Field Data Sets Research Articles

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene.

In order to guide orchard management robots to realize some tasks in orchard production such as autonomic navigation and precision spraying, this research proposed a deep-learning network called dynamic fusion segmentation network (DFSNet). The network contains a local feature aggregation (LFA) layer and a dynamic fusion segmentation architecture. The LFA layer uses the positional encoders for initial transforming embedding, and progressively aggregates local patterns via the multi-stage hierarchy. The fusion segmentation module (Fus-Seg) can format point tags by learning a multi-embedding space, and the generated tags can further mine the point cloud features. At the experimental stage, significant segmentation results of the DFSNet were demonstrated on the dataset of orchard fields, achieving an accuracy rate of 89.43% and an mIoU rate of 74.05%. DFSNet outperforms other semantic segmentation networks, such as PointNet, PointNet++, D-PointNet++, DGCNN, and Point-NN, with improved accuracies over them by 11.73%, 3.76%, 2.36%, and 2.74%, respectively, and improved mIoUs over the these networks by 28.19%, 9.89%, 6.33%, 9.89, and 24.69%, respectively, on the all-scale dataset (simple-scale dataset + complex-scale dataset). The proposed DFSNet can capture more information from orchard scene point clouds and provide more accurate point cloud segmentation results, which are beneficial to the management of orchards.

Research on Wind Speed Prediction Method Based on Feature Fusion

Wind speed occupies a central position in the study of atmospheric circulation and climate change, playing a crucial role in the accuracy and reliability of meteorological forecasting models. The current meteorological observation network, especially in remote and mountainous areas, faces challenges due to insufficient data resulting from low station density. This limitation hampers a comprehensive understanding of wind field dynamics, subsequently affecting the performance of forecasting models. To enhance the performance of meteorological prediction models, this study adopts an innovative approach that combines boxplot difference indexing and feature-level fusion techniques to filter key predictive features and reduce information redundancy. Furthermore, by integrating spatial location information, the contribution of geographic data to wind field prediction is enhanced, improving the quality of the fused data and thereby increasing the accuracy of model predictions. Experimental comparisons demonstrate that the integrated wind field dataset, post feature fusion, achieves better results across various deep learning models.

Generative Adversarial Networks-based solution for improving medical data quality and insufficiency

As big data brings intelligent solutions and innovations to various fields, the goal of this research is to solve the problem of poor-quality and insufficient datasets in the medical field, to help poor areas can access to high quality and rich medical datasets as well. This study focuses on solving the current problem by utilizing variants of generative adversarial network, Super Resolution Generative Adversarial Network (SRGAN) and Deep Convolutional Generative Adversarial Network (DCGAN). In this study, OpenCV is employed to introduce fuzziness to the Brain Tumor MRI Dataset, resulting in a blurred dataset. Subsequently, the research utilizes both the unaltered and blurred datasets to train the SRGAN model, which is then applied to enhance the low-quality dataset through inpainting. Moving forward, the original dataset, low-quality dataset, and the improved dataset are each used independently to train the DCGAN model. In order to compare the difference between the produced image datasets and the real dataset, the FID Score is separately computed. The results of the study found that by training DCGAN with SRGAN repaired medical dataset, the naked eye can observe that the medical image dataset is significantly clearer and there is a reduction in Frchet Inception Distance (FID) Score. Therefore, by using SRGAN and DCGAN the current problem of low quality and small quantity of datasets in the medical field can be solved, which increase the potential possibilities of big data in artificial intelligence filed of medicine.

Atom-profile updating dictionary learning with nucleus sampling attention mechanism sparse coding for audio magnetotelluric denoising

Audio magnetotellurics (AMT), as a commonly used passive geophysical technique, provides outstanding metal ore exploration capabilities based on the resistivity structure of the earth. However, the accuracy of AMT in translating geoelectrical structures decreases when the data collected in mining areas are of poor quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves and posing significant challenges for mineral exploration. To effectively denoise AMT data, we develop a new denoising method that combines atom-profile updating dictionary learning (APrU) with the nucleus sampling attention mechanism (NSAM) sparse coding. First, we use APrU to accurately learn the characteristics of the noise in the AMT data; then, we apply the updated dictionary to perform sparse coding on the AMT data by NSAM to obtain the noise; finally, we subtract the noise from the original AMT data to obtain the denoised data. Our experimental results suggest that the proposed method can learn an overcomplete dictionary via the to-be-processed AMT data, thereby enabling the sparse representation of the noise within the learned dictionary. We also demonstrate the efficacy of this method with a set of field data collected from the Lu-Zong mining area, and the attained denoised data faithfully restore the geoelectrical structures with heightened accuracy. The findings confirm that the proposed method realizes the unsupervised learning of the AMT data and allows us to achieve precise denoising performance.

