Fiber-Optic Sensing and UAV-Platform Techniques for Innovative Mineral Exploration (FUTURE) is a joint European Union and South African consortium project funded under ERA-NET Cofund ERA-MIN3. The project aims to develop cost-effective and novel seismic methods to image geological structures ahead of the mine face, ultimately leading to improved decision-making and safer mining practices. In this study we present results from numerical simulations conducted at South Deep Gold Mine for active in-mine seismic surveys. The study uses a finite differencing code known as WAVE3D, which models models the propagation of a seismic wave generated by stress pertubations that are applied to a defined grid. The numerical simulation model included two major ore bodies (Black Reef and Ventersdorp Contact Reef) and mine tunnels. The model was constrained using laboratory physical property measurements from underground borehole samples such as seismic velocities (e.g., compressional and shear waves) and bulk densities. Two case studies were created, one modelled the active shots and receivers on surface, and the other modelled the shots at depth (in a mine tunnel at ~4000 m depth) with receivers on surface. In both cases, the objective was to investigate the wavefield propagation through the rockmass between the mine tunnel and surface. The simulated wavefield produced by the source located along the mine tunnel was successfully recorded on surface. The surface-recorded wavefield exhibits clear one-way P-wave arrival times at approximately 450 ms to 500 ms, which can be used to calculate the surface-tunnel P-wave refraction tomogram. The numerical simulation results from the study were used to optimise the acquisition parameters of the in-mine seismic surveys conducted under the FUTURE project.

A direct, high-throughput and accurate method, multi-molecular infrared spectroscopic (MMIR) data fusion, was established for the detection of trace of estrone, diethylstilbestrol, betamethasone and prednisone acetate in peptide powders based on near-infrared and mid-infrared fusion datasets. Tri step MMIR with progressively improved resolution captured key fingerprint features of hormones in the regions of 3430-3250, 2950-2850, 1750-1550 and 1335-1050 cm-1 which were exclusively selected as the datasets. The back propagation neural network (BPNN) model with the best performance used a mid-level fusion strategy and achieved excellent root mean square error of prediction (RMSEP, 1.101 mg kg-1) and residual prediction deviation (RPD, 9.890). Compared to those of single dataset models, the RMSEP was reduced by more than 82.03%. This method had remarkable practical significance for the direct and accurate quality evaluation of peptide powder products with favorable limit of detection (LOD) but without pretreatment and chemical agents, verifying the feasibility of multiple analytical instruments for rapid and comprehensive detection of powdered foods.

Abstract The ultrasonic wave propagation velocity (UPV) technique enables the evaluation of mechanical properties of concrete, including fiber reinforced concrete (FRC). Therefore, the objective of this study is to verify potential correlations between the mechanical properties of FRC and UPV, based on experimental data selected after a systematic literature review. Additionally, relationships between UPV and compressive strength were assessed using models proposed by researchers. An artificial neural network (ANN) technique was applied to analyze which properties, when associated with UPV, assist in estimating the compressive strength of FRC. It was observed that the literature-proposed models for estimating compressive strength using UPV proved to be ineffective. The ANN model resulted in a determination coefficient of 0.973, an accuracy of 3.18 MPa, utilizing UPV, cement consumption, and the water-to-cement ratio as input data, with three neurons in the hidden layer and one output data (compressive strength).

Resumo A partir da técnica de velocidade de propagação de onda ultrassônica (VPU) é possível avaliar propriedades mecânicas de concretos, incluindo o concreto reforçado com fibras (CRF). Assim, o objetivo deste trabalho é verificar possíveis correlações entre as propriedades mecânicas do CRF e VPU, com base em dados experimentais de publicações selecionados após uma revisão sistemática da literatura. Também foram avaliadas relações entre a VPU e a resistência à compressão a partir de modelos propostos por pesquisadores. Aplicou-se a técnica de rede neural artificial (RNA) para analisar quais propriedades, quando associadas com a VPU, auxiliam na estimativa da resistência à compressão do CRF. Observou-se que os modelos propostos pela literatura para estimar a resistência à compressão por meio da VPU não foram eficazes. O modelo de RNA resultou em um coeficiente de determinação de 0,973, uma acurácia de 3,18 MPa e utilizou como dados de entrada a VPU, o consumo de cimento e a relação a/c, além de três neurônios na camada escondida e um dado de saída (resistência à compressão).

