Field Method Research Articles

Optimization of the Raman spectroscopic method for crystal orientation and residual stress of aluminum oxide

A half-wavelength plate was set in the common light path to simultaneously alter the polarization directions of both incident laser and scattered Raman signals, and the distribution functions of the polarized Raman scattering intensity about the polarization rotation angle and the crystal orientation angle were deduced. The measurement accuracies of crystal orientations when using different correction methods of light intensity were compared, and on that basis, a measurement system and a measurement method for the crystal orientation of 95% aluminum oxide (Al2O3) were established, which improved the accuracy and handleability of crystal orientation detection. Then, the number of Raman peak shifts needed for solving equations was reduced to three by studying the simplification method of stress fields, exploring the Raman selection method, and introducing the differential evolution algorithm. In this way, the method that can calculate the residual stress in the absence of peaks in Raman spectroscopic tests was proposed. The established test system and method were also adopted to test single-crystal and polycrystal Al2O3 samples with known stress. Results show that the established stress measurement method is highly reliable and accurate. Finally, the residual stress distribution in the ceramic side of 95%Al2O3/4J33 brazed joints was also tested.

Analysis of NBA player salary based on multiple linear regression model

Abstract. This study explores the use of machine learning techniques to predict NBA player salaries. Traditional salary evaluation methods often rely on subjective expert judgment, whereas data-driven approaches can provide more objective and accurate predictions. With the advancement of data analysis techniques and machine learning algorithms, it is now feasible to predict player salaries based on performance data. This study employs advanced statistical and machine learning techniques to analyze detailed player performance data, including points scored, rebounds, assists, steals, and blocks, to establish a data- and algorithm-based salary prediction model. This model can assist team management in making more scientific decisions during contract negotiations and player acquisitions, thereby avoiding the overvaluation or undervaluation of players and achieving a more balanced and fair salary distribution. Accurate salary predictions help teams allocate their limited salary cap more effectively, optimizing budget management and enhancing overall team competitiveness. This study not only demonstrates the practical value of data analysis and machine learning methods in the sports field but also promotes the further development of data science in sports management. Additionally, the results of the prediction model can provide valuable references for fans and media, enhancing their understanding of player salaries and team management strategies. This transparency enriches the overall fan experience and media coverage of the sport, facilitating more informed discussions and debates about player value and team decisions.

Identification and prevention of gas source in goaf: Stable carbon hydrogen isotope tracer method and numerical simulation of gas flow field

Identification and prevention of gas source in goaf: Stable carbon hydrogen isotope tracer method and numerical simulation of gas flow field

EEG Emotion Recognition Based on GADF and AMB‐CNN Model

ABSTRACTDeep learning has achieved better results in natural language processing, computer vision, and other fields. Nowadays, more deep learning algorithms have also been applied in brain‐based emotion recognition. In the studies on brain‐based emotion recognition, deep learning models typically use one‐dimensional time series as the input and cannot fully leverage the advantages of the models in image classification or recognition. To address this issue, based on the publicly available SEED and DEAP datasets, the Gramian angular difference field (GADF) method was proposed to construct two‐dimensional image representation datasets: SEED‐GADF and DEAP‐GADF datasets, in the paper. Additionally, a convolutional attention mechanism model (AMB‐CNN) was introduced and its classification performance was validated on SEED‐GADF and DEAP‐GADF datasets. AMB‐CNN achieved an average accuracy of 90.8%, a recall rate of 90%, and AUC of 96.86% on SEED‐GADF. On DEAP‐GADF, the average accuracy, recall rate, and AUC respectively reached 96.06%, 96.06%, and 98.58% in the valence dimension and 96.11%, 96.11%, and 98.73% in the arousal dimension. Finally, the comparison results with various algorithms and ablation experiments proved the superiority of the proposed model.

