Acquisition Process Research Articles

Unveiling AI's Accent in Spoken English Education

Viewed against the backdrop of the technological landscape, artificial intelligence has become the shining point of the 21st century. In recent years, AI has been pervasively applied into various realms in modern society, among which the incorporation into spoken English education has become one of the most heated topics. To navigate the application of AI in spoken English learning, this paper evaluates the effectiveness based on the Interaction Hypothesis proposed by Michael Long. To be specific, this research focuses on the comprehensive application of AI in the realm of spoken English acquisition, underscoring why AI language models have gained in popularity in innumerable apps concerning language learning and how AI serves as a double-edged sword in spoken English education. By illustrating the language acquisition process, a strong connection between the Interaction Hypothesis and AI-based spoken English learning is constructed and clarified. Through all-round evaluation and analysis, the article concludes that despite challenges, AI has not only acted as a knowledgeable mentor for individual learners,but also provided an effective driving force for the innovation and improvement of spoken English education. In the future, more advancements concerning human-like interactions and immersive learning environment are likely to emerge.

Adaptive sampling strategy for Fourier single-pixel imaging

Fourier single-pixel imaging is a technique that utilizes Fourier bases as illumination patterns to capture images. While the use of orthogonal bases enhances the imaging quality compared with other methods of single pixel imaging, the slow imaging speed has hindered its broader application. Acquiring only low-frequency Fourier coefficients enables capturing a significant portion of image information at low sampling ratio, thereby enhancing imaging speed. Nevertheless, truncating frequencies results in a loss of image detail and a decrease in picture quality. To address this challenge, this paper introduces an adaptive sampling strategy for Fourier single-pixel imaging, allowing for customized sampling distributions for each specific object. The sampling distribution is dynamically generated based on the acquired Fourier coefficients during the acquisition process, eliminating the need for prior information. Through extensive simulations and experimental analysis, this approach has demonstrated its ability to capture more complex image details at equivalent sampling ratios.

Optimization of the FPM iterative process based on bright-field spectral overlap rate analysis.

Fourier ptychographic microscopy (FPM) reconstructs high-resolution images through multiple iterations on a large number of sub-images at different angles, a process that is time-consuming. For a long time, various methods for optimizing the efficiency of FPM based on the acquisition process and algorithms have been proposed. However, there has been no specific analysis of the impact that the sub-images involved in the reconstruction have on the final result. In this Letter, we conduct the first, to our knowledge, analysis of the impact of a single sub-image on the reconstruction result of a high-resolution image in different numbers of iterations and obtain a curve depicting the change in image quality after the sub-images are involved in the reconstruction in different cycles. By analyzing this curve, the sub-images that exert a negative impact on the resulting image are exported along with their corresponding LED positions. On this basis, we propose the concept of bright-field spectral overlap ratio to distinguish whether the sub-images have a positive impact on the reconstruction results under different acquisition conditions and remove the sub-images that have a negative impact on the results during the iterative process. Both simulation and real experimental results demonstrate that our algorithm can reduce the reconstruction time while maintaining image quality. Moreover, it can be combined with other methods to improve reconstruction efficiency.

Balancing Development and Community Rights: Legal and Policy Frameworks for Land Conflict Resolution in Batam, Indonesia

The rapid development of Batam, Indonesia, as a Special Economic Zone (SEZ) has led to significant land conflicts between the state, developers, and local communities. The existing legal and policy frameworks have been criticized for prioritizing economic growth over community rights, leading to forced evictions, inadequate compensation, and social unrest. This study aims to analyze the legal and policy frameworks governing land conflict resolution in Batam and propose recommendations for balancing development and community rights. The research employs a qualitative approach, combining legal and policy analysis with empirical data collected through interviews with key stakeholders, including government officials, community leaders, and legal experts. The study examines relevant laws, regulations, and policies related to land acquisition, compensation, and dispute resolution in Batam. The findings reveal several shortcomings in the current legal and policy frameworks, including a lack of transparency and participation in decision-making processes, inadequate compensation mechanisms, and limited access to justice for affected communities. The study also highlights the power imbalance between the state, developers, and communities, which often leads to the marginalization of community rights. The study concludes that balancing development and community rights in Batam requires a comprehensive reform of the legal and policy frameworks. The recommendations include strengthening community participation in decision-making processes, ensuring fair and adequate compensation, and improving access to justice for affected communities. The study also emphasizes the need for greater transparency and accountability in land acquisition and development processes.

