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AbstractLandmines significantly hinder the rapid movement of military forces and pose major obstacles during critical operations. Unmanned aerial vehicles (UAVs), commonly known as drones, offer several significant advantages for landmine detection. Most studies have tested drone-based systems at controlled sites and demonstrated their effectiveness in limited scenarios, with no examples of using these systems to detect landmines and relay the results to mine clearance machines (MCMs) for actual removal in real military operations. This study evaluated the use of UAVs equipped with magnetometers to detect metallic landmines in military applications. By conducting a series of controlled experiments, the research identified optimal flight conditions—2 m/s flight speed, 1 m survey interval, and 0.5 m sensor altitude—that balance accuracy and operational efficiency. The findings demonstrate that UAV-based magnetometer systems can significantly enhance mine clearance operations by providing near real-time data to MCMs. This approach offers a safer, faster, and more cost-effective alternative to traditional landmine detection methods by addressing the limitations of ground-based operations, such as high risk to human operators and inefficiency. [ABSTRACT FROM AUTHOR]

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experience of the appraisers or the various measuring instruments, and the methods are either easily influenced by appraisers' experience or require too much work. To solve the above problems, there are studies using image recognition and intelligent algorithms to identify minerals. However, current studies cannot identify many minerals, and the accuracy is low. To increase the number of identified minerals and accuracy, we propose a method that uses both mineral photo images and the Mohs hardness in deep neural networks to identify the minerals. The experimental results showed that the method can reach 90.6% top-1 accuracy and 99.6% top-5 accuracy for 36 common minerals. An app based on the model was implemented on smartphones with no need for accessing the internet and communication signals. Tested on 73 real mineral samples, the app achieved top-1 accuracy of 89% when the mineral image and hardness are both used and 71.2% when only the mineral image is used. [ABSTRACT FROM AUTHOR]

Deep and even ultra-deep petroleum resources play a gradually increasing and important role with the worldwide continuous advancement of oil and gas exploration and development. In China, the deep carbonate reservoirs in the Tarim Basin are regarded as the key development areas due to their huge reserves. However, due to the unreasonable design of production pressure differential, some production wells suffered from severe borehole collapse and tubing blockage. Therefore, the main purpose of this paper is to optimize a more practical method for predicting the critical production pressure differential. The commonly used analytical methods with different failure criteria for predicting production pressure differential were summarized. Furthermore, their advantages and disadvantages were analyzed. A new numerical model is established based on the finite element theory in order to make the prediction of production pressure differential more accurate. Additionally, both analytical and numerical methods were applied to evaluating the production pressure differential of deep carbonate reservoirs in the Tarim Basin, and the results were discussed compared with field data. In addition, a series of laboratory tests, including porosity and permeability measurements, electron microscope scanning, XRD for mineral analysis, uniaxial and triaxial compressive strength test, etc., were carried out by using the collected carbonate cores from formations deeper than 7000 m to obtain the input parameters of the simulation such as the rock properties. The experimental results showed that the carbonate rocks exhibited a remarkable brittleness and post-peak strain softening. The calculation results revealed that the Mogi-Coulomb criterion is slightly conservative; however, it is more suitable than other criteria to evaluate pressure differential. Furthermore, it has been confirmed by the field data that the finite element numerical method can not only reveal the instability mechanism of the wellbore but also predict the critical production pressure differential accurately. Unfortunately, the on-site operators sometimes require a more convenient way, such as an analytical method, to figure out the pressure differential, even though the evaluation of the numerical method is more accurate. Therefore, the discussion in this paper can provide a basis for the operators to determine the production pressure differential flexibly. [ABSTRACT FROM AUTHOR]

In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation. [ABSTRACT FROM AUTHOR]

An interest in floating photovoltaic (PV) is growing drastically worldwide. To evaluate the feasibility of floating PV projects, an accurate estimation of electric power output (EPO) is a crucial first step. This study estimates the EPO of a floating PV system and compares it with the actual EPO observed at the Hapcheon Dam, Korea. Typical meteorological year data and system design parameters were entered into System Advisor Model (SAM) software to estimate the hourly and monthly EPOs. The monthly estimated EPOs were lower than the monthly observed EPOs. This result is ascribed to the cooling effect of the water environment on the floating PV module, which makes the floating PV efficiency higher than overland PV efficiency. Unfortunately, most commercial PV software, including the SAM, was unable to consider this effect in estimating EPO. The error results showed it was possible to estimate the monthly EPOs with an error of less than 15% (simply by simulation) and 9% (when considering the cooling effect: 110% of the estimated monthly EPOs). This indicates that the approach of using empirical results can provide more reliable estimation of EPO in the feasibility assessment stage of floating PV projects. Furthermore, it is necessary to develop simulation software dedicated to the floating PV system. [ABSTRACT FROM AUTHOR]