Deep-unrolling architecture for image-domain least-squares migration

Deep-image prior (DIP) is a novel approach to solving ill-posed inverse problems whose solution is parameterized with an untrained deep neural network and cascaded with the forward modeling operator. A key component to the success of such a method is represented by the choice of the network architecture, which must act as a natural prior to the inverse problem at hand and provide a strong inductive bias toward the desired solution. Inspired by the close link between neural networks and iterative algorithms in classical optimization, we apply an unrolled version of the gradient descent (GD) algorithm as our DIP network architecture, denoted as the deep-unrolling (DU) architecture. Each layer of the unrolled network is comprised of two parts: the first part corresponds to the GD step of the data-fidelity term, whereas the second part, formed by a six-layer convolutional neural network (CNN), plays the role of a regularizer in the associated objective function. The developed DU architecture is applied to the problem of image-domain least-squares migration (IDLSM) to invert migrated seismic images for their underlying reflectivity and is denoted as DU-IDLSM. As such, the DU architecture parameterizes the reflectivity, and the input of each layer of the unrolled network is the reflectivity at the previous layer. Similar to the classical DIP approach, the parameters of the DU architecture are optimized in an unsupervised fashion by minimizing the data misfit function itself. Through experiments with a part of the Sigsbee2A model and a marine field data set, we test the effectiveness of the DU-IDLSM approach and highlight two key benefits. First, the DU architecture can effectively regularize the inversion process, resulting in reflectivity estimates with fewer artifacts and higher image resolution than those produced by conventional IDLSM approaches. Second, we indicate that by including dropout layers in the CNN architecture, DU-IDLSM can produce a qualitative measure of the uncertainty associated with the least-squares migration process.

Geographic network effects in a circular economy: A field data analysis of reusable packaging services

AbstractProduct–service systems (PSS) have the potential to align businesses’ financial incentives with environmental objectives. Conceptual and qualitative research on non‐ownership consumption suggests that such offerings benefit from dense local networks, which motivate system adoption and use. However, geographic network effects and their magnitude have not been examined with field data capturing revealed behavior. This paper leverages a large field dataset from a system for reusable take‐out food containers to evaluate the effect of increased geographic network density of participating restaurants on (a) the acquisition of new users and (b) the frequency of system use. Based on fixed effects Poisson panel models, this paper finds statistically significant and practically meaningful positive effects of increased geographic network density on acquiring new users. Notably, marginal effects of increased geographic network density on user acquisition diminish as networks get denser. In terms of frequency of use, no significant effects of geographic network density are identified. These results contribute to the literature on non‐ownership consumption by presenting nuanced field evidence of indirect, cross‐side network effects in PSS. Furthermore, findings encourage businesses and policymakers to promote PSS with dense local networks.

Prediction of Bedform Dimensions on Alluvial Bed in Unidirectional Flow

In this study, the bedform dimensions of an alluvial bed in a unidirectional flow were experimentally investigated. A series of flume experiments was conducted; 700 sets of flume and field data were used in developing formulae for predicting the bedform dimensions on alluvial beds in unidirectional flow. Bedform dimensions include the length and height of bedforms generated by the lower and upper flow regimes; the resistance coefficient for the flow in different flow regimes is introduced into the proposed formulae. The momentum boundary-layer thickness was introduced as an independent variable instead of the flow depth. Based on a large amount of flume and field data, the coefficients of each parameter were determined; four typical formulae were used to compare the accuracy of the proposed formulae. The experimental results show that the momentum boundary-layer thickness, hydraulic radius, and resistance coefficient for the flow in different regimes correlate well with the bedform dimensions. The calculation results show that the dimensionless particle size should not be ignored in the calculation of bedform dimensions. The bedform dimensions have an obvious trend of rapid increase with an increase in the ratio of flow depth to sand size (H/d). The bedform dimensions obtained using the van Rijn method and the Engelund and Hansen method did not represent the variation trend of the bedform length in the upper flow regime with an increase in H/d when H/d was greater than 103. The calculations using the proposed formulae are more accurate and reasonable than those in previous studies predicting the bedform height and length on an alluvial bed in a unidirectional flow.

