Traditional Direct Measurement Research Articles

Assessment of different agricultural soil compaction levels using shallow seismic geophysical methods

A well-developed soil structure is crucial for a fertile soil and rules its influence on the ecosystem. Slight variations in the way soil components are organized and connected can greatly impact the soil mechanical and hydraulic characteristics. However, these features are challenging to measure at a scale that is relevant for agricultural management. In this study, we assess the capability of two seismic geophysical methods, i.e. Seismic Refraction Tomography and Multichannel Analysis of Surface Waves, to monitor the effects of compaction on agricultural bare soil. The purpose is to highlight the different mechanical response caused both by soil plastic deformation and soil water distribution due to increasing compaction. Results demonstrate that both geophysical techniques provide sufficient information to capture the effects of soil compaction and distinguish its significance, while traditional direct measurements, being punctual, lack sufficient spatial coverage. P-wave velocities carry a strong imprint of soil compaction, provided by seismic refraction, incorporating the information given by the multiphase medium in its entirety. On the other hand, S-wave velocity derived from Surface Waves discriminates the effect of solid matrix structure. This work, moreover, aims to pave the way for seismic methods to spatially characterize compaction at field scale, illustrating its potential and suggesting possible developments.

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran.

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

Dynamic identification of coupler yaw angle of heavy haul locomotive: An optimal multi-task ELM-based method

The lateral instability of the couplers seriously threaten the operation safety of 20,000-tonne heavy-haul trains. It is essential to dynamically monitor the coupler yaw angle (CYA) in order to evaluate the operation stability of the train. However, the traditional direct measurement methods for CYA are hindered by the harsh service environment, making it difficult to maintain long-term stability in monitoring. To address these issues, within this paper a multi-task deep multiple kernel extreme learning machine (MT-DMKELM) is developed to quantitatively identify CYA based on the multi-channel relative displacements between the car body and frame (RDCFs). Firstly, the correlation of multi-channel RDCFs is analyzed based on the dynamics model, and the data fusion of strong correlation channels is achieved through an extreme learning machine auto-encoder. Then, a deep network for feature extraction is created by stacking numerous multiple kernel extreme learning machine auto-encoders. Further, the high-level abstract features are input to multiple kernel extreme learning machine for regression estimation of CYA. In addition, the optimal model parameters are obtained using the genetic mutation particle swarm optimization for MT-DMKELM to improve the CYA identification performance. Finally, both dynamics simulation and field test are conducted to confirm the capability of the proposed methods in CYA identification.

A plasmonic MNAzyme signal amplification strategy for quantification of miRNA-4739 breast cancer biomarker

A major challenge in the 21st century is the development of point-of-care diagnostic tools capable to detect and quantify disease biomarkers in a straightforward, affordable, sensitive, and specific manner. The remarkable plasmonic properties of gold nanoparticles (AuNPs) have promoted their use for development of simple methodologies for nucleic acid detection in combination with a variety of oligonucleotides amplification techniques. Here, assemblies of AuNPs with Multicomponent Nucleic Acid enzymes (MNAzymes) has been successfully used in the design of a highly sensitive and simple bioassay for rapid spectroscopic detection and quantification of miRNA-4739 in blood samples. The miRNA selected is a doxorubicin chemoresistant biomarker in breast cancer which overexpression promotes the proliferation, progression, and survival of cancer cells. In this work, two alternatives experimental designs, based on use of MNAzymes and AuNPs, have been optimized and applied for sensitive miRNA-4739 quantification: one based on a traditional direct measurement of wavelength shift and a second non-conventional simple approach based on isolation and measurement of free nanoparticles absorbance. Improvement in sensitivity and, higher measurement accuracy and precision were achieved with the second approach. The developed bioassay provides a detection limit as low as 7 pmolL−1 for miRNA-4739 quantification and performed satisfactory selectivity and well practical applicability by analysis of the miRNA-4739 in blood, demonstrating that the proposed strategy is a promising and suitable spectroscopic method for breast cancer diagnosis thought liquid biopsy of circulating tumoral cells.

