Data Acquisition Approach Research Articles

Five-analyzer Johann spectrometer for hard X-ray photon-in/photon-out spectroscopy at the Inner Shell Spectroscopy beamline at NSLS-II: design, alignment and data acquisition.

Here, a recently commissioned five-analyzer Johann spectrometer at the Inner Shell Spectroscopy beamline (8-ID) at the National Synchrotron Light Source II (NSLS-II) is presented. Designed for hard X-ray photon-in/photon-out spectroscopy, the spectrometer achieves a resolution in the 0.5-2 eV range, depending on the element and/or emission line, providing detailed insights into the local electronic and geometric structure of materials. It serves a diverse user community, including fields such as physical, chemical, biological, environmental and materials sciences. This article details the mechanical design, alignment procedures and data-acquisition scheme of the spectrometer, with a particular focus on the continuous asynchronous data-acquisition approach that significantly enhances experimental efficiency.

Physics-Informed Masked Autoencoder for active sparse imaging

Imaging technology based on detecting individual photons has seen tremendous progress in recent years, with broad applications in autonomous driving, biomedical imaging, astronomical observation, and more. Comparing with conventional methods, however, it takes much longer time and relies on sparse and noisy photon-counting data to form an image. Here we introduce Physics-Informed Masked Autoencoder (PI-MAE) as a fast and efficient approach for data acquisition and image reconstruction through hardware implementation of the MAE (Masked Autoencoder). We examine its performance on a single-photon LiDAR system when trained on digitally masked MNIST data. Our results show that, with 1.8×10-6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.8\ imes 10^{-6}$$\\end{document} or less detected photons per pulse and down to 9 detected photons per pixel, it achieves high-quality image reconstruction on unseen object classes with 90% physical masking. Our results highlight PI-MAE as a viable hardware accelerator for significantly improving the performance of single-photon imaging systems in photon-starving applications.

An Integrated Data Acquisition Approach for the Structural Health Monitoring and Real-Time Earthquake Response Assessment of a Retrofitted Adobe Church in Peru.

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk.

Catalytic hydrogen combustion over supported metal catalysts; comparison and kinetic study

Catalytic H2 combustion (CHC) is an efficient way to mitigate H2 slip from industrial processes, overcoming the explosion and NOx emission risks. Although Pt-based catalysts are state-of-the-art catalysts, little is known about the CHC catalytic activity of other transition metals (TMs) and their reaction kinetics. Well-dispersed nano-sized M-γAl2O3 (M = Pt, Ru, Co, Ni, Mo) catalysts are synthesized and characterized with spectroscopic and electron microscopy methods. During the CHC reaction, a competitive reaction between O2, H2 and the catalyst is observed, which leads to partial or complete oxidation of the TMs. Stable CHC performance is attained for Ru-γAl2O3 and Co-γAl2O3 catalysts over 45 h. The kinetically controlled region is determined using a new data acquisition approach. The activation energy and pre-exponential factor are compared between M-γAl2O3 catalysts based on the Arrhenius model. This knowledge is highly valued and allows the design of supported catalysts and optimize the CHC reaction in various applications with different requirements.

Development and study of ultrasonic pulse velocity measurement system using wave analysis and two-point detection approach

Abstract This article focuses on the development and analysis of an Ultrasonic Pulse Velocity (UPV) measurement system used for the evaluation of concrete and Reinforced Cement Concrete (RCC) in civil construction. The UPV tester is essential for on-site assessments of structures, as it is used to measure the velocity of ultrasonic waves within the material, directly correlating with its strength. UPV testing is affected by the attenuation of ultrasonic waves in concrete, particularly due to the interfacial transition zone. Excess ultrasonic attenuation results in the reduction in the received signal amplitude which may also results in the omission of the initial pulses due to threshold comparison at the receiver. The study highlights the impact of receiver gain on threshold error and discusses the limitations associated with ADC sampling rate and amplitude resolution. UPV measurement, including counter or data acquisition approach, have error contributions associated with threshold voltage comparison. The error due to threshold amplitude selection is quantified, emphasizing the importance of accurate signal analysis, particularly in highly attenuating medium. The article presents the design and development of a PC-based UPV tester with automatic threshold error compensation. The system includes a transmitter, receiver, 32-bit microcontroller, and a Graphical User Interface (GUI) for data analysis. The article introduces a two-point linear detection logic to minimize errors caused by selected signal amplitude and omission of initial pulses in transit time measurements. The proposed method provides effective resolution of 10 ns through software, even at low sampling rate of 2 MS/s. The experimental results demonstrate the effectiveness of the developed UPV tester, with comparisons to a reference calibration facility at CSIR-NPL. The standard deviation in the ultrasonic transit time measurement by the developed UPV device, with threshold error correction, was ±70 ns.

