Data Acquisition Techniques Research Articles

The Influence of Social Presence on Purchase Intention in Live Video Commerce (Empirical Study in Bandung City)

The rapid development of internet technology has changed the way we live and interact. The increasing number of internet users in Indonesia has encouraged business actors to be more innovative in marketing their products digitally. The emergence of direct shopping features on various social media platforms has revolutionized the way consumers shop, allowing transactions to occur instantly and interactively. There is competition between e-commerce that uses live video features such as Shopee, TikTok, Tokopedia, and Lazada. This study aims to determine the role of social presence on purchase intention mediated by immersive experience and trust in live video commerce features in Indonesia. The research methodology employed is a descriptive and quantitative approach, which utilizes data acquisition techniques through online questionnaires to obtain 385 respondents. Purposive sampling is implemented in conjunction with nonprobability sampling. The data collected will be analyzed using SmartPLS version 3.9. The results of this study found that social presence has no effect on purchase intention. Keywords: Social Presence, Live Video Commerce, Purchase Intention.

Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime. Published by the American Physical Society 2024

A Survey of Multimodal Perception Methods for Human–Robot Interaction in Social Environments

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field.

End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution.

Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein-ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution. The analysis of samples via label-free, data-independent acquisition (DIA) techniques led to significant improvements on a workflow time per sample basis over current standard practices. Experiments utilizing three established PAL ligands with known targets, (+)-JQ-1, lenalidomide, and dasatinib, demonstrated the utility of having the flexibility to design experiments with a myriad of variables. Data revealed that this workflow can enable the confident identification and rank ordering of known and putative targets with outstanding protein signal-to-background enrichment sensitivity. This unified end-to-end throughput strategy for processing and analyzing these complex samples could greatly facilitate efficient drug discovery efforts and open up new opportunities in the chemical proteomics field.

Mental illness detection through harvesting social media: a comprehensive literature review

Mental illness is a common disease that at its extremes leads to personal and societal suffering. A complicated multi-factorial disease, mental illness is influenced by a number of socioeconomic and clinical factors, including individual risk factors. Traditionally, approaches relying on personal interviews and filling out questionnaires have been employed to diagnose mental illness; however, these manual procedures have been found to be frequently prone to errors and unable to reliably identify individuals with mental illness. Fortunately, people with mental illnesses frequently express their ailments on social media, making it possible to more precisely identify mental disease by harvesting their social media posts. This study offers a thorough analysis of how to identify mental illnesses (more specifically, depression) from users’ social media data. Along with the explanation of data acquisition, preprocessing, feature extraction, and classification techniques, the most recent published literature is presented to give the readers a thorough understanding of the subject. Since, in the recent past, the majority of the relevant scientific community has focused on using machine learning (ML) and deep learning (DL) models to identify mental illness, so the review also focuses on these techniques and along with their detail, their critical analysis is presented. More than 100 DL, ML, and natural language processing (NLP) based models developed for mental illness in the recent past have been reviewed, and their technical contributions and strengths are discussed. There exist multiple review studies, however, discussing extensive recent literature along with the complete road map on how to design a mental illness detection system using social media data and ML and DL classification methods is limited. The review also includes detail on how a dataset may be acquired from social media platforms, how it is preprocessed, and features are extracted from it to employ for mental illness detection. Hence, we anticipate that this review will help readers learn more and give them a comprehensive road map for identifying mental illnesses using users’ social media data.

Efficient Training Data Acquisition Technique for Deep Learning Networks in Radar Applications

In the field of radar, deep learning techniques have shown considerably superior performance over traditional classifiers in detecting and classifying targets. However, acquiring sufficient training data for deep learning applications is often challenging and time consuming. In this study, we propose a technique for acquiring training data efficiently using a combination of synthesized data and measured background data. We utilized graphics processing unit (GPU)-based physical optics methods to obtain the backscattered field of moving targets. We then generated a virtual dataset by mixing the synthesized target signal with the background signal real data. Subsequently, we trained a convolutional neural network using the virtual dataset to identify three different classes—Bird, Drone, and Background—from a range-Doppler map. When tested using the measurement data, the trained model achieved an accuracy of over 90%, demonstrating the effectiveness of the proposed method in acquiring training data for radar-based deep learning applications.

Computer-Assisted Porcelain Laminate Veneer Preparation: A Scoping Review of Stereolithographic Template Design and Fabrication Workflows.

