Acquisition Methodology Research Articles

Virtual Two-Dimensional Electrode Representation Through the Spatial Transformation of Production Data

The production of battery cells is characterized by a multi-process production chain in which the individual processes significantly impact intermediate and product properties. A virtual representation of the produced electrode enables the analysis of cause-and-effect relationships and can be used for quality assurance. While the virtual representation of components or products is already common in other engineering domains, an approach to a holistic virtual representation of the electrode in battery cell production does not yet exist. A high level of data accuracy in multiple dimensions is required to reduce scrap within electrode production and automate processes. Therefore, this work presents a comprehensive data acquisition, preprocessing, and analysis methodology to get a multi-dimensional representation of the electrode properties and process parameters. Multiple data modeling options were analyzed to generate a uniform and complete database of all sensor data. Gaussian process regression and Kriging modeling were used to interpolate the mass loading of electrodes after the coating process so that the holistic virtual representation of the electrode was enabled. The use of industrial edge computing and interpolation of sensor data enables a more accurate, virtual representation of produced electrodes.

TEACHING ENGLISH AS A FOREIGN LANGUAGE

This article provides an in-depth exploration of the multifaceted process of teaching English as a second language (TESL). Delving into the intricacies of language acquisition and pedagogical methodologies, the article offers insights into the challenges and innovations in TESL. With a focus on fostering effective communication skills and cultural understanding, the discussion encompasses the role of technology, cultural sensitivity, and the evolving landscape of TESL. The article concludes by emphasizing the pivotal role of TESL in promoting global communication and cultural exchange.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease.

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

Single-Cell Proteomics Analysis with Tecan Uno and SCREEN Workflow.

With advances in sample preparation, small-volume liquid dispensing technologies, high-resolution MS/MS instrumentation, and data acquisition methodologies, it has become increasingly possible to confidently investigate the heterogeneous proteome found within individual cells. In this chapter, we present an automated high-throughput sample preparation workflow based on the Tecan Uno instrument for quantitative single-cell mass spectrometry-based proteomics. Cells are analyzed by the Single-Cell Proteome Analysis platform (SCREEN), which was introduced earlier and provides deeper proteome coverage across single cells.

Data-independent acquisition proteomics of cerebrospinal fluid implicates endoplasmic reticulum and inflammatory mechanisms in amyotrophic lateral sclerosis.

While unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of amyotrophic lateral sclerosis (ALS), high-abundance proteins mask the presence of lower abundance proteins that may have diagnostic and prognostic value. However, developments in mass spectrometry (MS) proteomic data acquisition methods offer improved protein depth. In this study, MS with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of people with ALS (n = 40), healthy (n = 15) and disease (n = 8) controls. Quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins, all significantly upregulated in ALS versus healthy controls, and 9 with altered abundance in ALS versus disease controls (FDR < 0.1). Elevated chitotriosidase-1 (CHIT1) was common to both comparisons and was proportional to ALS disability progression rate (Pearson r = 0.41, FDR-adjusted p = 0.035) but not overall survival. Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS versus healthy controls) was proportional to disability progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and survival (Kaplan Meier log-rank p = 0.013) but not independently in multivariate proportional hazards models. Weighted correlation network analysis was used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was associated with age at symptom onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio = 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, was negatively correlated with disability progression rate (r = -0.42, FDR-adjusted p = 0.109). DIA acquisition methodology therefore strengthened the biomarker candidacy of CHIT1 and UCHL1 in ALS, while additionally highlighted inflammatory and endoplasmic reticulum proteins as novel sources of prognostic biomarkers.