The Application of an Inverse Methodology to Predict Nonlinear Thermal Properties in Laser Beam Welding

Modern engineering applications use processes that submit materials to high-temperature gradients. However, the traditional experimentation methods applied to determine thermal properties often do not provide reliable data when working temperatures are up to extreme conditions. Hence, the present work demonstrates the use of an inverse heat conduction problem methodology for estimating the thermal properties of a laser beam welding (LBW) process. The applied technique is the quadrilateral optimization method (QOM), which consists of a multivariable estimation approach developed to calculate the function’s parameters. Additionally, the future time regularization scheme was implemented in the objective function to regularize the results. The applied numerical process solved the energy equation using the finite volume method implemented in an in-house CUDA-C language code. The software is a multi-thread application run in a graphics processing unit for enhanced computational time efficiency. A validation study compared the estimated results with LBW simulated data. The QOM estimates the parameters of a function representing the range of thermal conductivity values. The proposed method is expected to lower experimental costs for obtaining thermal properties at high temperatures by eliminating the need for sophisticated technical equipment and skilled labor required by traditional direct measurements.

Error Calibration for Crosstalk of SQUIDs in Full Tensor Magnetic Gradiometer Based on Moore–Penrose Inverse and Helmholtz Coil

In order to calibrate crosstalk error between superconducting quantum interference device (SQUID) magnetometers in a multichannel SQUID system, we propose an innovative error calibration method for crosstalk of SQUID magnetometers based on the Moore–Penrose inverse method and Helmholtz coil in this article and successfully apply it to an eight-channel SQUID magnetometers based full tensor magnetic gradiometer (FTMG). We first analyze the causes of crosstalk through working principle of the SQUID readout circuit and build the crosstalk error model of the eight-channel FTMG. Then, we introduce the solution method of 56 crosstalk coefficients based on the Moore–Penrose inverse method and the construction method of matrix needed for solution based on Helmholtz coil. The effect of nonuniformity of Helmholtz coil on the calibration of crosstalk is analyzed and calculated. The effectiveness of the proposed method is proved by repetitive simulation and comparative experiments. Compared with the traditional direct measurement method, this method has the advantage of high efficiency, and the more the channels of SQUID, the more obvious is the superiority.

Electro-Magnetic Geophysical Dynamics under Conservation and Conventional Farming

In the context of global warming, agriculture faces severe challenges such as water scarcity and soil erosion. Key to achieving soil sustainability is the choice of farming practices, the consequences of which are generally site-specific. In this study, the ability of Electrical Resistivity Tomography (ERT) and Electro Magnetic Induction (EMI) methods were assessed for monitoring the effects of conventional (CONV) and conservation (CONS) agricultural practices. The aim is to highlight differences in soil water distribution caused by both short- and long-term effects of the two different practices. Results demonstrated that both ERT and EMI provided sufficient information to distinguish between the effects of CONV and CONS, while traditional direct measurements, being punctual techniques, lacked sufficient spatial resolution. The ERT transects showed that the soil was much more homogeneous as a result of CONS practices, resulting in a higher sensitivity to changes in the water content. Conversely, due to the heterogeneous soil structure under CONV, water distribution was more irregular and difficult to predict. Similar patterns were also observed with the EMI surveys, with a strong link to spatial variability. Finally, we conclude that for CONV soil, the accessible water for the plant is clearly controlled by the soil heterogeneities rather than by the forcing atmospheric conditions. This study is a first step towards paving the way for more refined hydrology models to identify which soil parameters are key to controlling spatial and temporal changes in soil water content.

A Novel Estimation Method of Wind Speed and Direction Based on GNSS/INS/ADS Integrated Navigation

Arriving on time is of great importance for flight management and passenger experience. One of the essential factors that impacts on‐time arrival is the wind condition. Accurate information of wind speed and direction around the fuselage helps to improve the performance of on‐time arrival and four‐dimensional trajectory (4DT) planning. To determine accurate wind information in real‐time, a novel airborne estimation method of wind speed and direction is proposed in this paper. Inertial Navigation System (INS), Global Satellite Navigation System (GNSS), and Air Data System (ADS) are fused in an Unscented Kalman Filter (UKF), which provides great accuracy and robustness in nonlinearity conditions. The dynamic models of wind are established, and implementations of the UKF are detailed. Finally, simulations are designed and the effectiveness of the proposed method is verified through the comparison with the traditional direct measurement method. Results demonstrate that the accuracy of wind speed and direction obtained by our method is nearly two times higher than the traditional direct measurement method.