RESUMEN Los lechos lacustres secos son una de las mayores fuentes de polvo en el mundo, y en las últimas décadas han causado problemas ambientales en las áreas circundantes a éstos. En este estudio, el desecado lago Urmia fue la principal fuente de polvo entre el 24 y el 25 de abril de 2017. El análisis sinóptico reveló que la intensa tormenta de polvo fue provocada por un fuerte ciclón en el Mar Negro y un sistema de baja presión sobre el centro de Irak, junto con un vasto sistema de alta presión. El análisis de trayectorias basado en HYSPLIT mostró que los altos niveles de PM10 registrados en la región del lago Urmia del 23 al 26 de abril de 2017 influyeron en el oeste de Azerbaiyán, el sur del mar Caspio, el suroeste de Kazajstán, el noroeste de Uzbekistán y el oeste de Turkmenistán. Las masas de aire más polvorientas (PM10 > 400 µg m-3) afectaron el sur del Mar Caspio y el oeste de Azerbaiyán. Los esquemas de polvo del modelo WRF-Chem simularon con precisión tanto la propagación del polvo como su perfil vertical, que ascendió a 5 km del lago; sin embargo, los esquemas de polvo AFWA y GOCART mostraron que las variaciones en los niveles de PM10 fueron previas a las concentraciones superficiales medidas en cinco estaciones alrededor del lago Urmia entre el 23 y el 26 de abril. Más aún, el modelo predijo niveles máximos 12 h antes que las máximas concentraciones superficiales de PM10 medidas en las estaciones durante el periodo de estudio.

ABSTRACT Dried lake beds are one of the largest sources of dust in the world, causing environmental problems in the surrounding areas. In this study, the desiccated Urmia Lake was the primary source of dust for all nearby synoptic stations during the April 24-25, 2017 dust episode. Synoptic analysis revealed that the heavy dust storm was triggered by a strong Black Sea cyclone and a low-pressure system over central Iraq in conjunction with a vast high-pressure system. HYSPLIT-based trajectory analysis showed that high PM10 recorded over the Urmia Lake region on April 23-26, 2017, influenced western Azerbaijan, the south of the Caspian Sea, southwestern Kazakhstan, northwestern Uzbekistan, and western Turkmenistan. The dustiest air masses (PM10 > 400 µg m-3) affected the south of the Caspian Sea and western Azerbaijan. Furthermore, the WRF-Chem model was run to evaluate the spatial distribution of dust particles in the study region. The vertical profile revealed that the simulated dust concentration ascended to 5 km from the lake. The WRF-Chem dust schemes accurately simulated dust propagation and the vertical dust profile over Urmia Lake; however, the AFWA and GOCART dust schemes showed that PM10 fluctuating changes were earlier than the measured surface PM10 at five stations around Urmia Lake on April 23-26, 2017. Furthermore, the maximum amount anticipated by the model simulation was 12 h earlier than the maximum surface mass concentration of measured PM10 at the stations throughout the period.

Abstract Increasing evidences demonstrate the role of sensory innervation in bone metabolism, remodeling and repair, however neurovascular coupling in bone is rarely studied. Using microfluidic devices as an indirect co-culture model to mimic in vitro the physiological scenario of innervation, our group demonstrated that sensory neurons (SNs) were able to regulate the extracellular matrix remodeling by endothelial cells (ECs), in particular through sensory neuropeptides, i.e. calcitonin gene-related peptide (CGRP) and substance P (SP). Nonetheless, still little is known about the cell signaling pathways and mechanism of action in neurovascular coupling. Here, in order to characterize the communication between SNs and ECs at molecular level, we evaluated the effect of SNs and the neuropeptides CGRP and SP on ECs. We focused on different pathways known to play a role on endothelial functions: calcium signaling, p38 and Erk1/2; the control of signal propagation through Cx43; and endothelial functions through the production of nitric oxide (NO). The effect of SNs was evaluated on ECs Ca2+ influx, the expression of Cx43, endothelial nitric oxide synthase (eNOS) and nitric oxide (NO) production, p38, ERK1/2 as well as their phosphorylated forms. In addition, the role of CGRP and SP were either analyzed using respective antagonists in the co-culture model, or by adding directly on the ECs monocultures. We show that capsaicin-stimulated SNs induce increased Ca2+ influx in ECs. SNs stimulate the increase of NO production in ECs, probably involving a decrease in the inhibitory eNOS T495 phosphorylation site. The neuropeptide CGRP, produced by SNs, seems to be one of the mediators of this effect in ECs since NO production is decreased in the presence of CGRP antagonist in the co-culture of ECs and SNs, and increased when ECs are stimulated with synthetic CGRP. Taken together, our results suggest that SNs play an important role in the control of the endothelial cell functions through CGRP production and NO signaling pathway.

ABSTRACT Wildfires pose a significant threat to natural ecosystems, human lives, and properties worldwide. Developing effective prevention and control strategies is crucial for minimizing the risk of catastrophic wildfire events. In this context, computational modelling has emerged as a valuable tool for simulating and analyzing wildfire behavior, which can aid in identifying potential areas for intervention and prevention measures. This article presents a cellular automata model for simulating forest fires in Sete Cidades National Park, Brazil. The model takes into account the wind direction and speed, vegetation type, and probability of burning in a two-dimensional lattice representing the forest area of interest. The simulation output is analyzed to determine the extent of the damage caused by the fire and to identify critical parameters for wildfire propagation. The results highlight the importance of considering wind direction and speed when developing prevention and control strategies for wildfires. By providing insights into the behavior of wildfires, computational modeling can support decision-making processes and facilitate the development of effective policies to manage the risk of wildfires.