The hawk eye scan: Halyomorpha halys detection relying on aerial tele photos and neural networks

This paper faces the challenge of monitoring the Brown Marmorated Stink Bug (H.halys) (Halyomorpha halys) within orchards, utilizing drones, and computer vision. H.halys is an invasive species originating from East Asia, that is extremely polyphagous and poses a significant threat to various crops. Our first contribution is a drone navigation protocol, which ensures risk-free drone flights in cluttered orchard environments, preserving image quality and avoiding obstacles. We then create a pioneering H.halys dataset consisting of aerial telephotos captured in the field autonomously by the drone. The dataset allows the development and evaluation for the first time of multiple ML models for H.halys detection in the field. We trained YOLOv5, YOLOv8, RetinaNet, and Faster-RCNN models using different learning methodologies, exploiting different percentages of images without the bug, and using different slicing procedures for the images. The Medium YOLOv5 model trained with all images containing a bug detects the largest number of H.halys on the testing set and overall performs the best, while RetinaNet and Faster-RCNN provide the best trade-off between precision and recall. Models vary in their ability to handle occluded H.halys and bug-free images, which are common since the presence of the bug cannot be predicted before capturing a photo. These results show promising potential for automating H.halys monitoring, despite the image complexity and the early dataset stage. Our work marks a significant step towards enhancing smart agriculture practices due to the simplicity of the data acquisition process and the off-the-shelf hardware selection.

DNTFE-Net: Distant Neighboring-Temporal Feature Enhancement Network for side scan sonar small object detection

Side-scan sonar small target detection becomes fundamental work for side-scan sonar applications which holds vital importance for marine engineering, maritime military, and so on. However, existing algorithms suffer from performance degradation and poor portability when sonar data is limited. We find that benefit from the strong temporal correlation of sonar images, background information from the long-term images and foreground information from the short-term images can be used to enhance small targets in the current image. To fully utilize these two cues, this paper presents the Distant Neighboring-Temporal Feature Enhancement Network (DNTFE-Net) for detecting small objects in sonar images. The algorithm is based on the signal acquisition process of sonar devices, which is considered a continuous time sequence. Firstly, a flexible foreground feature alignment module is developed to align the foreground information between neighboring signals and the current signal so that foreground information can be used to enhance small target features. Then, we propose a background comparison module for selecting distant-temporal signals that provide the most contextual information in order to improve the ability of migrating to different domains. Finally, a foreground background feature amalgamation module is designed to fuse neighboring and distant-temporal signals for enhancing small target features. Furthermore, real sonar data is collected to compose the real continuous sonar dataset. Our DNTFE-Net exhibits superior recognition performance on the real sonar dataset compared to other popular methods, with an improvement of at least 27%. Our algorithms are publicly available at https://github.com/zbyhnu/DNTFE-Net.git.

ZOLTÁN DÖRNYEI’S IMPACT ON LANGUAGE ACQUISITION AND EDUCATION STUDIES: A SCOPUS-BASED BIBLIOMETRIC STUDY (1991-2023)

Previous literature reviews and bibliometric studies have paid much attention to the scientific outputs of Zoltán Dörnyei, a leading figure in the field of second language acquisition and education. However, little attention has been paid to his scholarly impact. This study aims to analyze all publications citing his 87 works in Scopus. Bibliometrix, a comprehensive bibliometric tool, was run to analyze 7,621 documents published in 1,970 sources by 9,226 authors of 2,652 institutions across 99 countries between 1991 and 2023. The analyses include the publication trend, research contributors, including authors, document elements including keywords, collaboration networks of research contributors, shared references, and frequently used keywords. Although most documents were written in English in the fields of social sciences as well as arts and humanities, his impact could be observed across other subject areas and languages. Further discussion elaborated on four major themes of the publications, including motivation, L2 motivational self-system, language learning, and positive psychology. This study could provide researchers and educators with valuable insights into understanding and improving language acquisition processes based on Dörnyei’s incalculable intellectual contribution. As this study focused on quantitative citation analysis, future research on how Dörnyei’s works were cited could provide deeper insights into his impact.

