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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.

Blasting is a first preparatory stage that plays a fundamental role in the subsequent operations of an open pit mine. However, its adverse effects can seriously affect the environment and surrounding structures, especially ground vibration, which is measured by peak particle velocity (PPV). The present study proposes a robust model for predicting PPV in open pit mines. An adaptive fuzzy inference neural network (ANFIS) was used as the primary model. The moth–flame optimization (MFO), a swarm-based meta-heuristic algorithm, was integrated to ANFIS, leading to a MFO–ANFIS model, to improve its accuracy. Other intelligent models, such as XGBoost (extreme gradient boosting machine), ANN (artificial neural network), SVM (support vector machine), and two empirical equations (linear and non-linear), were also considered to compare with the proposed MFO–ANFIS model. The findings indicate that the proposed hybrid intelligent MFO–ANFIS model provided the best accuracy (i.e., 98.62%). Meanwhile, the other models provided accuracies of 50.55–96.96%. Among the other models, the artificial intelligence models (i.e., MFO–ANFIS, ANN, XGBoost, and SVM) were recommended to be better in predicting PPV compared to the empirical models. Besides, a sensitivity analysis was also adopted and discussed in this study to understand the role of the input variables in predicting PPV. The results revealed that explosive charge per borehole is more critical than total explosive used per blast; in addition, burden and distance from blast sites are still essential parameters in predicting PPV.

Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of structures, and changing the flow direction of groundwater. Therefore, many studies in recent years have focused on the accurate prediction and control of GV in open-pit mines. In this study, three intelligent hybrid models were examined for predicting GV based on different nature-inspired optimization algorithms and deep neural networks. Accordingly, a deep neural network (DNN) was developed for predicting GV under the enhancement of deep learning techniques. Subsequently, aiming at improving the accuracy and reducing the error of the DNN model in terms of the prediction of blast-induced GVs, three optimization algorithms based on the behaviors of whale, Harris hawks, and particle swarm in nature (abbreviated as WOA, HHOA, and PSOA, respectively) were considered and applied, namely HHOA–DNN, WOA–DNN, and PSOA–DNN, respectively. The results were then compared with those of the conventional DNN model through various performance indices; 229 blasting events in an open-pit coal mine in Vietnam were processed for this aim. Finally, it was found that the proposed intelligent hybrid models outperform the DNN model with deep learning techniques, although it is a state-of-the-art model that has been recommended and claimed by previous researchers. In particular, HHOA, WOA, and PSOA (with global optimization) further improved the accuracy of the DNN model by 1–2%. Of those, the HHOA–DNN model provided the highest performance with a mean-squared-error of 2.361, root mean squared error of 1.537, mean absolute percentage error of 0.123, variance accounted for of 93.015, and coefficient determination of 0.930 on the testing dataset. The findings also revealed that the explosive charge per blast, monitoring distance, and time delay per each blasting group are necessary parameters for predicting GV.

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.

Blasting plays a fundamental role in rock fragmentation, and it is the first preparatory stage in the mining extraction process. However, its undesirable effects, mostly ground vibration, can cause severe damages to the surroundings, such as cracks/collapses of buildings, instability of slopes, deformation of underground space, affect underground water, to name a few. Therefore, the primary purpose of this study was to predict the intensity of ground vibration induced by mine blasting operations with high accuracy, aiming to reduce the severe damages to the surroundings. A novel artificial neural network (ANN)-based cuckoo search optimization (CSO), named as CSO–ANN model, was proposed for this aim based on 118 blasting events that were collected at a quarry mine in Vietnam. Besides, stand-alone models, such as ANN, support vector machine (SVM), tree-based ensembles, and two empirical equations (i.e., USBM and Ambraseys), were considered and developed for comparative evaluation of the performance of the proposed CSO–ANN model. Afterwards, they were tested and validated based on three blasting events in practical engineering. The results revealed that the CSO algorithm significantly improved the performance of the ANN model. In addition, the comparative results showed that the accuracy of the proposed hybrid CSO–ANN model was superior to the other models with MAE (mean absolute error) of 0.178, RMSE (root-mean-squared error) of 0.246, R2 (square of the correlation coefficient) of 0.990, VAF (variance accounted for) of 98.668, and a20-index of 1.0. Meanwhile, the other models only yielded performances in the range of 0.257–0.652 for RMSE, 0.932–0.987 for R2, 20.942–98.542 for VAF and 0.227–0.955 for a20-index. The findings also indicated that explosive charge per borehole has a special relationship with ground vibration intensity. It should be considered and used instead of total explosive charge per blast in some cases, especially for the empirical models.