Towards automatic discontinuity trace extraction from rock mass point cloud without triangulation

We propose a new automatic methodology for identifying discontinuity traces directly from 3D point clouds of natural rocky slopes. The potential feature points of the discontinuity traces were detected based on a 1D truncated Fourier series. To extract trace points from the potential feature points, a curvature-weighted Laplacian-like smoothing technique was used to thin these points. Finally, the trace lines were constructed through a feature-parameter-weighted line growing algorithm. The effectiveness of the method was tested with four data sets. Additionally, the results of two of the data sets were compared with the results of CloudCompare and an existing method. Finally, the area density was calculated based on the extracted trace lines. The results show that the proposed discontinuity trace extraction method is fast, effective and automatic and has the potential for use as a supplement to traditional direct measurements of discontinuity traces, thereby providing important supplemental data for fracture related research.

Numerical model for individual time calculation for evacuation among secondary students

PurposeThe purpose of this paper is to design a numerical model to calculate the individual evacuation time among secondary students based on Knowledge, Attitude and Practice (KAP), human characteristics and travel distances.Design/methodology/approachValidated KAP questionnaires were distributed among 290 respondents. The KAP level was obtained based on the assigned scores. During a fire drill, the individual evacuation time was calculated by using personal digital watch while the travel distances were recorded and measured. A linear numerical model was derived by using multiple linear regression to identify the significant variables and the coefficients.FindingsThe CVI, CVR and Cronbach’s α value (0.75, 0.59 and 0.7, respectively) which are greater than minimum accepted level proved the reliability and consistency of the instrument. The evacuation time prediction by the developed numerical model showed strong correlation with the actual time (R=0.95). The regression analysis found that 89 per cent proportion of variance in the evacuation time are determined by the predictors. Based on the linear equation, it found that the decrease in weight, knowledge level and walking speed while increase in BMI, flat and stair travel distances could increase evacuation time. From the six significant variables, weight, walking speed, flat and stair distances showed significant correlation in the model with p<0.001, while BMI and knowledge showed p<0.05. The integration with mobility factors expand the formula which applicable within dynamic fire scenario.Research limitations/implicationsThe involvement of examination students in the study is restricted by the Ministry of Education Malaysia to avoid interruption of learning session which limited the data representation.Originality/valueInstead of using the traditional direct measurement of the evacuation time, the developed numerical model is an alternative convenient approach which could be used as one of the pre-assessment tool to identify the level of safety among students. The low cost and shorter time application of this model become one of the greatest advantages compared to other available approaches. The calculated individual evacuation time could be used directly to develop a better fire safety policy.

Study on the causes and methods of influencing concrete deflection

Under the long-term effect of static load on reinforced concrete beam, the stiffness decreases and the deformation increases with time. Therefore, the calculation of deflection is more complicated. According to the domestic and foreign research results by experiment the flexural deflection of reinforced concrete, creep, age, the thickness of the protective layer, the relative slip, the combination of steel yielding factors of reinforced concrete deflection are summarized, analyzed the advantages and disadvantages of the traditional direct measurement of deflection, that by increasing the beam height, increasing the moment of inertia, ncrease prestressed reinforcement ratio, arching, reduce the load, and other measures to reduce the deflection of prestressed construction, improve the reliability of structure.

Image Processing Based Leaf Rot Disease, Detection of Betel Vine (Piper BetleL.)

This paper deals with leaf rot disease detection for betel vine (Piper betel L.) based on image processing algorithm. The measurement of plant features is a fundamental element of plant science research and related applications. The information related to plant features is especially useful for its applications in plant growth modeling, agricultural research and on farm production. Few methods have been applied in leaf rot disease detection for betel vine leaf (Piper Betel L.). Traditional direct measurement methods are generally simple and reliable, but they are time consuming, laborious and cumbersome. In contrast, the proposed vision-based methods are efficient in detecting and observing the exterior disease features. In the present investigation, image processing algorithms are developed to detect leaf rot disease by identifying the color featureof the rotted leaf area. Subsequently, the rotted area was segmented and area of rotted leaf portion was deduced from the observed plant feature data. The results showed a promising performance of this automatic vision-based system in practice with easy validation. This paper describes the steps to achieve an efficient and inexpensive system acceptable to the farmers and agricultural researchers as well for studying leaf rot disease in betel vine leaf.