The Effect of Communication and Physical Work Environment on Employee Performance

The workplace environment significantly influences employee performance. Employee productivity is directly impacted by the work environment, which enhances performance. As a fundamental human activity, communication plays a crucial part in social relationships; as a result, its impact on the workplace is crucial. Employee performance is more productive when there is more built-in communication between departments. The primary objective of this study is to investigate the impact of communication and the physical work environment on employee performance within car and machinery manufacturing enterprises located in Bandung. The present investigation employed a nonprobability sampling technique. The utilization of sample acquisition methodology becomes crucial when the population size is constrained or when the research objective is to achieve the highest level of generalizability. Censuses are another name for saturated sampling, in which the entire population—61 people in this case—serves as a sample. Descriptive and verifiable research methodology is used. Utilized data collection methods include observation, questionnaire distribution, and interviewing. Multiple linear regression, multiple correlations, F-test, T-test, coefficient of determination, partial determination coefficient, and hypothesis testing are the data analysis techniques used. According to the research findings, a vehicle and machinery sector firm in Bandung can have good communication, a physical work environment, and staff performance. Employee performance is strongly influenced by communication and the physical work environment, with the remaining influences coming from elements that were not studied.

Development of time-resolved photoluminescence microscopy of semiconductor materials and devices using a compressed sensing approach

Charge carrier lifetime is a key property of semiconductor materials for photonic applications. One of the most established methods for measuring lifetimes is time-resolved photoluminescence (TRPL), which is typically performed as a single-point measurement. In this paper, we demonstrate a new time-correlated single photon counting method (TCSPC) for TRPL microscopy, for which spatial information can be achieved without requiring point-by-point scanning through the use of a compressed sensing (CS) approach. This enables image acquisition with a single pixel detector for mapping the lifetime of semiconductors with high repeatability. The methodology for signal acquisition and image reconstruction was developed and tested through simulations. Effects of noise levels on the reliability and quality of image reconstruction were investigated. Finally, the method was implemented experimentally to demonstrate a proof-of-concept CS TCSPC imaging system for acquiring TRPL maps of semiconductor materials and devices. TRPL imaging results of a semiconductor device acquired using a CS approach are presented and compared with results of TRPL mapping of the same excitation area measured through a point-by-point method. The feasibility of the methodology is demonstrated, the benefits and challenges of the experimental prototype system are presented and discussed.

Building Resilience in Jamaica’s Farming Communities: Insights From a Climate-Smart Intervention

Rural farmers, especially those with limited resources, are on the frontline of the climate crisis. In the Caribbean, vulnerability of agri-food systems to climatic disturbances is recognized, but empirical evidence detailing effective adaptation strategies remains patchy. In Jamaica, a combination of challenges—ranging from diminishing availability of arable land and stagnant agricultural innovation to the marginalization of small-scale farming and recurrent climatic shocks—has led to significant agricultural setbacks and socioeconomic distress for local farmers. This case study is based on a synthesis of quantitative and qualitative data derived from a climate adaptation initiative in Peckham, Clarendon, a pivotal agricultural hub in Jamaica. The data acquisition methodologies encompassed livelihood baseline assessments, community engagement surveys, training workshops, and focus group discussions. Synthesis of data from 31 agricultural training sessions and 16 climate-smart agriculture workshops, involving 458 farmers, offers cogent evidence of the initiative’s tangible impacts on the Peckham farming community. The initiative was guided by two core objectives: (1) enhancing food security and rural livelihoods through the implementation of a renewable energy-powered aquaponics framework, and (2) improving land and water resource management practices. To achieve these goals, the Farmer-Field School approach was employed to promote climate-smart agricultural practices and strengthen the institutional capabilities of farming groups. The insights from the assessment underscore the potential of integrated climate-resilient agricultural practices in addressing both economic and environmental challenges faced by rural farmers.

Challenges in Imaging Analyses of Biomolecular Condensates in Cells Infected with Influenza A Virus.