Abstract This study addresses the prediction of fatigue life in SAE AMS 7475-T7351 aluminum alloys under variable loads, commonly used in the construction of aircraft fuselages. The main objective of the research was to develop a numerical-experimental procedure to analyze crack growth, using the Walker’s approach which considers the effects of the stress ratio R on the fatigue crack growth rate d a / d N, combined with the Finite Element Method and Linear Regression of the Stress Intensity Factor. Observations showed that Walker’s model effectively consolidated fatigue crack propagation data for various stress ratios when applied longitudinally to L-T rolling orientation, due to low dependence of exponent m on R-value in d a / d N equation. Simple averaging of m values effectively calculated Walker’s exponent. The methodology employed experimental tests following ASTM standards for tension, fracture toughness, and fatigue, complemented by Finite Element Method (FEM) simulations. The Walker’s model proved more effective, while the Paris-Erdogan model, which ignores the R effect, resulted in overly conservative service life estimates. The principle of similitude suggests that this methodology could be effective in predicting fatigue life in cases with complex geometries, where calculating the Stress Intensity Factor Fracture parameter is challenging and the Finite Element Method shows efficiency.

Abstract To obtain a long propagation length and a small effective mode area, a new hybrid plasmonic waveguide model is proposed. We design a hybrid plasmonic waveguide composed of chiral metamaterials, silver metal at its center, and surrounded by a graphene interface using the COMSOL environment based on the finite element method. The interface thickness is 1.02nm, where three monolayer graphene were used. The waveguide properties can be achieved with the range of radius 5-60 nm, chemical potential 0.1-1 eV, and refractive index of metamaterial is -3 within the wavelength used. Moreover, we obtained a long propagation length, a small effective mode area, high power confinement, and lower loss. The results showed that the presence of graphene positively affects the waveguide properties, while increasing the chirality parameter, K, does not have a good effect. At the chemical potential 1eV, the propagation length will be 2.45mm in the absence of chirality, while it equals 0.4mm at K=1 with the used wavelength 1.55μm.

Abstract Mobile radio channel and its effects on the radio link path place fundamental limitations on the performance of wireless communication systems. This paper sets out a new approach for wideband modeling of mobile radio channels to classify distinct environments. It applies the hidden Markov model technique to different data sets of the main temporal dispersion parameters of the mobile radio channel: average delay and delay spread. The applied technique has proven to design properly the transitions among those temporal dispersion parameters, mapping statistically the transitions from one point in space to another, and it has revealed to be a feasible option to classify and describe statistically the wideband radio channel in each propagation medium.

Abstract In this paper, the application of AI and machine learning (ML) to the study of wireless propagation channels is investigated in two parts: first, an artificial neural network model is used to improve path loss prediction, and then, a pattern recognition model using multilayer perceptron (MLP) networks is used to identify and remove impulsive noise in power delay profiles (PDP). These studies were conducted based on field measurements in the 2400 MHz band in a public square with vegetation. The results are analyzed and compared with ordinary least squares (OLS) nonlinear regression results and results from similar studies. The Root Mean Square Error (RMSE) values between the experimental results of mean path loss and those provided by each propagation model are presented. The adjustment performed by OLS nonlinear regression and ANN significantly reduced the RMSE. The best results are those presented by artificial neural networks, with RMSE of 0.39 when using four neurons in the hidden layer of the ANN. The ANN used to identify and remove impulsive noise in power delay profiles (PDP) through pattern recognition proved to be more efficient than the CFAR technique. ANN technique found a larger number of valid multipaths compared to the CFAR technique.

As one of the most important types of evidence at the scene of the crime, the rapid identification of the human bloodstain is of great significance to solve the criminal case. In this paper, the spectral data of different species of bloodstain samples including human, chicken and pig were acquired by using a hand-held near-infrared spectrometer. Then, the training models were established via convolutional neural network-support vector machine algorithm. Meanwhile, the traditional support vector machine, genetic algorithm-back propagation and random forest classification algorithms were also compared. The results showed that the prediction accuracy of convolutional neural network-support vector machine algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a handheld near-infrared spectrometer and convolutional neural network-support vector machine algorithm could identify the species of bloodstain efficiently, non-destructively, quickly and accurately and it provided a new technical reference for bloodstains detection and identification.