A new method for the estimation of the Rayleigh wave group-velocity spectrum

S-wave velocity is commonly used to understand the characteristics of the subsurface, which can be retrieved by inverting the phase or group velocity in Rayleigh wave surveys. Now, many studies have shown the advantage of analyzing group velocity over phase velocity to obtain the S-wave velocity. However, previous methods often suffer from poor resolution in estimating group velocity. To improve the resolution of the group velocity and reduce uncertainty in extracting dispersion curves, we develop a new method for the estimation of the Rayleigh wave group-velocity spectrum based on the slant stack and an adaptive generalized S-transform (APST) with energy reassignment. This method incorporates the APST and the energy reassignment technique to enhance the time-frequency representation and then calculates the group velocity at each frequency using the slant stack. By conducting experiments with synthetic seismic records, it is evident that the new method has high resolution and accurate multimode group-velocity calculations. Furthermore, when applied to a field data set and a synthetic record with noise, the method shows better results and noise robustness. This confirms the superior performance of the method at generating high-resolution dispersion images of the group velocity compared with previous approaches. The method will become an efficient tool in Rayleigh wave exploration.

δ2H isotopic offsets in xylem water measurements under cryogenic vacuum distillation: Quantifying and correcting wood‐water hydrogen exchange influences

AbstractStable isotopes δ18O and δ2H are used to infer vegetation water sources. In some studies, significant xylem water δ2H offsets from potential source waters have been observed. The offsets appear to be more prevalent with cryogenic vacuum distillation (CVD) of plant water. Hypothesized mechanisms for these offsets include changes during plant water uptake and transport, and methodological problems. We propose that a large portion of the offsets are due to hydrogen isotope exchange between xylem water and non‐crystalline hydroxyl groups of wood cellulose and hemicellulose during CVD. We present a method for estimating the hypothesized isotopic exchange between wood tissues and water, which is the result of Rayleigh and equilibrium fractionation. To estimate the exchange, we use published wood properties for North American tree species and isotope chemical relationships as a function of moisture content, CVD temperature and water extraction efficiency. A simple model of exchange between xylem water and hydroxyl groups captures the range of observations in studies in which CVD and non‐CVD methods were compared. To evaluate the model, we compared observed δ2H offsets (sw‐excess) values from two field datasets (90°C, n = 364, and 170°C, n = 43) to δ2H offsets estimated with our chemical model. We found good agreement between observed and estimated δ2H offsets for samples extracted at 90°C (r2 = 0.69) but not for samples extracted at 170°C (r2 = 0.20). The offset may be eliminated by increasing the extraction temperature to 229°C or by adding a standard sufficient to raise the moisture content to >150%. A correction can also be approximated by applying a theoretical calculation based on the extraction temperature, moisture content and water extraction efficiency.

A Wavelet Extraction Method of Attenuation Media for Direct Acoustic Impedance Inversion in Depth Domain

The seismic image produced by pre-stack depth migration is more accurate and has clearer geological significance than the time image. However, the waveform of the depth-domain seismic image is affected not only by depth-dependent velocity variation but also by media attenuation, resulting in strong spectral variation of depth-domain seismic data. Therefore, depth-domain seismic inversion is still challenging. We propose a wavelet extraction method of attenuation media based on the generalized seismic wavelet, to address this issue. Then, the estimated depth-domain wavelets were applied to the direct acoustic impedance inversion. First, we investigated the effect of attenuation media on depth-domain source wavelets and derived an analytical formula for the depth-domain wavelets of attenuation media. Next, the time-domain generalized seismic wavelet was extended to the depth domain, which was utilized to study the feasibility of using the generalized seismic wavelet to characterize the seismic wavelet of the depth-domain attenuation media. Based on the orthogonal matching pursuit, we propose a method to extract the depth-domain generalized seismic wavelet directly from depth-domain seismic data. Finally, we applied this method to the depth-domain direct acoustic impedance inversion of a 3D field data example. Tests on the synthetic and 3D field datasets show that the proposed method can correctly extract the depth-domain seismic wavelet of attenuation media and attain direct inversion of the depth-domain acoustic impedance with high accuracy. Therefore, our method is effective and has robust potential in reservoir characterization, fluid prediction, and attribute extraction in the depth domain.