In this study, a coupling of generalized linear modeling (GLMNET) and nonlinear neural network modeling with multilayer perceptrons (MLPNN), called GLMNETs–MLPNN modeling, was conducted for predicting air over-pressure (AOp) induced by blasting in open-pit mines. Accordingly, six GLMNET models were developed first. Then, their predictions were bootstrap aggregated as the new predictors, and an optimal MLPNN model was developed based on these new predictors. To prove the improvement of the proposed GLMNETs–MLPNN model, the conventional models, such as GLMNET, support vector machine, MLPNN, random forest, and empirical, were considered and developed based on the same dataset. The results of the proposed model then were compared with that of the conventional models in terms of accurate prediction and modeling. The findings revealed that the bootstrap aggregating of six generalized linear models (i.e., GLMNET models) by a nonlinear model (i.e., MLPNN) could enhance the accuracy in predicting AOp with a root-mean-squared error (RMSE) of 2.266, determination coefficient (R2) of 0.916, and mean squared error (MAE) of 1.718. In contrast, the other stand-alone models provided poorer performances with RMSE of 2.981–4.686, R2 of 0.597–0.860, and MAE of 3.156–1.990. Besides, the sensitivity analysis results indicated that burden, stemming, distance, spacing and maximum explosive charge per delay were the most important parameters in predicting AOp.

A comprehensive study from Curcuma longa to powder nano curcuminoids has been carried out. Combining both a low-energy method (phase inversion temperature) and a high-energy method (ultrasonication), a series of narrow size curcuminoid nanoemulsions with average diameters (Z-average) from 10.3 to 27.5 nm and a polydispersity index (PDI) from 0.103 to 0.330 (a.u.) have been prepared. The antioxidant activity of the prepared nano curcuminoid nanoemulsions (NCEs) was investigated. The permeabilities via Frank-cell membrane have been tested and reached a maximum of 85.57% after 90 min. The accumulation of curcuminoids in intestinal rats has been checked and achieved saturated values from 124.97 to 153.56 μg cm−2 after 90 min. All NCEs were stable at the NaCl solution concentration of 0.1–1.0 M. After 60 days of cold-storage, the Z-averages have changed slightly. Moreover, NCEs have been used to prepare instant drinking powder by the spray-drying method.

This study aims to take into account the feasibility of three ensemble machine learning algorithms for predicting blast-induced air over-pressure (AOp) in open-pit mine, including gradient boosting machine (GBM), random forest (RF), and Cubist. An empirical technique was also applied to predict AOp and compared with those of the ensemble models. To employ this study, 146 events of blast were investigated with 80% of the total database (approximately 118 blasting events) being used for developing the models, whereas the rest (20% ~ 28 blasts) were used to validate the models’ accuracy. RMSE, MAE, and R2 were used as performance indices for evaluating the reliability of the models. The findings revealed that the ensemble models yielded more precise accuracy than those of the empirical model. Of the ensemble models, the Cubist model provided better performance than those of RF and GBM models with RMSE, MAE, and R2 of 2.483, 0.976, and 0.956, respectively, whereas the RF and GBM models provided poorer accuracy with an RMSE of 2.579, 2.721; R2 of 0.953, 0.950, and MAE of 1.103, 1.498, respectively. In contrast, the empirical model was interpreted as the poorest model with an RMSE of 4.448, R2 of 0.872, and MAE of 3.719. In addition, other findings indicated that explosive charge capacity, spacing, stemming, monitoring distance, and air humidity were the most important inputs for the AOp predictive models using artificial intelligence.