Smartphones have been drawing attention with respect to scientific uses; the scope of their applicability has rapidly expanded in recent years. Many smartphone applications (apps) have been developed in the realm of geosciences for purposes of collecting, storing, analyzing, and visualizing various sets of information and data. In this study, a large number of commercial applications available in the field of geosciences were investigated and the areas of use and major features of those applications were examined. The results revealed many cases in which the existing recording, analysis, and measurement methodologies and tools could be improved to a certain extent or replaced by utilizing the device's computing capacity, mobility, sensors, and other strengths. Commercially available applications have been developed for various purposes and a number of different disciplines. Although a great number of apps have been developed for mere information conveyance, computing, and other similar purposes, the results also identified many apps that can be used for complex purposes, such as sensor-based measurements, analysis, and field survey execution. The background of such app development and implementation is characterized by many limitations with respect to hard- and software aspects of smartphones, reflecting the characteristics of mobile-devices and differences that set them apart from PCs. Despite the weaknesses, both hard- and software aspects of smartphones are rapidly advancing, resulting in increasingly more variations in the type; even more applications are anticipated to be implemented in the future. [ABSTRACT FROM AUTHOR]

In this study, the objective was to develop a new and highly-accurate artificial intelligence model for slope failure prediction in open-pit mines. For this purpose, the M5Rules algorithm was combined with a genetic algorithm (GA) in a novel hybrid technique, named M5Rules–GA model, for slope stability estimation and analysis and 450-slope observations in an open-pit mine in Vietnam were modeled using the Geo-Studio software based on essential parameters. The factor of safety was used as the model outcome. Artificial neural networks (ANN), support vector regression (SVR), and previously introduced models (such as FFA-SVR, ANN-PSO, ANN-ICA, ANN-GA, and ANN-ABC) were also developed for evaluating the proposed M5Rules–GA model. The evaluation of the model performance involved applying and computing the determination coefficient, variance account for, and root mean square error, as well as a general ranking and color scale. The results confirmed that the proposed M5Rules–GA model is a robust tool for analyzing slope stability. The other investigated models yielded less robust performance under the evaluation metrics. [ABSTRACT FROM AUTHOR]

Smartphones can be utilized in the field of geosciences for various purposes due to their multifaceted abilities that combine both hard- and software features. The unique abilities of smartphones allow new methodologies for the collection and visualization of data that rarely become available in traditional computing platforms. In this study, commercially available smartphone applications (apps) that have been released in geoscience (e.g., geology/soil, minerals and rocks, petroleum and gas) so far were investigated. The apps were categorized based on the extent of smartphone feature usage into (a) basic, standard-feature apps; (b) calculator and referencing apps; and (c) sensing and communication apps. Furthermore, each of these categories was divided into several app groups based on specific features. Representative apps of each specific app group were selected and their characteristics and applicability were examined. Lastly, major limitations regarding smartphone app development and implementation in geoscience and implications for future improvements were discussed. [ABSTRACT FROM AUTHOR]

This paper presents the design calculations, implementations, and multi-engineering based computational constructions of an unmanned amphibious vehicle (UAmV) which efficiently travels underwater to detect and collect deep-sea minerals for investigations, as well as creative usage purposes. The UAmV is expected to operate at a 300 m depth from the water surface. The UAmV is deployed above the water surface near to the approximate target location and swims underwater, checking the presence of various mining, then extracts them using a unique mechanism and stores them in an inimitable fuselage location. Since this proposed UAmV survives in deep-sea regions, the design construction of this UAmV is inspired by hydrodynamic efficient design-based fish, i.e., Rhinaancylostoma. Additionally, standard analytical approaches are followed and, subsequently, the inimitable components such as wing, stabilizers, propellers, and mining storage focused fuselage are calculated. The computational analyses such as hydrodynamic investigations and vibrational investigations were carried out with the help of ANSYS Workbench. The hydrodynamic pressures at various deployment regions were estimated and thereafter the vibrational outcomes of UAmVs were captured for various lightweight materials. The computed outcomes were imposed in the analytical approach and thereby the electrical energy generations by the UAmV's components were calculated. Finally, the hydrodynamic efficient design and best material were picked, which provided a path to further works on the execution of the focused mission. Based on the low drag generating design profile and high electrical energy induction factors, the optimizations were executed on this work, and thus the needful, as well as suitable UAmV, was finalized for targeted real-time applications. [ABSTRACT FROM AUTHOR]