Computer-assisted preparation of porcelain laminate veneers (PLVs) using stereolithographic templates has been developed to enhance the accuracy of tooth preparation. However, the digital workflows involved in guided PLV preparation remain inconsistently defined across various practices. Therefore, this scoping review aimed to examine publications on computer-assisted PLV preparation to identify the key stage of digital workflows involved in designing and fabricating stereolithographic templates, as well as to highlight the limitations of various template designs. This scoping review aimed to identify publications on digital workflows for designing and fabricating stereolithographic templates in computer-assisted porcelain laminate veneer preparation. A systematic search on MEDLINE/PubMed, Web of Science and Scopus identified English-language articles published from 2014 to March 2024. Eligible articles focused on digitally designed and fabricated tooth reduction templates for porcelain laminate veneers, excluding conventional tooth preparation procedures for tooth reduction assessment. Seven clinical reports were included, demonstrating various 3D data acquisition techniques for virtual patient generation. All articles described virtual diagnostic wax-ups on digital casts, with two using a virtual articulator. Only five articles documented chair-side mock-ups with resin trial restorations to evaluate planned dental esthetics. Additionally, virtual tooth preparation prior to templates design was included in only four articles. The templates were designed using different software and ranged from simple designs with access windows to complex stacked templates with rotary instrument sleeved windows. Each template design had limitations affecting tooth reduction accuracy. All articles reported printing templates in clear acrylic resin using different technologies. In conclusion, the review highlights a lack of standardization in the digital workflow for designing stereolithographic templates for PLVs. Establishing a sound protocol for designing the tooth reduction templates is essential to ensure the accuracy and consistency of veneer preparation.

Evaluation of the existing conditions of lasiana beach tourism destinations and ecosystems, Kupang City

This research aims to determine the existing conditions of the destination and ecosystem of Laisiana beach tourism and evaluate the performance and importance of these existing conditions. This research falls under the action research design with data acquisition techniques using interview, observation, and documentation techniques. Determining the research population is through non-probability sampling with a convenience sampling approach and key actors sampling. The number of research samples is 75 people. Variable measurement is done on a Likert scale. The data types consist of primary and secondary data. Data analysis is done using a quantitative approach, namely interval technique and IPA analysis. The research results indicate that there is still a gap between the existing conditions of the destination and the tourism ecosystem towards satisfaction. The evaluation results show that the existing conditions of tourist destinations have not been a focus while the expectations of tourists are very high. Unlike the existing tourism conditions, most attributes such as community acceptance, tourist locations, tourist products, investments, development policies, and supporting tourism industries are expectations of tourists to be improved but are not a priority, while aspects that are not of concern to tourists receive excessive attention such as tourist targets, supporting facilities, tourism trends, product value, marketing, and tourism resources.

Dual-Modal Optical Imaging of Tissue Perfusion in Response to Cooling Stimulation Facilitates Early Detection of Pressure Ulcer.

Pressure ulcers present a significant human and economic challenge, lacking a reliable method for early detection. To address this, we developed a system capable of early detection by using cooling stimulation and dynamic data acquisition techniques to monitor blood perfusion and skin temperature. The system consists of laser speckle perfusion imaging and thermal imaging. And we performed simulations to demonstrate that the system is capable of detect tissue damage across multiple layers, from superficial to deep. Testing on a rabbit ear model demonstrated that this approach, which combines dynamic perfusion and temperature parameters, effectively distinguishes early pressure ulcer areas from normal skin with a significant p value of 0.0015. This distinction was more precise compared to methods relying solely on static parameters or one parameter. Our study thereby offers a promising advancement in the proactive management and prevention of pressure ulcers.

Reliability of task-based fMRI in the dorsal horn of the human spinal cord

Abstract The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across 2 consecutive days in 40 healthy volunteers. We assessed the test–retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat-pain stimuli (1 s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with fair reliability across days. Taken together, these results demonstrate that heat-pain stimuli as short as 1 s are able to evoke a robust and spatially specific BOLD response, which is, however, strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.