This paper describes a new technique to enhance the solubility of metal curcumin complexes. The solubility of the complex has increased significantly by the homogenization technique with the assistance of surfactants. The optimum conditions of the method have been determined, such as time, temperature, and surfactant concentration. The antioxidant, antibacterial, and anti-inflammatory activities of these complexes have been studied. The results show that the curcumin complexes have promising potential in antibacterial, antioxidant, and in the treatment of restoring burns in rats. The wounds of rats, treated by using 0.2 mg mL−1 of curcumin and its complex solutions three times daily, have wholly recovered after 12, 15, and 18 days of treatment with curcumin–Ca(II), curcumin–Zn(II), and curcumin–Fe(III), respectively.

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.

In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA–CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA–CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA–CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.

Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue as they may result in the instability of the surrounding structures, highways, water pipes, railways, and residential areas. In this study, the effects of blasting in a quarry mine in Vietnam were examined. A total of 25 blasting events were investigated with the help of an unmanned aerial vehicle, micromate instruments, and blast patterns, and 83 observations were recorded. Subsequently, the fuzzy C-means clustering (FCM) algorithm was applied to classify the 83 observations based on the blast parameters. Finally, based on the classification of the blasts, quantile regression neural network (QRNN) models were developed. The combination of FCM and QRNN models resulted in a novel, hybrid model (FCM-QRNN) for predicting blast-induced ground vibration. The US Bureau of Mines (USBM), random forest (RF), QRNN (without clustering), and artificial neural network (ANN) models were also considered and compared with the FCM-QRNN model to obtain a comprehensive assessment of the proposed model. The results indicate that the proposed FCM-QRNN model has a higher accuracy than the other models: USBM, QRNN, RF, and ANN. The proposed model can be used to control the undesirable effects of blast-induced ground vibration. Although this study and the proposed FCM-QRNN model are original works with positive results, the performance of this model in other locations still needs to be considered as a case study for further scientific information.

The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of ground vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced ground vibration.

Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of blasting, especially ground vibration, on the surrounding environment are significant. Ground vibration spreads to rocky environment and is characterized by peak particle velocity (PPV). At high PPV intensity, structures can be damaged and cause instability of slope. Therefore, accurately predict PPV is needed to protect the structures and slope stability. In this research, a novel intelligent approach for predicting blast-induced PPV was developed. The particle swarm optimization (PSO) and extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the PSO-XGBoost model. Accordingly, the PSO algorithm was used for optimization of hyper-parameters of XGBoost. A variety of empirical models were also considered and applied for comparison of the proposed PSO-XGBoost model. Accuracy criteria including mean absolute error, determination coefficient (R2), variance account for, and root-mean-square error were used for the assessment of models. For this study, 175 blasting operations were analyzed. The results showed that the proposed PSO-XGBoost emerged as the most reliable model. In contrast, the empirical models yielded worst performances.

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.

Air blast waves are recognized as one of the negative effects induced by blasting operations in open pit mines. The intensity of air over-pressure is taken into account as the primary parameter to determine the damages on the surrounding environment. Many researchers commented on the effects of meteorology on blast-induced air overpressure, such as temperature, relative air pressure, wind direction and speed, air humidity, to name a few. However, they were not fully addressed. Therefore, this study aims to fully address the effect of meteorology conditions on blast-induced air over-pressure in open pit mines through the air over-pressure predictive models. Nui Beo open pit mine in Quang Ninh province of Vietnam, where suffers significantly from tropical climate with two generally rainy and dry seasons, was selected as a case study for this aim. The results revealed that the meteorological conditions have a great effect on blast-induced air over-pressure, especially air humidity and wind speed. These contribute to enhance the effect of blasting operation for Nui Beo coal mine in particular, and for all coal mines in Vietnam.