Biomolecular condensates are crucial compartments within cells, relying on their material properties for function. They form and persist through weak, transient interactions, often undetectable by classical biochemical approaches. Hence, microscopy-based techniques have been the most reliable methods to detail the molecular mechanisms controlling their formation, material properties, and alterations, including dissolution or phase transitions due to cellular manipulation and disease, and to search for novel therapeutic strategies targeting biomolecular condensates. However, technical challenges in microscopy-based analysis persist. This paper discusses imaging, data acquisition, and analytical methodologies' advantages, challenges, and limitations in determining biophysical parameters explaining biomolecular condensate formation, dissolution, and phase transitions. In addition, we mention how machine learning is increasingly important for efficient image analysis, teaching programs what a condensate should resemble, aiding in the correlation and interpretation of information from diverse data sources. Influenza A virus forms liquid viral inclusions in the infected cell cytosol that serve as model biomolecular condensates for this study. Our previous work showcased the possibility of hardening these liquid inclusions, potentially leading to novel antiviral strategies. This was established using a framework involving live cell imaging to measure dynamics, internal rearrangement capacity, coalescence, and relaxation time. Additionally, we integrated thermodynamic characteristics by analysing fixed images through Z-projections. The aforementioned paper laid the foundation for this subsequent technical paper, which explores how different modalities in data acquisition and processing impact the robustness of results to detect bona fide phase transitions by measuring thermodynamic traits in fixed cells. Using solely this approach would greatly simplify screening pipelines. For this, we tested how single focal plane images, Z-projections, or volumetric analyses of images stained with antibodies or live tagged proteins altered the quantification of thermodynamic measurements. Customizing methodologies for different biomolecular condensates through advanced bioimaging significantly contributes to biological research and potential therapeutic advancements.

Digitalization of The Construction Industry with BIM and 3D Machine Control Integration for Construction Performance Improvement.

The Government of the Republic of Indonesia through Presidential Regulation Number 100 of 2014 and Presidential Regulation Number 117 of 2015 aims the construction of the 2770 km Trans Sumatra Toll Road from Bakauheni to Banda Aceh to be fully constructed by 2024. There are two key stages in the project phase, i.e planning and construction stages. The planning stage is carried out using the Building Information Model (BIM), while the construction phase integrates BIM with 3D-Machine-Control. This study is conducted in Trans Sumatera Pekanbaru - Bangkinang’s Toll Road with 40 kilometers long as a study case. The data acquisition methodology is conducted using qualitative and quantitative methods throughout the field’s experiments. This study conducted the examination of the LiDAR UAV digital survey’s accuracy, the BIM implementation on Level of Development (LOD) 200, 300, and 400, verification of the usage of Motor Grader with 3D-Machine-Control efficiency in elevation cutting works, and the application of Vibro Compactor with 3D-Machine-Control for improving the accuracy of soil compaction on the subgrade and base course. The results showed that the topographic data from UAV LiDAR are accurate within LE90 accuracy with 0.176 cm. The optimalization of 3D-Machine-Control could carry out the earthworks as similar as design also can be used as quality and quantity control. The time efficiency which obtained from implementing Motor Grader with 3D-Machine-Control for elevation cutting application is 47%.

The PATH to PPE Mastery - Programme for Assessment and Training in HCID (High Consequence Infectious Diseases) PPE (Personal Protective Equipment), Mastery

High Consequence Infectious Diseases (HCIDs), have the potential to cause pandemics and require particular focus for preparedness due to their high mortality rates. The application of Personal Protective Equipment (PPE) for HCIDs is complex and carries significant risk of Health Care Worker (HCW) contamination if done incorrectly. Previous reviews have reported a lack of information on the nature of training provided and the ideal timing of repeat training to best retain skills. Simulation Based Mastery Learning (SBML) is a methodology for skill acquisition which encompasses deliberate practice and repeated assessment until the learner achieves a pre-set Mastery standard. SBML has been demonstrated to improve competence, skill retention and patient outcomes in other clinical procedures. SBML has not been previously studied or utilised in HCID PPE training. We aimed to increase the likelihood of safe clinical practice by evidencing that Lothian modified SBML for PPE effectively prepares our priority learners. A quasi-experimental within group post-test design was used. Learners undertook a modified SBML programme which included two-hour asynchronous and two-hour synchronous components. 11 learners (10 infectious diseases registrars and 1 infectious diseases consultant) were enrolled in the programme with 8 completing all stages, all of whom achieved the Mastery passing standard. The resources were highly rated by learners with the exemplar videos of skills highlighted as particularly useful. Self-assessed preparedness for each skill increased following pre-learning and synchronous sessions. Modified SBML can be used as an effective methodology for the training and assessment of HCWs in the donning and doffing of HCID PPE.