Collocated cokriging (CCK) and neural-network multi-attribute transform (NN-MAT) are widely used in the prediction of reservoir properties because they can integrate sparsely-distributed, high-resolution well-log data and densely-sampled, low-resolution seismic data. CCK is a linear-weighted averaging method based on spatial covariance model. NN-MAT, based on a nonlinear relationship between seismic attributes and log values, treats data as spatially independent observations. In this study, we analyzed 3-D seismic and well-log data from the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA to investigate: (1) how CCK and NN-MAT perform in the prediction of porosity and (2) how the number of wells affects the results. Among a total of 64 wells, 25 wells were selected for CCK and NN-MAT and 39 wells were withheld for validation. We examined four cases: 25, 20, 15, and 10 wells. CCK overpredicted the porosity in the validation wells for all cases likely due to the strong influence of high values, but failed to predict very large porosities. Overprediction of CCK porosity becomes more pronounced with decreasing number of wells. NN-MAT largely underpredicted the porosity for all cases probably due to the band-limited nature of seismic data. The performance of CCK appears to be not affected significantly by the number of wells. Overall, NN-MAT performed better than CCK although its performance decreases continuously with decreasing number of wells. [ABSTRACT FROM AUTHOR]

• MLP was applied to predict diaphragm walls deflection induced by braced excavations. • HHO and WOA were applied to optimize the MLP model. • HHO-MLP and WOA-MLP were proposed as the novel and robust models for this aim. • HHO-MLP model yielded the most dominant performance in this study. • SSR, STR, ED, and WS are the most important input parameters in this study. The construction of metropolises in smart cities is the trend of developed countries. However, it may cause damages to the surrounding structures. For this reason, the diaphragm wall has been applied to prevent the deformation or collapse of the surrounding structures. Diaphragm walls can be deflected due to the swelling pressure or other geotechnical properties during construction. Therefore, the accurate prediction of diaphragm wall deflection (DWD) is challenging in construction aiming to ensure the safety of the surrounding structures. This study is, therefore, to propose two intelligent models for predicting DWD induced by deep braced excavations based on finite element method (FEM) and metaheuristic algorithms. Accordingly, a total of 1120 finite elements were analyzed to investigate the behaviors of DWD. Finally, a neural network with multiple layer perceptron (MLP) was optimized by two metaheuristic algorithms for predicting DWD, including whale optimization (WO) and Harris hawks optimization (HHO), called MLP-HHO and MLP-WO, respectively. The results indicated that the proposed MLP-HHO and MLP-WO provided high accuracy in predicting DWD. A comparison of the proposed models in this study and previous studies was also discussed to highlight the superiority of the proposed MLP-HHO and MLP-WO models. [Display omitted] [ABSTRACT FROM AUTHOR]

This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by an Internet of Things system. A limestone underground mine with an applied mine production management system (using a tablet computer and Bluetooth beacon) is selected as the research area, and log data related to the truck travel time are collected. The machine learning models are trained and verified using the collected data, and grid search through 5-fold cross-validation is performed to improve the prediction accuracy of the models. The accuracy of CART is highest when the parameters leaf and split are set to 1 and 4, respectively (94.1%). In the validation of the machine learning models performed using the validation dataset (1500), the accuracy of the CART was 94.6%, and the precision and recall were 93.5% and 95.7%, respectively. In addition, it is confirmed that the F1 score reaches values as high as 94.6%. Through field application and analysis, it is confirmed that the proposed CART model can be utilized as a tool for monitoring and diagnosing the status of truck ore transport operations. [ABSTRACT FROM AUTHOR]

Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal's sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions. Therefore, this study implicates the feasibility of biochar systems in the heavy metal sorption in water/wastewater and the use of artificial intelligence (AI) models in investigating efficiency sorption of heavy metal on biochar. Accordingly, this work investigated and proposed 20 artificial intelligent models for forecasting the sorption efficiency of heavy metal onto biochar based on five machine learning algorithms and bagging technique (BA). Accordingly, support vector machine (SVM), random forest (RF), artificial neural network (ANN), M5Tree, and Gaussian process (GP) algorithms were used as the key algorithms for the aim of this study. Subsequently, the individual models were bagged with each other to generate new ensemble models. Finally, 20 intelligent models were developed and evaluated, including SVM, RF, M5Tree, GP, ANN, BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN. Of those, the hybrid models (i.e., BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN) are introduced as the novelty of this study for estimating the heavy metal's sorption efficiency on the biochar systems. Also, the biochar characteristics, metal sources, and environmental conditions were comprehensively assessed and used, and they are considered as a novelty of the study as well. For this aim, a dataset of sorption efficiency of heavy metal was collected and processed with 353 experimental tests. Various performance indexes were applied to evaluate the models, such as RMSE, R2, MAE, color intensity, Taylor diagram, box and whiskers plots. This study's findings revealed that AI models could predict heavy metal's sorption efficiency onto biochar with high reliability, and the efficiency of the ensemble models is higher than those of individual models. The results also reported that the SVM-ANN ensemble model is the most superior model among 20 developed models. The predictive model proposed that heavy metal's efficiency sorption on biochar can be accurately forecasted and early warning for the water pollution by heavy metal. [Display omitted] • Biochar characteristics have significant effects on the SEoHM of biochar system. • Metal sources, environmental conditions were investigated for the SEoHM. • 15 novel hybrid models were proposed for predicting the SEoHM of biochar system. • SVM-ANN was proposed as the best model for predicting the SEoHM of biochar system. • Taylor diagram, boxplot and Q-Q plot were used to evaluate the models. [ABSTRACT FROM AUTHOR]

We propose a magnetometer system fitted on an unmanned aerial vehicle (UAV, or drone) and a data-processing method for detecting metal antipersonnel landmines (M16) in the demilitarized zone (DMZ) in Korea, which is an undeveloped natural environment. The performance of the laser altimeter was improved so that the drone could fly at a low and stable altitude, even in a natural environment with dust and bushes, and a magnetometer was installed on a pendulum to minimize the effects of magnetic noise and vibration from the drone. At a flight altitude of 1 m, the criterion for M16 is 5 nT. Simple low-pass filtering eliminates magnetic swing noise due to pendulum motion, and the moving average method eliminates changes related to the heading of the magnetometer. Magnetic exploration was conducted in an actual mine-removal area near the DMZ in Korea, with nine magnetic anomalies of more than 5 nT detected and a variety of metallic substances found within a 1-m radius of each detection site. The proposed UAV-based landmine detection system is expected to reduce risk to detection personnel and shorten the landmine-detection period by providing accurate scientific information about the detection area prior to military landmine-detection efforts. [ABSTRACT FROM AUTHOR]

Recently, collisions between equipment and workers occur frequently on the road in construction and surface mining sites. To prevent such accidents, we developed a smart helmet-based proximity warning system (PWS) that facilitates visual and tactile proximity warnings. In this system, a smart helmet comprising an Arduino Uno board and a camera module with built-in Wi-Fi was used to transmit images captured by the camera to a smartphone via Wi-Fi. When the image was analyzed through object detection and a heavy-duty truck or other vehicle was detected in an image, the smartphone transmitted a signal to the Arduino via Bluetooth and, when a signal was received, an LED strip with a three-color LED visually alerted the worker and the equipment operator. The performance of the system tested the recognition distance of the helmet according to the pixel size of the detected image in an outdoor environment. The proposed personal PWS can directly produce visual proximity warnings to both workers and operators enabling them to quickly identify and evacuate from dangerous situations.

A smart glasses-based wearable personnel proximity warning system (PWS) was developed for pedestrian safety in construction and mining sites. The smart glasses receive signals transmitted by Bluetooth beacons attached to heavy equipment or vehicles, with the proximity determined by the signal strength. A visual alert is displayed to the wearer when in close proximity. The media access control address of the Bluetooth beacon provides information on the approaching equipment or vehicle, which is displayed to the wearer so that they can respond appropriately. There was a detection distance of at least 10 m regardless of the direction the pedestrian was looking and the alert was successful in all 40 trials at ≥10 meters. The subjective workload was evaluated using the NASA task load index on ten subjects, either without a personal PWS, with a smartphone-based PWS, or with the smart glasses-based PWS. The mental, temporal, and physical stresses were lowest when using the smart glasses-based PWS. Smart glasses-based PWSs can improve work efficiency by freeing both hands of the pedestrians, and various functions can be supported through application development. Therefore, they are particularly useful for pedestrian safety in construction and mining sites.

In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R 2 , RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation., Competing Interests: The authors declare no conflict of interest.