Data acquisition approaches for single cell proteomics.

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Rapid detection of rare events from in situX-ray diffraction data using machine learning.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions.

Photocatalysis has emerged as an effective tool for addressing the contemporary challenges in organic synthesis. However, the trial-and-error-based screening of feasible substrates and optimal reaction conditions remains time-consuming and potentially expensive in industrial practice. Here, we demonstrate an electrochemical-based data-acquisition approach that derives a simple set of redox-relevant electro-descriptors for effective mechanistic analysis and performance evaluation through machine learning (ML) in photocatalytic synthesis. These electro-descriptors correlate to the quantification of shifted charge transfer processes in response to the photoirradiation and enabled construction of reactivity diagram where high-yield reactive "hot zones" can reflect subtle changes of the reaction system. For the model reaction of photocatalytic deoxygenation reaction, the influence of varying carboxylic acids (substrate A, oxidation-intended) and alkenes (substrate B, reduction-intended) and varying reaction conditions on the reaction yield can be visualized, while mathematical analysis of the electro-descriptor patterns further revealed distinct mechanistic/kinetic impacts from different substrates and conditions. Additionally, in the application of ML algorithms, the experimentally derived electro-descriptors reflect an overall redox kinetic outcome contributed from vast reaction parameters, serving as a capable means to reduce the dimensionality in the case of complex multiparameter chemical space. As a result, utilization of electro-descriptors enabled efficient and robust quantitative evaluation of chemical reactivity, demonstrating promising potential of introducing operando-relevant experimental insights in the data-driven chemistry.

Packaging performance evaluation and freshness intelligent prediction modeling in grape transportation

Vibrations during transportation inevitably lead to mechanical damage, endangering grape freshness and directly impacting their economic worth. While adequate packaging serves as a viable solution, current studies on packaging efficacy lack depth. Moreover, conventional methods for forecasting fruit freshness fail to accommodate the varying freshness levels of grapes across different packaging techniques. Consequently, a novel approach for predicting fruit freshness leveraging multi-sensing technology and machine learning algorithms is introduced. By reasonably evaluating packaging performance, the automation, intelligence, and accuracy of fruit freshness prediction are enhanced. Initially, critical control points in grape supply chain logistics were scrutinized using the HACCP method to identify key environmental parameters (vibration, temperature, and humidity) and their interaction with grape freshness. Subsequently, an environmental monitoring platform was devised for the grape supply chain, facilitating environmental surveillance under distinct packaging types (corrugated carton, foam box, plastic box, and inflatable package). Through a blend of environmental monitoring outcomes and physical-chemical indicators, the protective efficacy of diverse transport packaging was meticulously analyzed and appraised alongside finite element analysis. Notably, environmental data proved capable of characterizing grape freshness in lieu of quality data, with vibration metrics exhibiting strong correlations with quality metrics. Machine learning models were developed to predict grape freshness based on environmental cues, yielding prediction accuracies of 92.512% (SVM) and 94.334% (GA-ANN). The automated, non-destructive data acquisition and novel machine learning approaches offer a fresh avenue for evaluating packaging, predicting freshness, and managing food quality within grape logistics operations.

Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment

The application of distributed acoustic sensing (DAS) for shallow marine seismic investigations is assessed, in particular with respect to the collection of seismic surface wave data in an intertidal setting. Appropriate selection and directional sensitivity of fiber-optic cables is considered and the measured data is validated with respect to conventional seismic data acquisition approaches, using geophones and hydrophones, along with independent borehole and seismic cone penetration test (SCPT) data. In terms of cable selection, a reduction in amplitude and frequency response of an armored cable is observed, when compared with an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable from a practical perspective. The DAS approach also has indicated good consistency with conventional means of surface wave data acquisition, and the inverted [Formula: see text] also is very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that our novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface S-wave velocity data across considerable spatial scales (multikilometer) at high resolution, which is beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared with conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.

Study on intelligent assembly process planning and execution system based on digital twin

PurposeThe purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.Design/methodology/approachBased on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.FindingsDigital twin technology enhances the quality and efficiency of assembly process planning and execution system.Originality/valueIt provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

Mass spectrometry based proteomics: Changing the impact of protein analysis in forensic science