SelfMixed: Self-supervised mixed noise attenuation for distributed acoustic sensing data

Distributed acoustic sensing (DAS) is an emerging data acquisition technique known for its high sensing density, cost effectiveness, and environmental friendliness, making it a technology with significant future application potential in many fields. However, DAS signals are often contaminated by various types of noise, such as high-frequency, high-amplitude erratic, and horizontal noise, making their processing challenging. Therefore, it is crucial to leverage the physical characteristics of these diverse types of noise in DAS data and effectively attenuate them. In this work, we develop SelfMixed, a novel self-supervised learning method for mixed noise suppression of DAS data. We fully exploit the physical characteristics of different types of noise in DAS data and introduce a physical characteristic-based training strategy. Specifically, we use the [Formula: see text] norm to characterize random noise, the [Formula: see text] norm for erratic noise, and horizontal smoothness and vertical nonsmoothness for horizontal noise. In addition, we use a blind-spot-based training strategy for DAS denoising, relying solely on observed noisy data. To more effectively attenuate horizontal noise, we also introduce a Fourier transform-based parameterization method. By combining self-supervised deep priors with the physical characteristics of mixed DAS noise, our method effectively attenuates complex mixed noise in field DAS data. Extensive experiments on synthetic and field data from various geographic scenarios validate the superiority of SelfMixed over seven state-of-the-art DAS denoising approaches.

Predicting Particle Size and Soil Organic Carbon of Soil Profiles Using VIS-NIR-SWIR Hyperspectral Imaging and Machine Learning Models

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction.

Mamah Dede's Communication style in conveying da'wah message in the mama and AA action program in indosiar

ABSTRACTThis research examines the communication style employed by Mamah Dedeh, a preacher, in delivering sermons utilizing the Tubbs and Moss communication style theory approach. Preachers should be mindful of their communication style when delivering da'wah messages. Utilizing different communication methods has the potential to enhance the significance within an individual's subconscious mind. The chosen communication channel must possess the capacity to preserve the message while also being easily retained by the general population. This study employs a qualitative methodology to analyze Mamah Dedeh's communication style using certain methodologies. The data acquisition technique was conducted by duplicating lectures from YouTube. The collected results indicate that the communication style employed consists of The Controlling Style and The Structuring Style.

Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning.

Ocean plastic pollution is one of the global environmental problems of our time. "Rubbish islands" formed in the ocean are increasing every year, damaging the marine ecosystem. In order to effectively address this type of pollution, it is necessary to accurately and quickly identify the sources of plastic entering the ocean, identify where it is accumulating, and track the dynamics of waste movement. To this end, remote sensing methods using satellite imagery and aerial photographs from unmanned aerial vehicles are a reliable source of data. Modern machine learning technologies make it possible to automate the detection of floating plastics. This review presents the main projects and research aimed at solving the "plastic" problem. The main data acquisition techniques and the most effective deep learning algorithms are described, various limitations of working with space images are analyzed, and ways to eliminate such shortcomings are proposed.

The Effect of New Normal Enforcement On The Tourism Industry Sector

This research was conducted to determine the effect of implementing new habits on the marine tourism industry on Pramuka Island. Qualitative descriptive methods were used in this research. Primary data acquisition techniques were carried out through observation and interviews with stakeholders organizing tourism activities on Pramuka Island, Seribu Islands. Respondents were selected based on fulfilling aspects of attractions, amenities and accessibility that are important in developing a tourist location. The research results show that the implementation of new habits has had a positive impact on the sustainability of the tourism industry on Pramuka Island in the new normal era. Keywords: Covid 19 pandemic, marine tourism, new habits

A comparative study of principal component analysis and kernel principal component analysis for photogrammetric shape-based turbine blade damage analysis

Photogrammetry is popular as a non-contact full-field data acquisition technique for machine condition monitoring purposes. Two popular implementations thereof are Digital Image Correlation (DIC) and 3D Point Tracking (3DPT). Since these two approaches require surface preparation in the form of applying speckle patterns or discrete markers, their use is negated in several industrial applications, such as the online condition monitoring and/or assessment of horizontal axis wind turbines. A shape-based photogrammetric approach that focuses on analysing changes in the form of a rotating blade’s boundary contour is a viable alternative that does not require any surface preparation. 3D shapes of a blade at a particular instance can be extracted from a pair of stereoscopic images of the blade. Shape Principal Component Descriptors (SPCDs) determined from applying Principal Component Analysis (PCA) to Fourier coefficients of chain-code shape signatures of the blade contour can be determined. These can be regarded as indicators of the form of the shape, and as the blade rotates, variations in the SPCDs can be investigated to better understand the dynamics of the blades. This paper focuses on the application and performance evaluation of data reduction and classification techniques for a novel shape-based photogrammetry condition monitoring technique. Investigations are conducted to show that applying data reduction methods to raw time domain SPCDs can result in successful classification of differently damaged blades. Post-processing strategies are developed to ensure a more robust shape based condition monitoring tool for analysing turbine blades. Kernel Principal Component Analysis (KPCA) is implemented and it is shown that it out-performs the conventional PCA in terms of classifying different blade damage modes. The feasibility of using multi-domain statistical features as feature vectors to which PCA or KPCA is applied for classification purposes is also investigated. Results indicating how well differently damaged blades can be distinguished are provided, and it is clearly illustrated that multi-domain statistical features are more robust to noise contamination in the signals compared to using the raw SPCDs data.