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Phase Field Simulation Study of the β′ Precipitation Phase of Mg-Gd-Y Alloys

Abstract The key reinforcing precipitation phase in the aging precipitation sequence of Mg-Gd-Y alloys is the bamboo-leaf-like substable β′ phase, which can effectively inhibit the crystal base slip to enhance the age-hardening response of magnesium alloys. In this work, a phase field model of the aging precipitation process of Mg-Gd-Y ternary alloys was created by combining the phase field method and thermodynamic database. The microstructure evolution of the β′ precipitated phase in the isothermal aging process of Mg-Gd-Y alloys was simulated, and the impact of anisotropic parameters on the precipitated phase’s morphology was examined. Finally, the effect of Gd content on the β′ phase in the Mg-Gd-Y alloys was disclosed from the standpoint of the kinetics. β′ precipitation phase precipitation mechanism of Mg-Gd-Y alloys from the kinetic point of view. The results show that the anisotropic elastic energy and the interfacial energy work together to influence the shape of the β′ precipitation phase. Gd content increases lead to refined grains, an increase in the volume fraction of the Mg-Gd-Y alloy β′ precipitated phase, and a greater aspect ratio of the precipitated phase.

Bottom Stress and Drag on a Shallow Coral Reef

AbstractCoral reef roughness produces turbulent boundary layers and bottom stresses that are important for reef metabolism monitoring and reef circulation modeling. However, there is some uncertainty as to whether field methods for estimating bottom stress are applicable in shallow canopy environments as found on coral reefs. Friction velocities () and drag coefficients () were estimated using five independent methods and compared across 14 sites on a shallow forereef (2–9 m deep) in Palau with large and spatially variable coral roughness elements (0.4–1 m tall). The methods included the following: (a) momentum balance closure, (b) log‐fitting to velocity profiles, (c) Reynolds stresses, (d) turbulence dissipation, and (e) roughness characterization from digital elevation models (DEMs). Both velocity profiles and point turbulence measurements indicated good agreement with log‐layer scaling, suggesting that measurements were taken within a well‐developed turbulent boundary layer and that canopy effects were minimal. However, estimated from the DEMs, momentum budget and log‐profile fitting were consistently larger than those estimated from direct turbulence measurements. Near‐bed Reynolds stresses only contributed about 1/3 of the total bottom stress and drag produced by the reef. Thus, effects of topographical heterogeneity that induce mean velocity fluxes, dispersive stresses, and form drag are expected to be important. This decoupling of total drag and local turbulence implies that both rates of mass transfer as well as values of fluxes inferred from concentration measurements may be proportional to smaller, turbulence‐derived values of rather than to those based on larger‐scale flow structure.

Atomistic-informed phase field modeling of magnesium twin growth by disconnections

The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. Alternatively, mesoscale modeling approaches (such as the phase field method) address these constraints of atomistic simulations and can maintain atomic-level accuracy when integrated with atomic-level information. In this work, a phase field model is used to simulate the disconnection-mediated twinning, informed by molecular dynamics (MD) simulations. This work considers the specific case of the growth of {101¯2} twin in magnesium. MD simulations are first conducted to obtain the orientation-dependent interface mobility and motion threshold, and to simulate twin embryo growth and collect facet velocities, which can be used for calibrating the continuum model. The phase field disconnections model, based on the principle of minimum dissipation potential, provides the theoretical framework. This model incorporates a nonconvex grain boundary energy, elasticity and shear coupling, and simulates disconnections as a natural emergence under the elastic driving force. The phase field model is further optimized by including the anisotropic interface mobility and motion threshold suggested by MD simulations. Results agree with MD simulations of twin embryo growth in the aspects of final twin thickness, twin shape, and twin size, as well as the kinetic behavior of twin boundaries and twin tips. The simulated twin microstructure is also consistent with experimental observations, demonstrating the fidelity of the model.

Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS’s superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings.

