Acquisition Process Research Articles

Research on Detection Technology of Micro- Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process, this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm. The device detection model and data set are established based on Faster RCNN. Finally, the number of training was continuously optimized, and when the loss function of Faster RCNN converged, the identification result of the device was obtained.

What to consider at the development of educational programs and courses about next-generation cyber-physical systems?

Abstract We live in an age in which new things are emerging faster that their deep understanding. This statement in particularly applies to doing research and educating university students concerning next generation cyber-physical systems (NG-CPSs). The fast evolution of this system paradigm would have expected a rapid and comprehensive paradigmatic change in research and education concerning this family of systems. However, this has not happened yet. Seeking for a sufficing explanation, this paper reviews the current literature and makes an attempt to cast light on the most significant recent developments in the field of NG-CPSs. The main assumptions of the authors are that research and education should appear in harmony in academic knowledge acquisition and distribution processes, and that academic education of NG-CPSs should be organized and conducted according to a defendable future vision. Combining the results of a broadly-based study of the literature with prognostic critical thinking and personal experiences, this review-based position paper first discusses the current socio-techno-scientific environment, the involved stakeholders, and the demands and two approaches of truly systems-oriented education. Then, it concentrates on the recognized limitations of mono- and interdisciplinary research, and on supradisciplinary approach and transdisciplinary knowledge generation for NG-CPSs. As main contributions, the paper (i) identifies and analyzes the latest theoretical, engineering, and technological developments, (ii) reveals the major trends and their presumably significant implications, (iii) presents a number of thought-provoking findings and makes propositions about the desirable actions.

Image-based corn seed embryo orientation detection and adjustment for precision planting

Research has indicated that positioning a corn seed with its embryo side facing upward and its long axis perpendicular to a seeding row leads to the corn leaves growing perpendicular to the seeding row. This positioning results in an increased rate of light interception and yield for the crop. An identification and adjustment prototype was designed to rearrange corn seeds into the aforementioned seed orientation. The main parts are the teeth plate, V-shaped groove, photo sensor, camera, LED light, and pneumatic solenoid valve. When a corn seed enters the prototype, it will first be led to a leaning state, then go through an image acquisition and processing process, where the template matching method will be used to find a region of interest (ROI) on a corn seed image. The embryo side will be identified based on the average normalized saturation value in the ROI. The pneumatic solenoid valve will then be activated to adjust the seed accordingly to the targeted seed orientation. Trial results showed a success rate of 84 % and an average adjustment time of 1.438 s. The proposed method of embryo side direction identification and adjustment will contribute to the development of corn-oriented seeding technology.

INDONESIAN EFL LEARNERS AND SPEAKING ANXIETY: INSIGHTS FROM A META-SYNTHETIC ANALYSIS

This study presents a comprehensive meta-synthetic analysis of speaking anxiety among Indonesian English as a Foreign Language (EFL) learners. Speaking anxiety is a common challenge that significantly impacts the language acquisition process, affecting learners' confidence, performance, and overall proficiency. The meta-synthesis method was employed to integrate and interpret findings from multiple qualitative and quantitative studies conducted in the Indonesian context. The analysis reveals several key factors contributing to speaking anxiety, including fear of negative evaluation, lack of confidence in language proficiency, and cultural influences on communication styles. Additionally, the study highlights the role of educational environments and teacher-student dynamics in exacerbating or alleviating anxiety. The findings suggest that targeted interventions, such as supportive teaching practices, anxiety-reducing classroom activities, and the development of a positive learning environment, can effectively mitigate speaking anxiety. This meta-synthesis provides valuable insights for educators, policymakers, and researchers aiming to enhance EFL education in Indonesia by addressing the psychological barriers to effective language learning. By synthesizing diverse perspectives and empirical evidence, this study contributes to a deeper understanding of speaking anxiety in the Indonesian EFL context and offers practical recommendations for fostering a more conducive learning atmosphere for language learners.