Automatic stack velocity picking using a semi‐supervised ensemble learning method

AbstractPicking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method.

Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive models demonstrably improve when additional training data is used. We collated field datasets of pasture biomass obtained via destructive sampling, ‘C-Dax’ reflective measurements and rising plate meters (RPM) from ten livestock farms across four States in Australia. Remotely sensed data from the Sentinel-2 constellation was used to retrieve aboveground biomass using a novel machine learning paradigm hereafter termed “SPECTRA-FOR” (Spectral Pasture Estimation using Combined Techniques of Random-forest Algorithm for Features Optimisation and Retrieval). Using this framework, we show that the low temporal resolution of Sentinel-2 in high latitude regions with persistent cloud cover leads to extensive gaps between cloud-free images, hindering model performance and, thus, contemporaneous ability to forecast real-time pasture biomass. By leveraging the spectral consistency between Sentinel-2 and Planet Lab SuperDove to overcome this limitation, we used ten spectral bands of Sentinel-2, four bands of Sentinel-2 as a proxy for pre-2022 SuperDove (referred to as synthetic SuperDove or SSD), and the actual SuperDove (ASD), given that SuperDove imagery has a higher resolution and more frequent passage compared with Sentinel-2. Using their respective bands as input features to SPECRA-FOR, model performance for the ten bands of Sentinel-2 were R2 = 0.87, root mean squared error (RMSE) of 439 kg DM/ha and mean absolute error (MAE) of 255 kg DM/ha, while that for SSD increased to an R2 of 0.92, RMSE of 346 kg DM/ha and MAE = 208 kg DM/ha. The study revealed the importance of robust data mining, imagery harmonisation and model validation for accurate real-time modelling of pasture biomass with ML.

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

Existing methods for estimating formation boundaries from well-log data only analyze the formation along the wellbore, failing to capture changes in the 3D formation structure around it. This paper presents a method for real-time 3D formation boundary interpretation using readily available well logs and seismic image data. In the proposed workflow, the mean formation boundary is estimated as a curve following the well path. 3D surfaces are then fitted through this boundary curve, aligning with the slopes and features in the seismic image data. The proposed method is tested on both synthetic and field datasets and illustrates the capabilities of accurate boundary estimation near the well path and precise representation of boundary shape changes further away from the well trajectory. With this fully automated geological interpretation workflow, human bias and interpretation uncertainty can be minimized. Subsurface conditions can be continually updated while drilling to optimize drilling decisions and further automate the geosteering process.

Check dam impact on sediment loads: example of the Guerbe River in the Swiss Alps – a catchment scale experiment