Comparing two crowdsourcing platforms: assessing their potential for mapping Antarctica

sGeographical data acquisition in Antarctic regions is challenging due to the lack of human habitation, harsh environmental conditions, and limited accessibility. This research explores and evaluates the capability of two crowdsourcing platforms in mapping facilities across Antarctic regions. The study presents crowdsourcing projects related to polar regions in the literature. The methodology section outlines the data acquisition techniques employed by Flickr and Happywhale, and the spatial evaluation methods applied to the collected data. In the implementation and results section, the spatiotemporal potential of the data obtained from the two identified crowdsourced platforms is assessed, and the results based on spatial statistical methods are compared. In the discussion and conclusion section, the contribution of the two identified crowdsourced platforms to mapping activities is evaluated in terms of spatial, temporal, and content differences. This study reveals that Happywhale offers data with higher spatial consistency, considering seasonal representation and spatial autocorrelation. Additionally, content restrictions and reliance on GPS enhance spatial accuracy in Happywhale. At the same time, the liberation of data production leads to lower quality but increased quantity, diversity, and spatial coverage, as observed in Flickr. By comparing two crowdsourced platforms, this study enhances data acquisition and evaluation potential in Antarctica.

Reconstruction of the route and characterization of the El Habel tunnel of the Toudja roman aqueduct (Algeria) using 3D laser scanning technology

Nowadays, new data acquisition and representation techniques enable the creation of new fields of intervention and investigation for heritage specialists as well as architectural and archaeology disciplines, whether in conservation, research, or project studies. These innovative digitization techniques are critical for preserving and transmitting our heritage. The Toudja aqueduct, which supplied the Roman city of Saldae, now known as Bejaia, has deteriorated significantly over time. The El Habel tunnel preserves its history and didactic value as a technical and heritage asset, and it deserves further investigation. A 3D lasergrammetry survey method was used to reconstruct the tunnel trajectory in a plane, visualize its internal properties, including excavation error, model its components (shafts 1 and 2), and extract plans and profiles. The findings represent an improvement over previous research using conventional methods. The digitized and processed acquisitions show the tunnel's current route, geolocation, morphological, structural, and dimensional characteristics of its gallery, as well as the state of its surfaces. This study aims to incorporate this information into cultural and archaeological heritage maps to ensure its preservation. Furthermore, detecting visible disorders on the inner walls will enable more thorough examinations for potential tunnel rehabilitation.

Physics-guided deep learning-based inversion for airborne electromagnetic data

SUMMARY The Earth's subsurface structure provides critical insights into sustainable resource management and geologic evolution. The airborne electromagnetic (AEM) method is an efficient data acquisition technique and can be used to image the underground resistivity structure with high spatial resolution. However, inversion of the increasingly huge volume of AEM data poses a heavy computational burden. In this study, we develop a hybrid deep learning-based approach by using the physics-guided neural network (PGNN) which incorporates the governing physical laws into the loss function to solve the AEM inverse problem. The PGNN integrates the strength of data-driven method for representation learning with electromagnetic laws and allows for the underlying physical constraints to be strictly satisfied. We validate the effectiveness of our approach using both synthetic and field datasets. Compared with the classic Gauss–Newton method, our PGNN inversion system shows strong robustness against multiple noise sources and reduces the risk of being trapped in local extrema. Moreover, the PGNN-inverted results are physically more consistent with the AEM observations compared to the purely data-driven approach. Application to the field AEM data from Northern Australia demonstrates that the PGNN-based inversion framework effectively estimates the subsurface electrical properties with considerable lateral continuity and significantly higher efficiency, completing the inversion of more than 2734000 AEM soundings taking only minutes on a common PC. Our proposed PGNN-based method shows great promise for large-scale underground resistivity imaging, and the well-identified subsurface resistivity structure can effectively improve our understanding of resource distributions and geological hazards.

Reliability of task-based fMRI in the dorsal horn of the human spinal cord.

The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with fair reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.