Droplet size, spray structure and droplet velocity mapping in hollow cone sprays using SLIPI-based techniques

The Structured Laser Illumination Planar Imaging (SLIPI) technique received significant attention in recent years due to notable improvements in the measurement of droplet size and the visualization of internal structure in both dilute and dense sprays. However, despite the improvements, the technique remains limited to these microscopic characteristics of the spray, primarily due to the complexity of the optical setup and image acquisition process. In this article, the capabilities of the SLIPI application are exploited, specifically employing the 3p-SLIPI-LIF/Mie (SLSD), and combined application of 1p-SLIPI-LIF & PIV (1p-SLIPI-PIV) for mapping of both Sauter Mean Diameter (SMD) and mean droplet velocity in combination with detailed spray structure analysis. The measurements were carried out in hollow cone sprays operated at the range of injection pressures from 25 to 107 bar. The results obtained using the SLIPI techniques were compared with the Phase Doppler Anemometry (PDA) measurements showing good agreement for both the SMD and mean droplet velocity. The significant improvements in the important microscopic characteristics (droplet size & velocity) and visualization of the internal structure in a wider flow field of hollow cone sprays allowed detailed analysis and understanding of the dispersed phase flow owing to the suppression of diffused light resulting from the multiple scattering effects.

Enhancing EFL Learners’ Communicative Skills through Dramatisation of Lessons

Dramatisation breathes vitality into the realm of language learning, transcending mere vocabulary to engage the entire learner - their body, mind, emotions, and senses. This research explores the integration of drama within language instruction and its profound impact on students' speaking proficiency. Studies consistently highlight that enacting lessons not only bolsters fluency but also instills a sense of confidence in learners. As students step into various roles and articulate lines, their language skills undergo marked improvement, rendering their speech more natural and expressive. This newfound confidence serves as a catalyst, empowering students to actively participate in classroom discourse and embrace English communication with enthusiasm. Even for those initially hesitant to voice their thoughts, the immersive experience of dramatisation provides a safe haven. The playful nature of role-play alleviates apprehensions, enabling even the most reserved learners to find their voice and connect with their peers on a deeper level. Furthermore, the empathetic exploration of diverse perspectives inherent in dramatisation enriches students' spoken English with nuance and emotion. Beyond immediate language proficiency, dramatised lessons foster positive attitudes towards learning, injecting an element of enjoyment and excitement into the language acquisition process. By engaging in creative expression, learners not only acquire language skills but also develop a genuine passion for linguistic exploration. In essence, the integration of dramatisation into language instruction serves as a powerful tool for guiding students towards achieving proficiency in a language. Its multifaceted benefits extend beyond the classroom, shaping learners into confident, expressive communicators with a profound appreciation for the intricacies of language and culture.

Developmental process of the understanding of linguistic register in children: A comparison of typically developing children, autistic children, and children with Williams syndrome.

Although the developmental process of linguistic register-the appropriate manner of speech as determined by the listener and social situation-has been gradually clarified in typically developing (TD) children, research on the mechanism and developmental process of register acquisition in atypically developing children are insufficient. This study compared the developmental process of understanding linguistic register among TD children, autistic children, and those with Williams syndrome (WS), and examined the contributions of social cognition and motivation to the acquisition of linguistic register. Two experiments were designed to assess the recognition of which linguistic register to use when communicating with different listeners and of the listener's feelings according to the speakers' use of register. The results revealed that the process of understanding register-listener associations was nearly identical among all groups of children and their understanding improved with age. Conversely, their understanding of the effect of register selection on the listener's feelings varied. Importantly, as TD children mature, they become aware that adult listeners may feel negatively when spoken to in an inappropriate register, whereas autistic children and those with WS do not exhibit the same awareness. Thus, our results suggest that atypical social cognition and motivation do not disturb the understanding of register-listener associations. However, social cognition and motivation play important roles in understanding the effect of register selection on the listener's feelings. These findings provide a significant contribution to clarifying the mechanism of linguistic register acquisition.