A new method for automated discontinuity trace mapping on rock mass 3D surface model

This paper presents an automated discontinuity trace mapping method on a 3D surface model of rock mass. Feature points of discontinuity traces are first detected using the Normal Tensor Voting Theory, which is robust to noisy point cloud data. Discontinuity traces are then extracted from feature points in four steps: (1) trace feature point grouping, (2) trace segment growth, (3) trace segment connection, and (4) redundant trace segment removal. A sensitivity analysis is conducted to identify optimal values for the parameters used in the proposed method. The optimal triangular mesh element size is between 5cm and 6cm; the angle threshold in the trace segment growth step is between 70° and 90°; the angle threshold in the trace segment connection step is between 50° and 70°, and the distance threshold should be at least 15 times the mean triangular mesh element size. The method is applied to the excavation face trace mapping of a drill-and-blast tunnel. The results show that the proposed discontinuity trace mapping method is fast and effective and could be used as a supplement to traditional direct measurement of discontinuity traces.

Analysis of the Surface Shape Effect on Optical Homogeneity Measurement of Large Calibre Optical Materials

Optical homogeneity is an important measurement of the optical materials, directly affects wavefront quality of the refractive optical system, changes the system wavefront aberration. As the traditional direct measurement method requires high precision surface shape machining for optical material (PV value &lt; 0.1λ), it was gradually replaced by the method of Fizeau interferometer four step, which requires lower surface shape. This article used 600mm diameter Fizeau interferometer, a four step method tested the optical homogeneity of the same 500 mm diameter fused quartz material with a front surface shape respectively 3.719 λ and 0.427 λ (λ = 632.8nm). Comparison of the test results showed that the PV difference of optical homogeneity between the two was only 0.036 λ, △n inaccuracy was 10-7, and the homogeneity morphology matched exactly. To further analyze the factors affecting the optical homogeneity, the front surface shape of the other sample was sliced ​​in a predetermined direction. This artical analyzed the effect of morphology upon the slicing line on optical homogeneity. The conclusion was that the direct affecting factor on optical homogeneity was the slope of surface morphology, rather than the overall PV value.

An Automatic Plant Growth Measurement System for Plant Factory

Plant factories are closed cultivation spaces where environmental conditions are controllable. In order to find the most suitable environmental conditions for plant growth, plant features can provide a good indication. Traditional direct measurement methods are simple but destructive and laborious. In this research, an automatic plant growth measurement system, which is composed of a stereo vision system and a weight measurement system, was developed. The growth of Boston lettuces were monitored and measured with our automatic system. The camera mounted on a sliding rail was moved by a linear actuator to extend the field of view of the planting bed. Using image processing methods and stereo vision techniques, panoramic images of the plating bed were constructed and plant geometric features such as projected leaf area, maximum and minimum radii and plant volume were calculated. At the same time, plant weights were continuously recorded by the weight measurement instrument. Both the vision-based system and weight measurement system are based on non-destructive methods, so the plants features can be monitored throughout the whole growth cycle without affecting its growth. The plant growth measurement system developed in this research can obtain a large amount of plant features easily comparing to traditional destructive measurement methods. Besides, the relationship between the projected leaf area and the fresh weight of Boston lettuces was also discussed in this research. The results have proved the feasibility and functionality of this integrated system. This system would provide valuable information for plant factory managements and agricultural research applications.

지상라이다를 이용한 암반사면 불연속면거칠기에 대한 확률특성 분석

Joint roughness is one of the most important parameters in analysis of rock slope stability. Especially in probabilistic analysis, the random properties of joint roughness influence the probability of slope failure. Therefore, a large dataset on joint roughness is required for the probabilistic analysis but the traditional direct measurement of roughness in the field has some limitations. Terrestrial LiDAR has advantagess over traditional direct measurement in terms of cost and time. JRC (Joint Roughness Coefficient) was calculated from statistical parameters which are known from quantitative methods of converting the roughness of the material surface into JRC. The mean, standard deviation and distribution function of JRC were obtained, and we found that LiDAR is useful in obtaining large dataset for random variables.