It is important to identify the types of tobacco diseases accurately and take effective control measures in time to improve the efficiency of tobacco planting. In this paper, a hand-held near-infrared spectrometer was used to collect the spectral data of different types of tobacco disease samples. The training models were established via convolutional neural network algorithm. Meanwhile, the traditional classification algorithms support vector machine and back propagation neural network were also compared. The results showed that the prediction accuracy of convolutional neural network algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a hand-held near-infrared spectrometer and convolutional neural network algorithm could identify tobacco leaf disease species efficiently, non-destructively, quickly and accurately, which provided a new technical reference for tobacco leaf disease species detection and identification.

Abstract Introduction: Cardiac arrest or arrhythmia caused by bupivacaine may be refractory to treatment. Apelin has been reported to directly increase the frequency of spontaneous activation and the propagation of action potentials, ultimately promoting cardiac contractility. This study aimed to investigate the effects of apelin-13 in reversing cardiac suppression induced by bupivacaine in rats. Methods: A rat model of cardiac suppression was established by a 3-min continuous intravenous infusion of bupivacaine at the rate of 5 mg.kg−1.min−1, and serial doses of apelin-13 (50, 150 and 450 μg.kg−1) were administered to rescue cardiac suppression to identify its dose-response relationship. We used F13A, an inhibitor of Angiotensin Receptor-Like 1 (APJ), and Protein Kinase C (PKC) inhibitor chelerythrine to reverse the effects of apelin-13. Moreover, the protein expressions of PKC, Nav1.5, and APJ in ventricular tissues were measured using Western blotting and immunofluorescence assay. Results: Compared to the control rats, the rats subjected to continuous intravenous administration of bupivacaine had impaired hemodynamic stability. Administration of apelin-13, in a dose-dependent manner, significantly improved hemodynamic parameters in rats with bupivacaine-induced cardiac suppression (p < 0.05), and apelin-13 treatment also significantly upregulated the protein expressions of p-PKC and Nav1.5 (p < 0.05), these effects were abrogated by F13A or chelerythrine (p < 0.05). Conclusion: Exogenous apelin-13, at least in part, activates the PKC signaling pathway through the apelin/APJ system to improve cardiac function in a rat model of bupivacaine-induced cardiac suppression.

Abstract In hybrid welding, due to the large number of welding parameters and the coupling between different welding parameters, any change of welding parameters will have a significant impact on the weld section size, so it is always essential to choose reasonable welding parameters to improve the stability of the weld. In this paper, an automatic measurement system for weld section size is designed. The center point of the weld section contour is taken as the origin of the polar coordinate system, and the pixel coordinates of the boundary points on the weld section contour are detected every 15 degrees. Based on 32 groups of laser-arc hybrid welding experiments, the BP (Back Propagation) neural network is used to establish the prediction model between the input parameters (welding current, laser power, welding Angle, welding gap, and weld blunt) and the output weld section size. Aiming to the problem of too many input and output parameters of the BP neural network, a multiple population genetic algorithm (MPGA) is introduced to optimize the internal weights and thresholds of the BP neural network to improve the prediction accuracy. Finally, the difference between the dimensions of the left and right sides of the weld is calculated as the symmetry of the weld profile. The results show that the measurement accuracy of the automatic measurement system can reach 98%, and the prediction accuracy of the section size and symmetry can reach about 90% with the optimized BP neural network. The research method in this paper is of great significance to the study of welding process parameter optimization.

Abstract This article presents an empirical methodology for analyzing the propagation of aggregate shocks across regions. While focusing on the specific case of Brazil and Ceará, the proposed methodology can be easily adapted to examine other regions as well. We develop a dynamic model, termed SAMBA+REG, which complements the Central Bank of Brazil’s dynamic stochastic general equilibrium model (SAMBA) by incorporating regional elements. This theoretical framework allows us to explore how aggregate shocks impact a specific region. We conduct simulations using fiscal and monetary shocks and the results align with the existing literature. SAMBA+REG not only facilitates the evaluation of the effects of central government economic policies on a regional economy, but also offers insights into various strategies for state governments to respond effectively to these policies.

Resumo Este artigo apresenta uma metodologia empírica para analisar a propagação de choques agregados entre regiões. Embora focando especificamente no caso do Brasil e do Ceará, a metodologia proposta pode ser facilmente adaptada para examinar outras regiões também. Desenvolvemos um modelo dinâmico, chamado SAMBA+REG, que complementa o modelo DSGE do Banco Central do Brasil (SAMBA) ao incorporar elementos regionais. Esse arcabouço teórico nos permite explorar como choques agregados impactam uma região específica. Realizamos simulações de choques fiscais e monetários, e os resultados estão alinhados com a literatura existente. O SAMBA+REG não apenas facilita a avaliação dos efeitos das políticas econômicas do governo central em uma economia regional, mas também oferece insights sobre várias estratégias para os governos estaduais responderem de forma eficaz a essas políticas.