Single-shot multi-sensitivity distributed acoustic sensing with hardware envelope retrieving acceleration

Distributed acoustic sensing (DAS) can virtualize a fiber optic cable into an ultra-dense seismic network, offering long-term seismic wave observing capability and high-fidelity waveform recording performance. In practical applications, DAS systems still face two main challenges. Firstly, the large amount of raw data brings a burden on storage and demodulation speed. Secondly, the fixed strain sensitivity of DAS limits the dynamic measurement range of the actual seismic signal. In this work, we present a single-shot multi-sensitivity distributed acoustic sensing method with hardware assistance. A hardware filtering module is utilized to achieve equivalent sampling results at a lower sampling rate, thereby reducing the volume of raw data and accelerating the acquisition and demodulation process. The average processing time for a single-sideband pulse detection can be reduced from 214.79 s to 9.83 s, resulting in approximately a 20-time reduction. Meanwhile, multiple sidebands pulse with different bandwidths is generated to enable multi-sensitivity detection under hardware filtering. The different ranges of strain events can be recovered through a modulated pulse with 20 MHz, 40 MHz, and 80 MHz bandwidths. The hardware-assistance multi-sensitivity DAS method offers a potential solution to complex environments and real-time detection applications.

Sleep deprivation increases the generalization of perceptual and concept-based fear: An fNIRS study

Insufficient sleep can initiate or exacerbate anxiety by triggering excessive fear generalization. In this study, a de novo paradigm was developed and used to examine the neural mechanisms governing the effects of sleep deprivation on processing perceptual and concept-based fear generalizations. A between-subject design was adopted, wherein a control group (who had a typical night's sleep) and a one-night sleep deprivation group completed a fear acquisition task at 9:00 PM on the first day and underwent a generalization test the following morning at 7:00 AM. In the fear acquisition task, navy blue and olive green were used as perceptual cues (P+ and P−, respectively), while animals and furniture items were used as conceptual cues (C+ and C−, respectively). Generalization was tested for four novel generalized categories (C+P+, C+P−, C−P+, and C−P−). Shock expectancy ratings, skin conductance responses, and functional near-infrared spectroscopy were recorded during the fear acquisition and generalization processes. Compared with the group who had a typical night's sleep, the sleep deprived group showed higher shock expectancy ratings (especially for P+ and C−), increased oxygenated hemoglobin in the dorsolateral prefrontal cortex, and increased activation in the triangular inferior frontal gyrus during the generalization test. These findings suggest that sleep deprivation increases the generalization of threat memories, thus providing insights into the overgeneralization characteristics of anxiety and fear-related disorders.

The Involvement of the Serotonergic System in Ketamine and Fluoxetine Combination-induced Cognitive Impairments in Mice.

Glutamatergic N-methyl-D-aspartate (NMDA) receptors play vital roles in memory formation. Changes in the activity of these receptors influence memory processes. Ketamine is a noncompetitive NMDA receptor antagonist drug with promising mood-altering and pain-reducing effects in low doses. These effects are believed to be related to altered serotonergic transmission. The present study investigated the involvement of the serotonergic system in low-dose ketamine administrations' effects on memory acquisition, consolidation, and retrieval processes. Sixty-four male BALB/c mice were used in this experiment and separated into 8t groups. Mice were treated subchronically with a selective serotonin reuptake inhibitor, fluoxetine, and a serotonin depletion agent, p-chlorophenylalanine (pCPA). A serotonin antagonist, methiothepin, and ketamine were acutely administered 60 minutes before or after the behavioral tests. A passive avoidance (PA) test measured emotional memory acquisition, consolidation, and retrieval processes. Hippocampi malondialdehyde (MDA) levels were analyzed, and histopathological examinations were performed. Ketamine alone did not significantly affect memory encoding processes in the PA test, while the ketamine-fluoxetine combination disrupted memory consolidation. Fluoxetine negatively affected the memory acquisition process, which was normalized during the consolidation and retrieval trials. Drug applications did not significantly alter hippocampal MDA levels. In all ketamine-applied groups, histopathologic alterations were evident. Low-dose ketamine administration induces neurodegeneration, and it also impairs memory functions when combined with fluoxetine, indicating increased serotonergic transmission may be involved in the memory-impairing and neurotoxic effects of ketamine.