The 103Rh NMR spectroscopy and relaxometry of the rhodium formate paddlewheel complex.

The nuclear magnetic resonance (NMR) spectroscopy of spin-1/2 nuclei with low gyromagnetic ratio is challenging due to the low NMR signal strength. Methodology for the rapid acquisition of 103Rh NMR parameters is demonstrated for the case of the rhodium formate "paddlewheel" complex Rh2(HCO2)4. A scheme is described for enhancing the 103Rh signal strength by polarization transfer from 1H nuclei, which also greatly reduces the interference from ringing artifacts, a common hurdle for the direct observation of low-γ nuclei. The 103Rh relaxation time constants T1 and T2 are measured within 20minby using 1H-detected experiments. The field dependence of the 103Rh T1 is measured. The high-field relaxation is dominated by the chemical shift anisotropy mechanism. The 103Rh shielding anisotropy is found to be very large: |Δσ| = 9900 ± 540ppm. This estimate is compared with density functional theory calculations.

Stress Separation in Digital Photoelasticity Part B - Whole Field Evaluation of Stress Components

In photoelasticity, the method of obtaining the individual values of principal stresses/normal stresses separately is referred to as stress separation. For stress separation, one needs the value of fringe order and the isoclinic angle free of noise over the domain. With improved data acquisition and smoothing methodologies, it has now become possible to get accurate and smooth variation of the fringe order and the isoclinic value over the domain. This has been discussed in detail in Part-A of this article. Shear difference is one of the widely used techniques for stress separation in photoelasticity. Though it is a line byline technique, a whole field evaluation of stress separation using this technique and its representation is shown in this paper. It is observed that spikes in isoclinic values lead to streak formation in the whole field representation of separated stress components. The use of outlier algorithm for smoothing isoclinic and isochromatic data has removed these streaks and has also greatly improved the accuracy of the separated stress components. The various issues related to digital implementation such as boundary pixel identification, grids for accommodating the various boundaries etc. are given in the Appendix. Performance of the methodology is verified for simply and multiply connected objects. The models are subjected to moderate loads to have sufficient isochromatic-isoclinic interaction. The stress components obtained are smoothed with the outlier algorithm for improved accuracy. Whole field separated stress values are obtained for the domains considered.

Geometrical digital twins of the as-built microstructure of three-leaf stone masonry walls with laser scanning

Research on irregular stone masonry walls is hampered by the lack of detailed geometrical models of their internal micro-structure, i.e. the shape and size of each stone and its position within the wall. Without such a geometric digital twin of walls tested in the laboratory, it is difficult to evaluate the accuracy of existing numerical simulation techniques. Here, we describe the generation of geometrical digital twins of three irregular stone masonry walls built in the laboratory. We labelled each stone manually and then obtained the geometry of the individual stones using a portable laser scanning device. With the same device we scanned the wall after the construction of each layer. We then registered the position of each stone in the layer. This paper outlines the methodology for the data acquisition and digital reconstruction and presents the datasets for the walls. The developed geometrical digital twins provide unique information regarding the micro-structure of constructed walls that is key for the development and validation of numerical simulation techniques for stone masonry.

Comparison of Near-Infrared Spectroscopy-Based Cerebral Autoregulatory Indices in Extremely Low Birth Weight Infants.