AbstractMass spectrometry‐based proteomics, born from the invention of soft ionization, has revolutionized protein analysis. In‐depth characterization of complex and variable mixtures of proteins, such as that found at crime‐scenes, is now possible. This capability continues to expand with advancements in mass spectral instrumentation and computational power, allowing for the development of dynamic data acquisition approaches and sophisticated bioinformatics tools for data interpretation. Proteins are the major component of all biological material, as a result they have been of interest to forensic practitioners for centuries. While DNA is the gold standard for identification, proteins are able to provide contextual information about biological material not available through genomic approaches alone. Mass spectrometry‐based proteomics has expanded the breadth and ease of forensic protein analysis with several emerging applications that promise to become common place as investigative tools, particularly in forensic serology and toxicology. This includes the targeted analysis of protein biomarkers for the classification of biological tissue and fluids, the detection of protein toxins such as ricin and identification of peptide hormones such as insulin. The ability of protein analysis to leverage information from the genome has been demonstrated to achieve individual identification from biological material, an emerging technology that can be used in the absence of extractable DNA. In recent years, there has been considerable scrutiny of forensic techniques that lack objectivity and a statistical basis, an alternative molecular approach for forensic analysis that utilizes the high specificity of mass spectrometry, is a welcome addition with many potential future applications.This article is categorized under: Forensic Chemistry and Trace Evidence &gt; Trace Evidence

Refining the survey model of the LADM ISO 19152–2: Land registration

Cadastral surveying involves the delineation of property boundaries and the extent and documentation of easements and restrictions (imposed by private or public law), forming the foundation for Land Administration (LA). Survey models and processes constitute vital parts of Cadastres and Land Administration Systems (LASs). However, these models are often inadequately documented and lack standardization in practice. To address the global diversity and complexity of legal and administrative challenges in LA, standardization efforts have yielded the ISO 19152:2012 (ISO, 2012) Land Administration Domain Model (LADM), the Global Land Tool Network's (GLTN) Social Tenure Domain Model (STDM), and the OGC LandInfra/InfraGML standard. The current edition of the LADM focuses on standardised conceptual modelling of LA-related information, including a dedicated sub-package for Spatial and Surveying representation. As part of the ongoing LADM revision, a refined survey model is being developed to support a broad range of surveying and data acquisition approaches and levels of accuracy. Recognizing that surveying technology is not bound by national practices and regulations, this paper focuses specifically on the surveying aspect of LADM. It illustrates that the proposed refined survey model is applicable not only to conventional real property formation but also to participatory land rights recordation processes. The approach adopted in this research is technology-neutral, accommodating the ongoing evolution of surveying technology. It offers support for a broad range of surveying and data acquisition approaches, with varying levels of accuracy. As the demand for high-precision positioning has been persistent within the land mapping and surveying community, particularly since the initial adoption of GPS, aiming to achieve centimetre-level accuracies (initially confined to local services), the paper addresses the fundamental principles of the High Accuracy Service (HAS) concept within the proposed model. The main results presented in this paper are the conceptual model of the refined survey model of LADM Edition II (ISO19152–2), as well as an abstract, reference, cadastral surveying workflow following the principles of the proposed model.

Exploring key parameters influencing student performance in a blended learning environment using learning analytics

Understanding the factors that influence students' results in hybrid learning environments is becoming increasingly important in today's educational environment. The goal of this research is to examine factors that influence students' academic performance as well as their level of participation in blended learning environments. A comprehensive study was conducted with 330 interested participants from the prestigious government polytechnics of the state of Karnataka in order to achieve this goal. Our data acquisition approach relied on the administration of a meticulously crafted survey questionnaire. The conceptual framework underpinning this study seamlessly integrates Transactional Distance Theory (TDT) principles with valuable insights derived from prior research. The Welch test and one-way ANOVA (Analysis of Variance) are two statistical approaches that we used selectively to reinforce our research which produced surprising results. These findings underscore the pivotal role played by certain specific factors. The geographical location of learners and the medium through which they pursue their studies have emerged as critical determinants significantly influencing academic performance. Aspects like the frequency of login activities and active engagement in forum discussions have been found to exert a positive influence on learners' academic performance. In contrast, the duration of sleep did not show a significant impact on performance. These insights bear tangible implications for teachers and policymakers who are dedicated to the enhancement of the quality of BL programs with the ultimate goal of enriching the overall educational experience.

A systematic approach for data acquisition for tool modelling and analysis of wear-dependent tool deflections during milling Inconel 718

Nickel-based superalloys are commonly used for components in the aerospace industry. Due to their favorable mechanical properties, such as high strength and corrosion resistance, these materials are suitable for use under extreme temperature conditions. However, the poor machinability results in high process forces and high tool wear, and, therefore, large tool displacements, so that high manufacturing tolerances are often difficult to achieve. Simulation systems can be used to optimize the process. The data acquisition for the development of the models required is challenging. In the following, a method for in-situ digitization of the tool is presented. Furthermore, the occurring tool deflections during the machining of Inconel718 are analyzed for different wear conditions to serve as basis for the parameterization of simulation models.