Digital Test and Evaluation

The Department of Defense (DoD) is undergoing a Digital Transformation (DT), which will affect all acquisition processes in some way. As documents are replaced with digital models, the existing processes will be required to facilitate emerging concepts like the Rapid Acquisition Process (RAP) and Test and Evaluation as a Continuum (TEaaC). Traditional Test and Evaluation (T&E) will be augmented by automated digital testing in all program lifecycle phases. Digital testing will rely on many forms of digital models, including Model-Based Systems Engineering (MBSE), linked together via a digital thread and an Authoritative Source of Truth (ASOT). Validation of such digital artifacts will be paramount in assuring system developers and test engineers use accurate and credible models for decisions. This paper proposes a model-based validation technique, details a proof-of-concept, and describes how it may enable future acquisition.

Rockfall susceptibility analysis of the “San Michele Arcangelo” historic trail (Central Italy) based on virtual outcrops and multiple propagation models

The “San Michele Arcangelo” historic trail (Aurunci Regional Park, central Italy), located along the southern slope of Mt. Altino, which is highly prone to rockfalls, is hiked every year by thousands of faithful on pilgrimage who are exposed to such kinds of instabilities. To contribute to a better understanding of the condition and evolution of such phenomena, providing a susceptibility scenario able to support the adoption of mitigation measures, a specific analysis was completed based on field and literature data. Three-dimensional virtual outcrop models were obtained through both photogrammetric and iPad Pro LiDAR surveys to derive geomechanical features of outcropping rocks and estimate potential detaching block volume. Possible mechanisms of detachment were then analyzed using the Markland test method. Susceptibility to rockfall propagation and block deposition was analyzed using GIS processing and rockfall propagation simulations based on deterministic/stochastic models. Such models consist of a combination of a deterministic algorithm able to simulate the physics of rockfall movement during propagation and a stochastic treatment of input parameters based on random sampling within defined interval. Two different rockfall simulations were compared in this study using the Rockyfor3D™ (ecorisQ Association) software and the newly developed RocFall3© (RocScience) code. The use of iPad Pro LiDAR survey provides accurate and high-resolution point clouds, with high speed of acquisition and real-time processing of data. Discontinuity sets identified from 3D models have represented the input for kinematic analyses of slopes, allowing to identify potential failure mechanisms among wedge and planar sliding or toppling. Rockfall simulations indicate the potential for rock blocks propagation and deposition over the whole study area. Comparing the results of rockfall simulations with the geomorphological map of the area it is evident the control exerted by the hydrographic network on rockfall propagation as demonstrated by the presence of screes and slope deposits along the main channels. Understanding the potential susceptibility to both propagation and deposition of rock blocks along the trail provides useful indication for the assessment of appropriate mitigation measures to realize for the safer touristic fruition of the site.

A composite spread spectrum sequence for underwater acoustic signal acquisition

AbstractSpread spectrum technology has been widely employed for positioning and communicating with autonomous underwater vehicles (AUVs), but conventional spread spectrum sequences lack confidentiality and reliability in UWA channel. Considering the limitations of conventional sequences and the characteristics of underwater acoustic (UWA) channel, a composite chaotic orthogonal sequence (CCOS) based on the UWA channel is proposed. The confidentiality of the CCOS is superior to that of the m‐sequence, while the autocorrelation performance of the CCOS is superior to that of the orthogonal sequence. Moreover, the CCOS can compensate for the imbalance of logistic chaotic sequence when assigned certain initial values. Acquisition is a crucial component of accessing the spread spectrum signal; therefore, the acquisition performance indicates the applicability of the CCOS. The source–target model is established to simulate communication with an underwater moving target. To simulate the acquisition process, a parallel algorithm based on fast Fourier transform is adopted, and the entire simulation process is completed based on the BELLHOP ray acoustic model. Through data processing, the Doppler shift error is less than half of the frequency‐search element. Furthermore, the acquisition probabilities of the CCOS with different numbers of bits are over 90%, which demonstrates the reliability of the CCOS.