Air quality in the mining industry is a severe environmental issue associated with many health problems. Managing air quality in mining areas has faced many challenges due to the lack of understanding of the climatic factors and physical removal mechanisms of gas contaminants. In order to study the effects of the atmospheric conditions on the pit pollution, the air velocity distribution and gas dispersion behavior were evaluated in the deepest open-pit coal mine in Vietnam based on the monitoring data and numerical modeling. The field study was conducted in Coc Sau open-pit coal mine located in the northeastern of Vietnam. Two fixed monitoring stations were installed at the ground level to measure the wind speed, wind direction, and temperature to evaluate the atmospheric class stability based on the Froude number and Pasquill stability class. These monitoring data were also used for 3D CFD analysis of the polluted gas dispersion behavior. Furthermore, the vertical temperature profile within the pit was measured to determine the existence of the temperature inversion layer. In general, the Froude Number is an estimate of whether the flow can cross high mountains or not and is basically the ratio of the wind perpendicular to the mountain chain to the stability of the atmosphere. The Froude Number was found to be 0.1–0.7 during the 4-day test. With Fr < 1.3, the airflow in the pit is totally decoupled from the airflow above the pit. The existence of the temperature inversion layer was observed. CFD analysis of the air velocity distribution and CO gas dispersion behavior indicates that high dust and gas concentrations within the pit observed during the study were partly attributed to the stability of the atmosphere.

Recently a variety of vibration monitoring devices based on MEMS (micro electro-mechanical system) 3-axis acceleration sensor has been introduced and is gradually replacing analog wire-type geophones for blasting vibration monitoring. Blasting vibration monitoring tasks generally require frequent movement of the monitoring devices. Since accurate device set along the vertical axis is essential at a new location, acceleration sensors sensitive to the gravitational acceleration are not suitable for accurate monitoring of the blasting vibration. In this study, the vibration monitoring system with a 3-axis MEMS acceleration sensor is developed for wireless mesh network monitoring. Individual monitoring units are equipped with an algorithm for reorientation along the direction of gravity once they are placed on a particular baseline. The algorithm aims at automatically adjusting the z-axis and resetting the zero offset value altered after each blasting vibration monitoring and relocation. With this feature, it shows individual unit can be applied as conventional portable devices as well. In addition, comparative studies are also carried out along with conventional units for 3-axis acceleration and primary frequency analysis. There are several advantages of the developed system. Firstly, this system has been designed for easy installation and wireless remote management to provide readings and alerts when the user-defined allowable limit is exceeded. Secondly, due to remote management, it can improve staff safety, reduce human resources, and save time and cost. Thirdly, this system can be positioned over a large area as each sensor can act as a repeater. Finally, multiple sensors can be installed to measure various locations monitoring at the same time. Furthermore, without the cables to interface with operations or accidental damage, this system improves safety and reduces maintenance costs. The readings from the multiple sensors deployed at target locations are transmitted to the management node connected to the PC. Thus, all the live data can be seen on the PC. This system is built to be deployed on mining and construction sites, tunnel, bridges, and other structures. The system is designed with the ultimate goal of understanding challenges and provide solutions to protect assets by the low-cost system with high accuracy and reliability.

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.

One of the most adverse effects encountered during blasting in open-pit mines is ground vibration. The peak particle velocity (PPV) is a measure used for ground vibrations; however, accurate prediction of PPV is challenging for blasters as well as managers. Herein, boosted generalised additive models (BGAMs) were applied for estimating the effects of blast-induced PPV. An empirical equation, support vector machine (SVM) and artificial neural network (ANN) were also adapted and used to approximate the blast-induced PPV for comparison. Herein, a database covering 79 blasting cases at Nui Beo’s open-pit coal mine, Vietnam, were used as a case example. Several performance indicators such as the coefficient of determination (R2), root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the quality of each predictive model. According to the results, the proposed BGAM performed better than the other models, yielding the highest accuracy with an R2 of 0.990, RMSE of 0.582 and MAE of 0.430. ANN and SVM models exhibited only slightly lower performance, while the empirical technique had the worst performance. Two testing blasts were performed to validate the accuracy of the developed BGAMs in practical engineering and the results showed that the BGAMs provided high accuracy than other models. Results also revealed that the elevation difference between the blasting site and monitoring point is one of the predominant parameters governing the PPV predictive models.