Premature infants are born with immature cerebral autoregulation function and are vulnerable to pressure passive cerebral circulation and subsequent brain injury. Measurements derived from near-infrared spectroscopy (NIRS) have enabled continuous assessment of cerebral vasoreactivity. Although NIRS has enabled a growing field of research, the lack of clear standardization in the field remains problematic. A major limitation of current literature is the absence of a comparative analysis of the different methodologies. To determine the relationship between NIRS-derived continuous indices of cerebral autoregulation in a cohort of extremely low birth weight (ELBW) infants. Premature infants of birth weight 401-1000 g were studied during the first 72 h of life. The cerebral oximetry index (COx), hemoglobin volume index (HVx), and tissue oxygenation heart rate reactivity index (TOHRx) were simultaneously calculated. The relationship between each of the indices was assessed with Pearson correlation. Fifty-eight infants with a median gestational age of 25.8 weeks and a median birth weight of 738 g were included. Intraventricular hemorrhage (IVH) was detected in 33% of individuals. COx and HVx demonstrated the highest degree of correlation, although the relationship was moderate at best (r = 0.543, p < 0.001). No correlation was found either between COx and TOHRx (r = 0.318, p < 0.015) or between HVx and TOHRx (r = 0.287, p < 0.029). No significant differences in these relationships were found with respect to IVH and no IVH in subgroup analysis. COx, HVx, and TOHRx are not numerically equivalent. Caution must be applied when interpreting or comparing results based on different methodologies for measuring cerebral autoregulation. Uniformity regarding data acquisition and analytical methodology are needed to firmly establish a gold standard for neonatal cerebral autoregulation monitoring.

Automatic defect detection in fiber-reinforced polymer matrix composites using thermographic vision data

The detection of internal defects, not visible to the naked eye from the outside of materials, using non-destructive testing (NDT) are increasingly requested by industrial processes. This study proposes a novel methodology for acquisition and processing of images from a thermographic camera using computer vision methods to test composite materials made of a polymer matrix reinforced with glass, carbon, and kevlar fibers. The image is acquired while cooling the sample, following a suggested procedure. The processing methodology is divided into three steps, image pre-processing, image processing, and data post-processing. In image preprocessing, filters are applied to improve image quality, and methods are proposed to segment and identify the region of interest. In image processing, a blob analysis method is suggested for defect identification, isolation and characterization. A data analysis method is proposed for the post-processing step to characterize the defects identified in the previous step. Samples with known defects in terms of size, geometry, and location were used to test the developed system. The system showed high performance, achieving 98% accuracy, and suitability for defect detection larger than 0.5 mm in thickness and 600 mm2 in area. The experimental results showed that the algorithm did not detect any false positives, and that the type of reinforcement used in the analyzed samples had no influence on the results. On the other hand, the depth of the delaminations had an influence on the pixel intensity contrast of the defect region, and its instant of maximum contrast. The lesser the depth of the defects detected, the higher the value of their intensity and the shorter the instant of maximum contrast.

Digital Forensics for E-IoT Devices in Smart Cities

With the global expansion of urban infrastructure and development of 5G communication technology, advanced information and communications technology has been applied to power systems and the use of smart grids has increased. Smart grid systems collect energy data using Internet-of-Things (IoT) devices, such as data concentrator units (DCUs) and smart meters, to effectively manage energy. Services and functions for energy management are being incorporated into home IoT devices. In this paper, the IoT for energy management in smart cities and smart homes is referred to as the E-IoT. Systems that use the E-IoT can efficiently manage data, but they present many potential security threats, because the E-IoT devices in such homes and enterprises are networked for energy management. Therefore, in this study, to identify vulnerabilities in the E-IoT device systems, digital forensics is applied to the E-IoT device systems. E-IoT devices supplied to Korean power systems were used to build a digital forensic test bed similar to actual E-IoT environments. For digital forensics application, E-IoT data acquisition and analysis methodology was proposed. The proposed methodology consisted of three methods—network packet data analysis, hardware interface analysis, and mobile device paired with E-IoT—which were applied to a DCU, smart meter, smart plug, smart heat controller, smart microwave, and smart monitoring system. On analyzing the user and system data acquired, artifacts such as the device name and energy consumption were derived. User accounts and passwords and energy-usage logs were obtained, indicating the possibility of leakage of personal information and the vulnerabilities of E-IoT devices.