An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques

Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.

Parallelisms between Verb–Particle Constructions in English and Verb–Verb Compounds in Japanese: Evidence from Acquisition Research

This study shows two parallelisms between (i) the acquisition process of English verb–particle constructions (VPCs) by children in the process of acquiring English as a native language (henceforth ENL children) and (ii) that of Japanese verb–verb compounds (VVCs) by children in the process of acquiring Japanese as a native language (henceforth JNL children) using the CHILDES database. First, both JNL and ENL children produce creative N–N compounds and complex predicates during the same period, in line with the proposal by Snyder that the Compounding Parameter (TCP) is set to positive for both Japanese and English languages. Second, particles or verbs which are used to represent the path of motion in English VPCs and Japanese VVCs are produced before the VPCs and VVCs they are used in because complex predicates are created by the combination of two or more constituents, such as verbs and particles. Thus, our findings corroborate the proposal that Japanese is a [+TCP] language and suggest that Japanese makes use of VVCs instead of VPCs. Furthermore, this parallelism observed among ENL and JNL children in the acquisition process of creative N–N compounds and VPCs/VVCs, respectively, suggests that English VPCs and Japanese VVCs are related expressions in a grammatical sense. This in turn implies that VPCs and VVCs are connected by more than their semantics; indeed, it implies that they are realizational variations of the same abstract linguistic structure.

Simulation‐Training‐Based Deep Learning Approach to Microscopic Ghost Imaging

Herein, deep learning‐ghost imaging (DLGI) based on a digital micromirror device is realized to avoid the difficulties of a charge‐coupled device (CCD) scientific camera being unable to obtain the sample images in extremely weak illumination conditions and to solve the problem of the inverse relationship between imaging quality and imaging time in practical applications. Deep learning for computational ghost imaging typically requires the collection of a large set of labeled experimental data to train a neural network. Herein, we demonstrate that a practically usable neural network can be prepared based on the simulation results. The acquisition results of the CCD scientific camera and the simulation results with low sampling are used as the training set (1000 observations) and we can complete the data acquisition process within one hour. The results show that the proposed DLGI method can be used to significantly improve the quality of the reconstructed images when the sampling rate is 60%. This method also reduces the imaging time and the memory usage, while simultaneously improving the imaging quality. The imaging results of the proposed DLGI method have great significance for application in clinical diagnosis.

Interpretation of subsurface layers using the Wenner configuration geoelectric method and geochemical tests: Case study at Muara Fajar landfill – Rumbai, Pekanbaru

The need for water daily is a big problem for people living around Muara Fajar landfill – Rumbai, Pekanbaru. This study aims to identify the depth of subsurface structures around the Muara Fajar landfill area using the Wenner configuration dielectric method and determine the water quality in the community wells around the landfill site. The data obtained in the acquisition process are used to calculate the apparent resistivity value at each measurement point and analyzed quantitatively and qualitatively. Then proceed with the inversion process using the progress program so that obtained resistivity value and layer thickness at each point of measurement. The results of measurement in track-1 get a range of resistivity values ranging from 62.9 – 171 ohm-meters and track-2 from 32.4 – 70.6 ohm-meters. Underground water taken is residents' well water in the research area as many as 5 sample points using GPS. This underground water sample will be tested with parameters such as pH, TDS, BOD, COD, NH3, and turbidity. Underground water cannot be consumed from the results of the water quality test with the parameters mentioned at the beginning if referring to the Regulation of the Minister of Health of the Republic of Indonesia because the pH obtained in all underground water samples is < 6.5 or acidic, the value of the BOD, COD, NH3, and turbidity parameters, the values are by the quality standard.