Water invasion and residual gas distribution in partially filled fractures via phase-field method

Water invasion is a significant factor affecting the conductivity of fractures in coal seams. The partially contact characteristics of deep coal seam fractures are pronounced, and surface wettability varies significantly. However, there is a limited understanding of how water invasion behavior in partially filled fractures affects the gas produced by these fractures. In this study, a high-temperature and high-pressure contact angle testing device was employed to assess the wettability of coal seams under in situ conditions. The geometry of partially filled fractures was reconstructed using random functions, while the phase field method was employed to calculate the interactions at the two-phase interface during water invasion. The results indicate that the deep coal seams in the Ordos Basin demonstrate weak air-wetting properties under in situ conditions. The partially contact characteristics of the filled fractures in the deep coal seams categorize the fractures into distinct pore and throat regions. The variations in connectivity levels lead to the gas exhibiting uninvaded, clustered, and fully invaded characteristics following water invasion. The change in gas saturation during water invasion is more sensitive to larger values of lgCa and higher cos(θ). A larger displacement pressure difference and a smaller contact angle enable the invasion fluid to penetrate smaller throats, resulting in a higher number of clusters of residual gas and a smaller cluster radius. The results enhance our understanding of water invasion behavior, and the variability of fracture surface properties and gas-water two-phase flow in deep coal seams deserves further investigation.

Quantitative assessment approach of fuel cell operating state of safety based on potential field method

Quantitative assessment approach of fuel cell operating state of safety based on potential field method

Assessment of Slope Stability at Erbil Citadel, Kurdistan Region of Iraq Using Bejerman’s Method

The Erbil Citadel is located in the center of Erbil City; the capital of the Kurdistan Region of Iraq. The shape of the citadel is a large, oval hill. The Citadel was inhabited more than 6,000 years ago and is thought to be one of the longest-continuously inhabited sites in the world. Therefore, the Citadel was announced in 2014 as a World Heritage by UNESCO. The height of the Citadel is not uniform; it ranges between (28–30) m, the top elevation is 429 m (a.s.l.), whereas the bottom elevation ranges between (407– 416) m (a.s.l). The Citadel has an oval shape with diameters between (350 – 420) m, the E–W being the longest. The slopes of the Citadel (Tell) display severe erosion and slope stability problems, the main types are creep, which can endanger the existing historical buildings, especially on the edges of the Citadel. Different data about the Citadel have been reviewed but couldn’t find special studies concerning the slope stability of the Citadel. Therefore, we have applied Bejerman’s method, which is a quick field method to assess the slope stability of the slopes of the Citadel to find the Landslide Possibility Index (LPI). Eleven stations were selected around the Citadel to apply Bejerman’s method, the stations were selected in the site; wherever clear indications were found for slope instability. The results of the assessment showed that the LPI ranges between 18–22, which means grade IV, and Failure possibility of Medium (2 stations), and High (9 stations). Recommendations are given to keep the slopes of the citadel as safe as possible.

Block method of mining quarry fields as a tool to reduce environmental impact in conditions of intensification of production

Relevance. The intensification of open-pit mining in the coal basins of the Russian Federation is accompanied by an increase in negative impact on the environment. Directly in Kuzbass, the environment is exposed to the impact of the coal industry, especially when conducting open-pit mining using deep mining systems, with external dumping, which is accompanied by the withdrawal of a large amount of undisturbed land. The block method of mining quarry fields, well known in mining, has been rarely implemented in existing and planned quarry fields. The general line of its application is the lack of a unified approach to the design of block parameters, including environmental and economic assessment of the impact of this technological solution on the environment. Aim. Reduce environmental payments of mining enterprises with justified parameters of blocks for the development of quarry fields of promising inclined and steeply dipping coal deposits. Methods. Systematic generalization of the actual situation of mining operations in open-pit mines using the block method of mining quarry fields, the method of options and technical and economic modeling using spreadsheets. Results and conclusions. The authors have proposed the ecological and economic criteria for assessing the application of the block method of quarry field development. This allows us to study the patterns of change in environmental indicators. Using the example of the Krasnobrodskoe open-pit mine, the costs of environmental payments for the use of land resources, emissions and dusting are calculated for two options different from the design documentation. Based on the calculation results, the authors proved the efficiency of applying the block procedure for developing promising coal deposits, allowing us to reduce the costs for environmental impact by 22.5 and 63%, respectively, when changing the length of the initial block along the bottom from 2.2 to 1.4 km. From which it follows that the combined ecological and economic indicators allow us to fully assess the feasibility of applying the method for calculating block parameters, which is generally reflected in reduction in the costs for negative impact on the environment.