Practicalities of Incorporating 3D Laser Scanning with BIM in Live Construction Projects: A Case Study

The integration of laser scanning technology and Building Information Modelling (BIM) processes offers a transformative approach to managing the complexities in live construction projects. This paper aims to explore the significant impacts of incorporating laser scanning and BIM on construction projects in terms of as-built models, information management, and overall project performance utilising case study analysis of a building that was not BIM-based. The research scope is defined by the need to investigate the integration of laser scanning and BIM in live construction projects. It details the data acquisition process, challenges encountered due to site obstructions, and the methodologies employed for spatial modelling procedures. Key findings reveal that such integration can significantly enhance the accuracy of data collection and improve project outcomes. Results also identify the need for specialised equipment and skills for the effective implementation of such integrations. The research concludes by offering a practical approach to enhancing construction processes, from design to maintenance. This paper contributes to the body of knowledge by providing a detailed analysis of the practical application of laser scanning and BIM in a live construction project, offering insights into the benefits, challenges, and future directions for integrating these technologies in the construction industry.

Fourier Domain Robust Denoising Decomposition and Adaptive Patch MRI Reconstruction.

The sparsity of the Fourier transform domain has been applied to magnetic resonance imaging (MRI) reconstruction in k -space. Although unsupervised adaptive patch optimization methods have shown promise compared to data-driven-based supervised methods, the following challenges exist in MRI reconstruction: 1) in previous k -space MRI reconstruction tasks, MRI with noise interference in the acquisition process is rarely considered. 2) Differences in transform domains should be resolved to achieve the high-quality reconstruction of low undersampled MRI data. 3) Robust patch dictionary learning problems are usually nonconvex and NP-hard, and alternate minimization methods are often computationally expensive. In this article, we propose a method for Fourier domain robust denoising decomposition and adaptive patch MRI reconstruction (DDAPR). DDAPR is a two-step optimization method for MRI reconstruction in the presence of noise and low undersampled data. It includes the low-rank and sparse denoising reconstruction model (LSDRM) and the robust dictionary learning reconstruction model (RDLRM). In the first step, we propose LSDRM for different domains. For the optimization solution, the proximal gradient method is used to optimize LSDRM by singular value decomposition and soft threshold algorithms. In the second step, we propose RDLRM, which is an effective adaptive patch method by introducing a low-rank and sparse penalty adaptive patch dictionary and using a sparse rank-one matrix to approximate the undersampled data. Then, the block coordinate descent (BCD) method is used to optimize the variables. The BCD optimization process involves valid closed-form solutions. Extensive numerical experiments show that the proposed method has a better performance than previous methods in image reconstruction based on compressed sensing or deep learning.

P‐54: XAI Models for Efficient Analysis of Temperature and Power Consumption in High‐Brightness Panels and Modules

Predicting temperature and power consumption in panels and modules under high brightness has long been a daunting and time‐intensive task, often requiring over a week of simulation work. Addressing this challenge, our study introduces a novel machine learning framework, bifurcated into panel and module stages, to streamline the data acquisition process. A key innovation in our approach is the use of symbolic regression to overcome the limitations posed by the reliance on real‐world measurements for power consumption and heat generation. Employing SPICE simulations, we estimated the current density within the panel, with subsequent validation through Explainable AI (XAI) analysis. This revealed a significant correlation between current density and panel heat generation. Our research also revisits the traditionally neglected areas outside the panel's center, uncovering their impact on heat generation. The layered complexity in modules, previously a barrier to physical sample creation and measurement, was navigated using simulation. XAI insights demonstrated the crucial roles of graphite and metal layers in heat dissipation, and the drive IC as a primary heat source. This integrated approach of real and computational data in machine learning significantly reduced the analysis timeframe from a week to mere minutes, marking a breakthrough in predictive accuracy and efficiency for high‐brightness scenarios.