Research on flexible piezoresistive sensors based on nanocomposite materials

As a core component, sensors play a crucial role in fields such as the Internet of Things, biomedicine, and artificial intelligence by converting physical information into electrical signals. With technological advancements, the demand for improved sensor performance and environmental adaptability has been increasing, driving progress in advanced materials, new structures, and refined processing technologies to enhance the efficiency of converting pressure signals into electrical signals. Flexible piezoresistive sensors are widely used due to their high sensitivity, good linear response, and fast response time. The performance of these sensors heavily depends on the microstructure of the materials, and traditional detection methods struggle to capture their dynamic changes. Therefore, developing in-situ characterization techniques to monitor changes in material structure and chemical properties in real-time is crucial for optimizing material performance. Additionally, integrated sensor systems that study signal acquisition, circuit interfaces, and signal processing, combined with artificial intelligence algorithms for data processing and analysis, not only improve system efficiency but also broaden their application range.

Causes of Interlanguage Fossilization and the Coping Strategies

Interlanguage fossilization is an important topic in the study of second language acquisition, which involves changes and solidifications in the language acquisition process that learners develop while learning a second language. The purpose of this paper is to explore the concept of interlanguage fossilization, its causes and its implications in language teaching and education. This paper firstly introduces the concepts of interlanguage language and fossilization, and then focuses on the causes of this phenomenon, including both intra-individual and external environmental factors. Then, the paper puts the impact of the interlanguage fossilization phenomenon in language teaching and education, and how to prevent or slow down the occurrence of it through effective teaching strategies into discussion. Through an in-depth study of interlanguage fossilization, the language acquisition process of learners could be better understood, thus contributing more effectively to the development of language teaching and education.

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Plasmid-mediated acquisition and chromosomal integration of blaCTX-M-14 in a subclade of Escherichia coli ST131-H30 clade C1.

Escherichia coli ST131 is a multidrug-resistant lineage associated with the global spread of extended-spectrum β-lactamase-producing organisms. Particularly, ST131 clade C1 is the most predominant clade in Japan, harboring blaCTX-M-14 at a high frequency. However, the process of resistance gene acquisition and spread remains unclear. Here, we performed whole-genome sequencing of 19 E. coli strains belonging to 12 STs and 12 fimH types collected between 1997 and 2016. Additionally, we analyzed the full-length genome sequences of 96 ST131-H30 clade C0 and C1 strains, including those obtained from this study and those registered in public databases, to understand how ST131 clade C1 acquired and spread blaCTX-M-14. We detected conjugative IncFII plasmids and IncB/O/K/Z plasmids carrying blaCTX-M-14 in diverse genetic lineages of E. coli strains from the 1990s to the 2010s, suggesting that these plasmids played an important role in the spread of blaCTX-M-14. Molecular phylogenetic and molecular clock analyses of the 96 ST131-H30 clade C0 and C1 strains identified 8 subclades. Strains harboring blaCTX-M-14 were clustered in subclades 4 and 5, and it was inferred that clade C1 acquired blaCTX-M-14 around 1993. All 34 strains belonging to subclade 5 possessed blaCTX-M-14 with ISEcp1 upstream at the same chromosomal position, indicating their common ancestor acquired blaCTX-M-14 in a single ISEcp1-mediated transposition event during the early formation of the subclade around 1999. Therefore, both the horizontal transfer of plasmids carrying blaCTX-M-14 to diverse genetic lineages and chromosomal integration in the predominant genetic lineage have contributed to the spread of blaCTX-M-14.

Comparison of milk-clotting activity measurement using the Berridge's operator-based approach and a proposed digital method

The milk clotting activity (MCA) analysis of coagulants used in the production of cheese is an important parameter used in the modern dairy industry. The MCA is calculated using the estimated milk-clotting time (MCT). The standard method used for estimation of MCT is presented in the ISO 23058/IDF 199 Standard using the Berridge's operator-based approach. In this paper a new method is presented to estimate the MCT using digital image acquisition and signal processing. The estimate is obtained by processing a sequence of images of the wall of a round bottom flask containing milk and the coagulant. Digital signal processing and filter techniques were used to remove interferences and artifacts and improve the clotting time estimation. A set of 28 samples of milk plus coagulant were processed. The method's accuracy is about 1/30 s and the average relative error, when compared to an experienced human operator, was με = 2.82%, according to the experimental results, with an associated standard deviation of σε = 3.07% and a median of mε = 1.92%. The method may be used to replace the human operator during the MCT estimation procedure.