The INOVMineral Project’s Contribution to Mineral Exploration—A WebGIS Integration and Visualization of Spectral and Geophysical Properties of the Aldeia LCT Pegmatite Spodumene Deposit

Due to the current energetic transition, new geological exploration technologies are needed to discover mineral deposits containing critical materials such as lithium (Li). The vast majority of European Li deposits are related to Li–Cs–Ta (LCT) pegmatites. A review of the literature indicates that conventional exploration campaigns are dominated by geochemical surveys and related exploration tools. However, other exploration techniques must be evaluated, namely, remote sensing (RS) and geophysics. This work presents the results of the INOVMINERAL4.0 project obtained through alternative approaches to traditional geochemistry that were gathered and integrated into a webGIS application. The specific objectives were to: (i) assess the potential of high-resolution elevation data; (ii) evaluate geophysical methods, particularly radiometry; (iii) establish a methodology for spectral data acquisition and build a spectral library; (iv) compare obtained spectra with Landsat 9 data for pegmatite identification; and (v) implement a user-friendly webGIS platform for data integration and visualization. Radiometric data acquisition using geophysical techniques effectively discriminated pegmatites from host rocks. The developed spectral library provides valuable insights for space-based exploration. Landsat 9 data accurately identified known LCT pegmatite targets compared with Landsat 8. The user-friendly webGIS platform facilitates data integration, visualization, and sharing, supporting potential users in similar exploration approaches.

Mapping near-surface structures in a geophysical test site using magnetic and electromagnetic induction gradients

We applied Magnetic Gradiometry (MG) and Electromagnetic Induction (EMI) techniques to map and differentiate the geophysical response of 17 buried structures at <2.50 m deep in the Teoloyucan Geophysical Test Site (TGTS). To enhance the response of near-surface structures, we include the Electromagnetic Induction Gradient (EMIG) and complement MG and EMI acquisition and processing methodologies. This includes, acquiring and adding together orthogonal-transect surveys, computing the EMIG and sectioning area maps into close-up maps. The orthogonal-transect surveys clearly show distinct distributions of the physical properties resulting from the acquisition direction, the orientation of the transects and from the orientation and heading of the sensors. Compared with EMI, EMIG provides remarkable results for apparent conductivity although limited for in-phase data. For MG and EMIG, close-up maps yield better results than area maps. Also, adding the orthogonal-transect survey grids gives better results, followed by the E−W transect surveys and then by the S−N transect surveys. Most of the mapped structures present low-medium MG values and medium-high EMIG values. The proposed methodology is consistent for concrete and air-cavity structures, and for structures made with various materials and complex geometries. In some cases, the structures do not have enough contrast with their surrounding terrain and in others, the anomalies are shifted in <1.50 m. In this study, we demonstrate the convenience of computing the EMIG and the benefits of applying the used methodology to retrieve near-surface features that would be overlook with common-practice methodologies.

DIGITAL MANAGEMENT FOR THE RESTORATION PROJECT. THE CASE OF THE TEMPLE OF VENUS IN BAIA

Abstract. The work that is presented here shows the results of the survey and material characterization campaign, performed through digital information acquisition and management processes of the Temple of Venus in Baia, in the framework of the activities by the Departments of Civil, Building, and Environmental Engineering of the University of Naples Federico II, in agreement with the Archaeological Park of Campi Flegrei. The artifact stands as a deeply interesting example of an architectural palimpsest, which - despite being involved by significant structural reinforcement interventions - presents interesting technical solutions, to be further investigated in their singularity and specificity. Unfortunately, it cannot be currently visited, until the execution of works to guarantee its complete and safe accessibility, also in terms of structural safety. The use of digital methodologies for data acquisition, in the integration of range and image-based survey techniques, and their semantic de-discretization has allowed systematizing the developed knowledge into models that can be easily queried and implemented over time, where both the geometric and the information component are indispensable for orienting and elaborating a conscious and articulate restoration project, as required by this building.