INDEPENDENT LEARNING OF THE RUSSIAN LANGUAGE: STRATEGIES, MOTIVATION, AND EFFECTIVENESS IN THE CONTEXT OF MODERN EDUCATIONAL TECHNOLOGIES

Language learning is a complex and rewarding journey that requires dedication, persistence, and effective strategies. In the context of learning the Russian language independently, learners face unique challenges and opportunities. This article explores various strategies, discusses the role of motivation, and evaluates the effectiveness of modern educational technologies in facilitating independent learning of Russian. By understanding these key aspects, learners can enhance their language acquisition process and achieve proficiency in Russian.

Duties of Board in Maximizing Shareholder Value During Acquisitions: A Comparative Analysis Between UK, US and China

Focusing on the legal and strategic frameworks in China, the United States (especially Delaware), and the United Kingdom, this paper provides a comparative analysis of the roles and responsibilities of corporate boards in maximizing shareholder value in the acquisition process. It explores the varied legal frameworks to set board responsibilities, the strategic roles of boards in decision-making, and the challenges in balancing the interests of boards and shareholders. This paper further discusses the importance of conflicts of interest between boards and shareholders, transparent communication, and ethical standards, and how these factors affect the realization of value in acquisition decisions. It highlights regional case studies to illustrate the practical application of these principles in different legal and cultural contexts, emphasizing the need for boards to navigate complex regulatory landscapes and shareholder expectations to drive successful acquisition outcomes. This entails adopting practices like enhanced due diligence, independent oversight, and transparent communication to navigate the complexities of global corporate governance effectively.

Semantics of INTO in Motion Events and Its Vietnamese Equivalents

In the English language, despite its affinity with other spatial prepositions, the preposition into demonstrates versatility by conveying spatial as well as non-spatial meanings contingent upon contextual factors. Throughout the process of English language acquisition and in the context of translation between English and Vietnamese, a diverse array of interpretations of into has been articulated within distinct discursive settings. The primary objective of examining the corpus of into used in The Complete Sherlock Holmes is to elucidate the nuances and distinctions in its semantic usage and subsequently conduct a comparative analysis with its Vietnamese equivalents.

Complexity and modification of the bull sperm glycocalyx during epididymal maturation.

Mammalian spermatozoa have a surface covered with glycocalyx, consisting of heterogeneous glycoproteins and glycolipids. This complexity arises from diverse monosaccharides, distinct linkages, various isomeric glycans, branching levels, and saccharide sequences. The glycocalyx is synthesized by spermatozoa developing in the testis, and its subsequent alterations during their transit through the epididymis are a critical process for the sperm acquisition of fertilizing ability. In this study, we performed detailed analysis of the glycocalyx on the sperm surface of bull spermatozoa in relation to individual parts of the epididymis using a wide range (24) of lectins with specific carbohydrate binding preferences. Fluorescence analysis of intact sperm isolated from the bull epididymides was complemented by Western blot detection of protein extracts from the sperm plasma membrane fractions. Our experimental results revealed predominant sequential modification of bull sperm glycans with N-acetyllactosamine (LacNAc), followed by subsequent sialylation and fucosylation in a highly specific manner. Additionally, variations in the lectin detection on the sperm surface may indicate the acquisition or release of glycans or glycoproteins. Our study is the first to provide a complex analysis of the bull sperm glycocalyx modification during epididymal maturation.

Enhancing endoscopic scene reconstruction with color-aware inverse rendering through neural SDF and radiance fields.

