several metal ions were calculated. The proposed method was applied to the determination of thorium in synthetic solution and in the monazite sand samples with good results.

Type IX secretion system (T9SS), exclusively present in the Bacteroidetes phylum, has been studied mainly in Flavobacterium johnsoniae and Porphyromonas gingivalis. Among the 18 genes, essential for T9SS function, a group of four, porK-N (P. gingivalis) or gldK-N (F. johnsoniae) belongs to a co-transcribed operon that expresses the T9SS core membrane complex. The central component of this complex, PorM (or GldM), is anchored in the inner membrane by a trans-membrane helix and interacts through the outer membrane PorK-N complex. There is a complete lack of available atomic structures for any component of T9SS, including the PorKLMN complex. Here we report the crystal structure of the GldM and PorM periplasmic domains. Dimeric GldM and PorM, each contain four domains of ~180-Å length that span most of the periplasmic space. These and previously reported results allow us to propose a model of the T9SS core membrane complex as well as its functional behavior., No structural data for the bacterial type IX secretion system (T9SS) are available so far. Here, the authors present the crystal structures of the periplasmic domains from two major T9SS components PorM and GldM, which span most of the periplasmic space, and propose a putative model of the T9SS core membrane complex.