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.

Purpose The purpose of this narrative review is to summarize the evidence derived from randomized controlled trials (RCTs) regarding approaches and techniques for lower extremity nerve blocks.

Background: This prospective, randomized, observer-blinded study compared ultrasound-guided supraclavicular (SCB), infraclavicular (ICB), and axillary (AXB) brachial plexus blocks for upper extremity surgery of the elbow, forearm, wrist, and hand. Methods: One hundred twenty patients were randomly allocated to receive an ultrasound-guided SCB (n = 40), ICB (n = 40), or AXB (n = 40). Performance time (defined as the sum of imaging and needling times) and the number of needle passes were recorded during the performance of the block. Subsequently, a blinded observer recorded the onset time, block-related pain scores, success rate (surgical anesthesia), and the incidence of complications. The main outcome variable was the total anesthesia-related time, defined as the sum of performance and onset times. Results: No differences were observed between the 3 groups in terms of total anesthesia-related time (23.1-25.5 mins), success rate (95%-97.5%), block-related pain scores, vascular puncture, and paresthesia. Compared with the supraclavicular and infraclavicular approaches, ultrasound-guided AXBs required a higher number of needle passes (6.1 [SD, 2.0] vs 2.0-2.6 [SD, 1.1-1.8]; both P ≤ 0.001), a longer needling time (7.4 mins [SD, 2.2 mins] vs 4.9-5.5 mins [SD, 1.9-4.2 mins]; both P ≤ 0.016), and a longer performance time (8.5 mins [SD, 2.3 mins] vs 6.0-6.2 mins [SD, 2.1-4.5 mins]; both P ≤ 0.008). Supraclavicular blocks resulted in a higher rate of Horner syndrome (37.5% vs 0%-5%; both P Conclusion: Adjunctive ultrasonography results in similar success rates, total anesthesia-related times, and block-related pain scores for the SCB, ICB, and AXB.

The purpose of this narrative review is to summarize the evidence derived from randomized controlled trials (RCTs) regarding approaches and techniques for lower extremity nerve blocks.Using the MEDLINE (January 1966 to April 2007) and EMBASE (January 1980 to April 2007) databases, medical subject heading (MeSH) terms "lumbosacral plexus", "femoral nerve", "obturator nerve", "saphenous nerve", "sciatic nerve", "peroneal nerve" and "tibial nerve" were searched and combined with the MESH term "nerve block" using the operator "and". Keywords "lumbar plexus", "psoas compartment", "psoas sheath", "sacral plexus", "fascia iliaca", "three-in-one", "3-in-1", "lateral femoral cutaneous", "posterior femoral cutaneous", "ankle" and "ankle block" were also queried and combined with the MESH term "nerve block". The search was limited to RCTs involving human subjects and published in the English language. Forty-six RCTs were identified.Compared to its anterior counterpart (3-in-1 block), the posterior approach to the lumbar plexus is more reliable when anesthesia of the obturator nerve is required. The fascia iliaca compartment block may also represent a better alternative than the 3-in-1 block because of improved efficacy and efficiency (quicker performance time, lower cost). For blockade of the sciatic nerve, the classic transgluteal approach constitutes a reliable method. Due to a potentially shorter time for sciatic nerve electrolocation and catheter placement than for the transgluteal approach, the subgluteal approach should also be considered. Compared to electrolocation of the peroneal nerve, electrostimulation of the tibial nerve may offer a higher success rate especially with the transgluteal and lateral popliteal approaches. Furthermore, when performing sciatic and femoral blocks with low volumes of local anesthetics, a multiple-injection technique should be used.Published reports of RCTs provide evidence to formulate limited recommendations regarding optimal approaches and techniques for lower limb anesthesia. Further well-designed and meticulously executed RCTs are warranted, particularly in light of new techniques involving ultrasonographic guidance.