Virtual surgical training is crucial for enhancing minimally invasive surgical skills. Traditional geometric reconstruction methods based on medical CT/MRI images often fall short in providing color information, which is typically generated through pseudo-coloring or artistic rendering. To simultaneously reconstruct both the geometric shape and appearance information of organs, we propose a novel organ model reconstruction network called Endoscope-NeSRF. This network jointly leverages neural radiance fields and Signed Distance Function (SDF) to reconstruct a textured geometric model of the organ of interest from multi-view photometric images acquired by an endoscope. The prior knowledge of the inverse correlation between the distance from the light source to the object and the radiance improves the real physical properties of the organ. The dilated mask further refines the appearance and geometry at the organ's edges. We also proposed a highlight adaptive optimization strategy to remove highlights caused by the light source during the acquisition process, thereby preventing the reconstruction results in areas previously affected by highlights from turning white. Finally, the real-time realistic rendering of the organ model is achieved by combining the inverse rendering and Bidirectional Reflectance Distribution Function (BRDF) rendering methods. Experimental results show that our method closely matches the Instant-NGP method in appearance reconstruction, outperforming other state-of-the-art methods, and stands as the superior method in terms of geometric reconstruction. Our method obtained a detailed geometric model and realistic appearance, providing a realistic visual sense for virtual surgical simulation, which is important for medical training.

‘If I Knew How to Speak English…’: How language shapes refugee mothers’ perceptions of past, present, and future in Canada

Abstract What role does language learning play in refugees’ memory-construction on the one hand, and imagining of the future self on the other? Using a temporal perspective on migration, I extend scholarship examining the role of language in the space-time continuum of resettlement. With three waves of semi-structured interviews with twenty Syrian refugee mothers (N = 60) who have recently arrived in Canada, this article examines how their experiences with time and future projections are influenced by their experiences of language learning in the host country. First, mothers’ lack of English proficiency and struggle to learn leads to a sense of nostalgia towards the past, where their proficiency in Arabic is associated with past feelings of comfort, security, and mastery. In addition, mothers find themselves ‘stuck’ in the present, where multiple structural barriers (e.g., absence of extended kin; limited government support) and individual challenges (e.g., health issues; having children with disability) significantly slow down their language acquisition process and prevent them from achieving other goals. This leads to a clear conflict between government expectations for the long-term future and the mothers’ immediate priorities. Finally, despite those government temporal expectations building on newcomers’ language acquisition, mothers do not want to envision the future due to past experiences of uncertainty, belief in divine control, and a foreclosure of the future. This article demonstrates the ways in which language, space, and time co-construct notions of the future, and a sense of potential ‘stuckness,’ well beyond the temporal limits of intensive state intervention in refugee lives.

3DFin: a software for automated 3D forest inventories from terrestrial point clouds

Abstract Accurate and efficient forest inventories are essential for effective forest management and conservation. The advent of ground-based remote sensing has revolutionized the data acquisition process, enabling detailed and precise 3D measurements of forested areas. Several algorithms and methods have been developed in the last years to automatically derive tree metrics from such terrestrial/ground-based point clouds. However, few attempts have been made to make these automatic tree metrics algorithms accessible to wider audiences by producing software solutions that implement these methods. To fill this major gap, we have developed 3DFin, a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds. 3DFin empowers users to automatically compute key forest inventory parameters, including tree Total Height, Diameter at Breast Height (DBH), and tree location. To enhance its user-friendliness, the program is open-access, cross-platform, and available as a plugin in CloudCompare and QGIS as well as a standalone in Windows. 3DFin capabilities have been tested with Terrestrial Laser Scanning, Mobile Laser Scanning, and terrestrial photogrammetric point clouds from public repositories across different forest conditions, achieving nearly full completeness and correctness in tree mapping and highly accurate DBH estimations (root mean squared error <2 cm, bias <1 cm) in most scenarios. In these tests, 3DFin demonstrated remarkable efficiency, with processing times ranging from 2 to 7 min per plot. The software is freely available at: https://github.com/3DFin/3DFin.