Computed tomography measurement of the auricle in Han population of north China

A large number of anthropometric studies of the human auricle have been conducted in various populations. However, data from Han Chinese population are currently not available. The morphology of the auricle is highly complex. Consequently, traditional direct measurement techniques are unable to provide sufficient detail and accuracy. The present study sought to provide anthropometrical data on the auricles of Han Chinese individuals using a three-dimensional (3D) computed tomography (CT) reconstruction technique. A total of 485 data sets were utilised for this study. This sample consisted of data from 241 females aged 18-75 years and 244 males aged 18-74 years. All participants were categorised by gender and further into three age groups. Twelve anthropometrical dimensions of normal auricles were measured with 3D CT. Auricular, tragal, lobular and conchal indices were calculated to further examine the auricles. Earlobes were classified into three basic groups according to the angle of the junction of the earlobe with the cheek. Few of the anthropometrical data showed significant bilateral differences in auricular dimensions. The height of the tragus, lobular length, lobular width, conchal width, protrusion at tragal level and inclination angle of auricles showed no significant differences between males and females. All the linear dimensions of auricles, except the height of the tragus, conchal length and width, increased with age for both males and females. Protrusion at the superaurale and tragal levels showed a decrease of between 4% and 9% for males and 4% and 10% for females across all age groups. This study is the first to report CT measurement of normal auricles in a northern Han Chinese population. We found that this method enabled the complexity of the ear to be measured easily and accurately. Increasing our knowledge of normal auricular dimensions will be beneficial for plastic surgeons.

Automated anthropometric data collection from three-dimensional digital human models

This paper presents the development of an automated anthropometric data-collection system for digital human models obtained from a three-dimensional whole-body scanner. Thirty-seven landmarks were specified for feature recognition and data collection. Both color information and geometric relations were used to develop the algorithms for identifying the landmarks. The proposed landmark identification method has been tested on 105 human models. The recognition rate was over 98%. The experimental results indicate that the proposed method was very efficient and effective in identifying the landmarks. Furthermore, 103 anthropometric dimensions can be extracted from a digital human model. Comparing the data obtained from the proposed system with those obtained by the traditional direct measurement method, the system is fast, accurate, and consistent. Moreover, based on the collected body dimensions, clothing patterns can be directly generated in CAD file format, and textile cutting can be subsequently executed by a CNC laser cuter. An automated tailoring system can thus be developed. The concept of mass customization for the apparel industry can thus be realized.

Erosion rates evaluated by the 137Cs technique and direct measurements on long-term runoff plots under tropical conditions

Measured 137Cs losses (Bq m −2) from long-term runoff plots under tropical conditions and correspondent estimated soil erosion rates (Mg ha −1 year −1) were significantly correlated to directly measured soil losses of the same plots, during the same period (1963–2002). A tendency of higher 137Cs activities was observed in soil profiles of the bottom third part of runoff slopes that could be explained by the restricting effect of the collector system on the runoff flow or by tillage translocation. Despite of the very low 137Cs activity found in the soil of the plots, the isotopic technique yielded confident results, comparable to those obtained by the traditional direct measurements.

Effect of brain edema on infarct volume in a focal cerebral ischemia model in rats.

Infarct volume is one of the common indexes for assessing the extent of ischemic brain injury following focal cerebral ischemia. Accuracy in the measurement of infarct volume is compounded by postischemic brain edema that may increase brain volume in the infarcted region. We evaluated the effect of brain edema on infarct volume determined by triphenyltetrazolium chloride and hematoxylin and eosin stains in a focal cerebral ischemia model in rats. In a middle cerebral artery occlusion model in rats, infarction is confined to the cerebral cortex. The infarct was delineated by triphenyltetrazolium chloride stain and, in selected samples, by hematoxylin and eosin stain. We determined infarct size at different times after the ischemic insult (6 hours to 7 days) in relation to the evolution of brain edema by the direct measurement of infarct volume. Indirect measurement to reduce the effect of edema on infarct volume was also conducted in the same brain samples. Direct measurement showed that infarct volume fluctuated with the evolution of brain edema (one-way analysis of variance, p < 0.0001). Infarct volume determined by indirect measurement was independent of the extent of brain edema and remained stable from 6 hours to 3 days after ischemia. There was a good correlation between triphenyltetrazolium chloride and hematoxylin and eosin stains in delineating infarct volume with both direct and indirect measurement. Traditional direct measurement of infarct volume is associated with an overestimation of infarct volume during the development of brain edema in the first 3 days after ischemia. This artifact can be reduced with indirect measurement, which is based on noninfarcted cortex volume.