Abstract. The construction of check dams is a common practice around the world where the aim is to reduce the damage by flooding events through mountain streams. However, quantifying the effectiveness of such engineering structures has remained very challenging and requires well-selected case studies, since the outcome of such an evaluation depends on site-specific geometric, geologic and climatic conditions. Conventionally, the check dams' effectiveness has been estimated using information about how the bedload sediment flux in the stream changes after the check dams are constructed. A permanent lowering of the bedload flux not only points to a success in reducing the probability of sediment transport occurrence but also implies that the sediment input through the system is likely to decrease. Here, we applied a method for data acquisition and two different equations (Meyer-Peter–Müller and Recking approach) to estimate and compare the sediment transport in a mountain stream in Switzerland under engineered and non-engineered conditions. Whereas the first equation is derived from a classical approach that is based on flume experiment data with a slope of less than 0.02 m m−1, the second equation (Recking) has been derived based on a bedload field dataset comprising active mountain streams under steeper conditions. We selected the Guerbe (Gürbe) River situated in the Swiss Alps as a case study, which has been engineered since the end of the 19th century. This has resulted in more than 110 check dams along a ca. 5 km reach where sediment has continuously been supplied from adjacent hillslopes, primarily by landsliding. We measured the riverbed grain size, topographic gradients and river widths within selected segments along this reach. Additionally, a gauging station downstream of the reach engineered with check dams yielded information to calibrate the hydroclimatic situation for the study reach, thus offering ideal conditions for our catchment-scale experiment. Using the acquired data and the dataset about historical runoff covering the time interval between 2009 and 2021 and considering the current engineered conditions, we estimated a mean annual volume of transported bedload which ranges from 900 to 6000 m3 yr−1. We then envisaged possible channel geometries before the check dams were constructed. We inferred (1) higher energy gradients which we averaged over the length of several check dams and which we considered a proxy for the steeper river slope under natural conditions; (2) channel widths that are smaller than those measured today, thereby anticipating that the channel was more confined in the past; and (3) larger grain size percentiles, which we consider to be similar to the values measured from preserved landslides in the region. Using such potential non-engineered scenarios as constraints, the two equations both point towards a larger sediment flux compared to the engineered state, although the results of these equations differed significantly in magnitude. Whereas the Recking approach returned estimates where the bedload sediment flux is ca. 10 times larger in comparison with the current situation, the use of the Meyer-Peter–Müller equation predicts an increase of ca. 100 times in bedload fluxes for a state without check dams. These results suggest that the check dams in the Guerbe River are highly efficient not only in regulating sediment transport by decreasing the probability of high sediment flux occurrence during torrential conditions but also in stabilizing the channel bed by avoiding incision. The most likely consequence is a stabilization of the terrain around such structures by reducing the activation of landslides.

Prodromal Parkinson disease signs are predicted by a whole-blood inflammatory transcriptional signature in young Pink1−/− rats

BackgroundParkinson disease (PD) is the fastest growing neurodegenerative disease. The molecular pathology of PD in the prodromal phase is poorly understood; as such, there are no specific prognostic or diagnostic tests. A validated Pink1 genetic knockout rat was used to model early-onset and progressive PD. Male Pink1−/− rats exhibit progressive declines in ultrasonic vocalizations as well as hindlimb and forelimb motor deficits by mid-to-late adulthood. Previous RNA-sequencing work identified upregulation of genes involved in disease pathways and inflammation within the brainstem and vocal fold muscle. The purpose of this study was to identify gene pathways within the whole blood of young Pink1−/− rats (3 months of age) and to link gene expression to early acoustical changes. To accomplish this, limb motor testing (open field and cylinder tests) and ultrasonic vocalization data were collected, immediately followed by the collection of whole blood and RNA extraction. Illumina® Total RNA-Seq TruSeq platform was used to profile differential expression of genes. Statistically significant genes were identified and Weighted Gene Co-expression Network Analysis was used to construct co-expression networks and modules from the whole blood gene expression dataset as well as the open field, cylinder, and USV acoustical dataset. ENRICHR was used to identify the top up-regulated biological pathways.ResultsThe data suggest that inflammation and interferon signaling upregulation in the whole blood is present during early PD. We also identified genes involved in the dysregulation of ribosomal protein and RNA processing gene expression as well as prion protein gene expression.ConclusionsThese data identified several potential blood biomarkers and pathways that may be linked to anxiety and vocalization acoustic parameters and are key candidates for future drug-repurposing work and comparison to human datasets.

Effects of hydropeaking by an upstream dam on thermal mixing in a riverine lake

The hydropeaking operations of a hydropower dam can significantly affect the flow and thermal regimes in riverine reservoirs, and this effect is very complex, both in space and time. In this study, we used a 3D hydrodynamic model, calibrated with field datasets, to investigate the thermal mixing resulting from hydropeaking within Paldang Lake (PL) in South Korea. The spatiotemporal temperature patterns revealed that density currents critically affected thermal mixing in the reservoir and developed differently based on upstream dam operations and flooding events. Notably, the confluence of the North Han River (NHR) and South Han River (SHR) played a key role in shaping the thermal regime. During the hydropeaking release from the upstream dam in the NHR, the colder water of the NHR from the deep-water discharge of the upstream dam plunged beneath the warmer water of the SHR, leading to three-dimensional mixing. Consequently, the thermal mixing patterns varied depending on the discharge patterns from the upstream dam. Additionally, diurnal variations in the dam discharge frequently affected areas of the reservoir exhibiting high Schmidt stability. During the non-operation period of the upstream dam, the SHR flow backflowed into the downstream section of the NHR, resulting in the confluent area exhibiting the poorest vertical mixing. Secondary currents also played a crucial role in influencing the flow and thermal regimes, with the intensity of the secondary flow varying along the longitudinal mainstream of the reservoir. The thermal mixing state was predominantly weakened to partial mixing within a curved reach owing to the secondary current. Overall, this study demonstrates that the complexity of flow and thermal processes in the PL is significantly influenced by hydropeaking events. These findings emphasize the importance of understanding density currents and secondary flows according to dam operation in managing riverine reservoirs for enhanced ecological health and sustainable water resource management.