MATLAB language assisted data acquisition and processing in liquid chromatography Orbitrap mass spectrometry: Application to the identification and differentiation of Radix Bupleuri from its adulterants

Comprehensive and rapid analysis of secondary metabolites like saponins remains challenging. This study aimed to establish a semi-automated workflow for filtration, identification, and characterization of saikosaponins in six Bupleurum species. Radix Bupleuri, a high-sales herbal medicine, is often adulterated, restricting its quality control and applications. Two authentic Radix Bupleuri species and four major adulterants were analyzed through UHPLC-LTQ-Orbitrap-MS for targeted saikosaponin analysis. To reveal trace saikosaponins and obtain quality fragment data, a MATLAB-based process automatically enumerating “sugar chain + aglycone + side chain” combinations and deduplicating generated a predicted saikosaponin database covering all possible saikosaponins as a precursor ion list for comprehensive targeted acquisition. To focus on informative ions and reduce MS analysis workload, we utilized MATLAB to automatically filtrate the false positive ions by MS1 and MS2 spectrometry. The newly established MATLAB-assisted data acquisition approach exhibited 50 % improvement in characterization of targeted saikosaponins. Furthermore, positive and negative ionization workflows were designed for accurate saikosaponins characterization based on fragmentation rules. In total, 707 saikosaponins were characterized, including over 500 potential new compounds and previously unreported C29 aglycones. We identified 25 saikosaponins present in both authentic species but absent in adulterants as potential markers. This unprecedented comprehensive multi-origin species differentiation demonstrates the promise of MATLAB-assisted acquisition and processing to advance saponin identification and standardize the Radix Bupleuri market.

Application of Remote Sensing in Desertification Monitoring

Desertification has become one of the greatest international concerns in recent decades. This study mainly focuses on the significance and necessity of applying remote sensing to improve efficiency in desertification monitoring. Results show that applications of remote sensing in desertification monitoring have shown advantages and promises of development in the future. Satellite images and unmanned aerial vehicle technology are two major sources of remote sensing data. This study also contains specific applications of remote sensing techniques, including normalized difference vegetation index (NDVI), land cover classification, and spectral mixture analysis (SMA). NDVI measures the vegetation cover to reflect potential desertification but fails to offer a comprehensive picture of the desertification status of an area. While land cover classification identifies different types of land use to monitor desertification, track changes over time, and find potential environmental stressors, the technique requires huge investments in technology and labor. SMA, the sub-pixel classification technique, provides a detailed analysis while consuming significant computing resources and time. This study also provides prospects for future developments of data acquisition and analysis approaches in desertification monitoring using remote sensing. Making suggestions on future research topics and possible methodologies, this study hopes to inspire further research in this area to better deal with the global issue of desertification.

A novel data acquisition approach for valves in industrial refrigeration

In this paper, a novel method is introduced for the acquisition and transmission of critical parameters from valves used in industrial refrigeration. The parameter sensing provides local intelligence to the valve, which can be later used as a feedback signal for the controller. The hardware design includes ICS (Industrial Control Servo) valve with modifications to connect a Linear Variable Transformer (LVT) position sensor, pressure and temperature sensors. Also, a circuit board carrying the phase shift oscillator, Root Mean Square (RMS) to Direct Current (DC) converter, power supply and a dedicated Bluetooth Low Energy (BLE) module for transmitting the collected data is designed. The transmitted data can be received in a mobile phone android application and used for a secondary data calculation, alarm notification and alerting service technician. The sensor integration along with BLE connectivity creates a novel platform for data acquisition in Industrial Refrigeration (IR). The proposed method tested in real time to regulate the Helium gas flow rate and measured valve parameters is monitored by the dedicated mobile application.

Do Geographical Indication Products Promote the Growth of the Agricultural Economy? An Empirical Study Based on Meta-Analysis

Do geographical indication products help facilitate the development of the agricultural economy? This problem is a point of controversy in the field of global agricultural intellectual property. For a long time, there have been different viewpoints on this problem; that is, there is a positive correlation, negative correlation, U-shape correlation, or no correlation between the geographical indication products and the development of the agricultural economy in the context of different studies. To clarify the influence mechanism between the two and explain why there are these disputes, this study used the meta-analysis method to statistically reanalyze 405 observation values provided in 64 independent research samples from the context of different regions around the world. The study results show that geographical indications not only generate more economic benefits than ordinary products but also contribute to the growth of the agricultural economy by effectively promoting the development of agricultural product trade and the enhancement of agricultural product price. There exists a low positive correlation between the geographical indication products and the agricultural economy (r = 0.176, 95% CI = [0.126, 0.225]). In addition, the promotion effect of geographical indication products on the agricultural economy is regulated by the country of origin of the samples, sample level, publication journal, data type, data acquisition approach, and research method. Our research findings further revealed the internal relationship mechanism between the geographical indication products and the agricultural economy and lay a foundation for better protecting and developing geographical indication products.