Analisis SWOT Pentingnya Fasilitas Dan Layanan Lembaga Keuangan Bank Konvensional Di Kecamatan Singojuruh

One sector that can realize regional economic development includes the presence of financial institutions which are often considered the heart and driving force of a country's economy. This can be seen in the lack of banking financial institutions in the Singojuruh District area until now, so that it has an impact on regional income such as handling tax payments, salaries, pensions or other financial transactions related to bank financial institutions carried out in other areas close to Singojuruh District, Banyuwangi Regency. This type of research is descriptive qualitative. The type of method used in this research is field methods (field research). In this study, the data that has been collected is analyzed using the Internal Factor Evaluation (IFE) and (EFE) Matrix. For identification in this research, the SWOT matrix is ​​used internally and externally. The conclusion is to expand a representative banking network, take advantage of regional, land and educational advantages with the availability of mobile banking services, optimize the performance of human resources to speed up the process of banking product services and information and expand the reach of banking services or units.

A Simulation Study on the Effect of Supersonic Ultrasonic Acoustic Streaming on Solidification Dendrite Growth Behavior During Laser Cladding Based on Boundary Coupling

Laser cladding has unique technical advantages, such as precise heat input control, excellent coating properties, and local selective cladding for complex shape parts, which is a vital branch of surface engineering. During the laser cladding process, the parts are subjected to extreme thermal gradients, leading to the formation of micro-defects such as cracks, pores, and segregation. These defects compromise the serviceability of the components. Ultrasonic vibration can produce thermal, mechanical, cavitation, and acoustic flow effects in the melt pool, which can comprehensively affect the formation and evolution for the microstructure of the melt pool and reduce the microscopic defects of the cladding layer. In this paper, the coupling model of temperature and flow field for the laser cladding of 45 steel 316L was established. The transient evolution laws of temperature and flow field under ultrasonic vibration were revealed from a macroscopic point of view. Based on the phase field method, a numerical model of dendrite growth during laser cladding solidification under ultrasonic vibration was established. The mechanism of the effect of ultrasonic vibration on the solidification dendrite growth during laser cladding was revealed on a mesoscopic scale. Based on the microstructure evolution model of the paste region in the scanning direction of the cladding pool, the effects of a static flow field and acoustic flow on dendrite growth were investigated. The results show that the melt flow changes the heat and mass transfer behaviors at the solidification interface, concurrently changing the dendrites’ growth morphology. The acoustic streaming effect increases the flow velocity of the melt pool, which increases the tilt angle of the dendrites to the flow-on side and promotes the growth of secondary dendrite arms on the flow-on side. It improves the solute distribution in the melt pool and reduces elemental segregation.

Integration of geoelectric methods and remote sensing in assessing the environment state in the area of impact of ecologically hazardous objects

Relevance. The need for comprehensive assessment of environmental components in the zones of ecologically hazardous industrial facilities impact, which enables to operatively identify the spatial structure of technogenic pollution. Aim. Chemical pollution assessment of the geological environment in the zones of industrial objects impact, based on the combined use of remote sensing and ground-based geoelectric methods. Objects. Sources of pollution during the production of mineral fertilizers (phosphogypsum dumps, sulfur storage). Methods. The author has developed a complex of methods, consisting of multispectral space imaging (Sentinel-2 satellite images) and ground-based geoelectric researches: water surface resistivity measurements, natural electric field potential imaging, vertical electrical sounding. According to satellite image data (vegetation indices NDVI, NBR, SWVI), technogenic geosystems modifications are distinguished, characterizing different levels of chemical pollution (TM-1, TM-2 and natural geosystem). Within the modification zones, geoelectrical studies are carried out to assess the depth of contamination. Results. In the zone of phosphogypsum dumps impact, two technogenic modifications were distinguished, with a total area of 83.3 hectares, characterized by a statistically significant decrease in the values of vegetation indices compared to the average level. Based on the results of vertical electrical sounding, it was established that the depth of contamination in the TM-1 zone reaches up to 20 m, in the TM-2 zone – up to 2.5 m. The migration of contaminated substances from the dumps occurs with surface drain. In the zone of impact of the sulfur storage, the area of pollution, specified with vegetation indices, covers two hectares. According to the results of the natural electric field method and vertical electrical sounding, the flow of pollution from the sulfur storage site penetrates into groundwater, which is discharged in the TM-1 zone, causing the death of vegetation. In the TM-2 zone, contamination is registered only in the ground aquifer. The developed set of methods enables to quickly and relatively comprehensively diagnose the state of the undergoing technogenic impacts geological environment.

MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9

With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. Existing technologies still face performance bottlenecks when dealing with diverse scenarios. To address these issues, this study proposes a lightweight, cross-dataset enhanced object detection method for the agricultural domain based on YOLOv9, named Multi-Adapt Recognition-YOLOv9 (MAR-YOLOv9). The traditional 32x downsampling Backbone network has been optimized, and a 16x downsampling Backbone network has been innovatively designed. A more streamlined and lightweight Main Neck structure has been introduced, along with innovative methods for feature extraction, up-sampling, and Concat connection. The hybrid connection strategy allows the model to flexibly utilize features from different levels. This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. At the same time, the model size was reduced by 9.3%, and the number of model layers was decreased, reducing computational costs and storage requirements. Additionally, MAR-YOLOv9 demonstrated significant advantages in detecting complex agricultural images, providing an efficient, lightweight, and adaptable solution for object detection tasks in the agricultural field. The curated data and code can be accessed at the following link: https://github.com/YangxuWangamI/MAR-YOLOv9.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Cooperative Containment Strategies to Pursue One Faster Evader with Collision Avoidance

On the surface of complex sea areas with obstacles, the encirclement mission against one hostile unmanned surface vehicle (USV) with high capability is usually extremely challenging. Especially when the evasion USV is endowed with a higher speed, the pursuit USVs are more likely to fail. In this work, the containment strategies by multiple low-speed pursuit USVs to capture one high-speed evasion USV considering collision avoidance are studied. On the basis of the conventional artificial potential field (APF) method, we first design a virtual target point to replace the actual evasion USV, which will help the pursuit USVs better implement the surrounding behavior. Thereafter, we reset the triggering conditions of the APF method to avoid the unnecessary obstacle avoidance actions. The proposed strategies can help the pursuit USVs better accomplish the containment strategies while bypassing the obstacles. Simulation experiments have verified the effectiveness of the designed cooperative containment strategies.

Comparison of microwave and pulsed electric field methods on extracting antioxidant compounds from Arvaneh plant (Hymenocrater platystegius Rech. F)

Improving the quality of extracts derived from medicinal plants is a critical concern due to their extensive use across various industries. The Arvaneh plant (Hymenocrater platystegius Rech. F), a species native to the Khorasan province in Iran and belonging to the mint family, was the focus of this study. We optimized the extraction of Arvaneh plant using both microwave (MW) and pulsed electric field (PEF) techniques. The MW method was tested at different processing times (90, 180, and 270 s) and power levels (180, 540, and 900 W), while the PEF method was evaluated with varying electric field intensities (0.25, 3.25, and 6.25 kV/cm) and pulse numbers (10, 45, and 80) using a central composite design (CCD). The results revealed that extraction efficiency, total phenolic content, and total flavonoid content were significantly higher with the PEF method compared to the MW method (p < 0.05). Moreover, the PEF technique showed superior performance in preserving the antioxidant properties of the extract, as assessed by DPPH and FRAP methods. GC/MS analysis confirmed the presence of 27 bioactive compounds in the Arvaneh extract obtained through PEF-assisted extraction. In conclusion, the PEF method proved to be highly efficient for extracting bioactive compounds from the Arvaneh plant.