Application of sparse S transform network with knowledge distillation in seismic attenuation delineation

Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data. However, it suffers from several inevitable limitations, such as the restricted time-frequency resolution, the difficulty in selecting parameters, and the low computational efficiency. Inspired by deep learning, we suggest a deep learning-based workflow for seismic time-frequency analysis. The sparse S transform network (SSTNet) is first built to map the relationship between synthetic traces and sparse S transform spectra, which can be easily pre-trained by using synthetic traces and training labels. Next, we introduce knowledge distillation (KD) based transfer learning to re-train SSTNet by using a field data set without training labels, which is named the sparse S transform network with knowledge distillation (KD-SSTNet). In this way, we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels. To test the availability of the suggested KD-SSTNet, we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.

Sound-field image denoising using deep neural network considering physical characteristics of sound

This paper presents a method of denoising utilizing deep neural networks (DNN) in sound field images measured by optical methods. Optical sound measurement has attracted much attention because of its capability to visualize sound field with high spatial resolution from a distance, which is difficult with conventional microphones. However, optical methods have the issue of the measured sound-field images often being heavily distorted by noise. This is caused by the weak phase of light changed by sound. Conventionally, noise-reduction filters considering the physical properties of sound have been known to be used for sound-field denoising. In this background, we propose a DNN-based sound-field denoising method that takes into account the physical properties of sound. In the DNN, a network architecture originally used for denoising natural images is employed, and we integrate loss functions based on the Helmholtz equation. Additionally, a 2D sound field dataset obtained from numerical acoustic simulation with random parameters is used during the training. Numerical and experimental data comparison experiments showed that the proposed DNN-based sound-field denoising outperformed the previous non-DNN methods.

Effects of Explicit Sponsorship Disclosure on User Engagement in Social Media Influencer Marketing

Social media influencer marketing has grown substantially in the last decade and is a major advertising channel for many brands. Social media influencers weave sponsored posts with organic content into their feeds, which raises concerns among regulators and consumer advocates that users may not be able to clearly distinguish between sponsored and organic influencer content. Thus, regulators often mandate the explicit disclosure of sponsored content. However, there is little empirical evidence based on field data about the effects of explicit sponsorship disclosure. Therefore, we empirically investigate the effects of explicitly disclosing sponsorship in influencers’ content on users’ engagement using a large-scale field dataset collected from Facebook and Instagram. Our empirical results suggest that explicit sponsorship disclosure increases user awareness of the advertising nature and earns users’ favorability by enhancing transparency about the sponsored content. We further designed two online experiments to corroborate our empirical results and directly test the underlying mechanisms. Our findings have novel and important implications for marketers, influencers, social media platforms, and regulators in the influencer marketing industry.

Two-dimensional magnetotelluric inversion using unstructured triangular mesh implemented in Julia

This study presents a 2-D magnetotelluric inversion code tailored for unstructured triangular meshes, developed using Julia, a high-level, high-performance programming language designed for scientific and numerical computation. The forward modeling engine utilizes a node-based finite element method to solve electromagnetic fields throughout the modeling domain. The inversion process employs the Gauss-Newton optimization algorithm, iteratively updating the model via the minimization of a regularized least-squares objective function. We verified the accuracy of the forward modeling using two reference models and performed a synthetic inversion experiment to validate our inversion method and stress the necessity of accurately handling topography-influenced data. Through its application to constructing an electrical resistivity model beneath the northwestern end of Sumatra Island, Indonesia, we demonstrate the practicality of our code for inverting field datasets.