Acquisition Method Research Articles

Christian Religious Education Teachers’ Perception on the use of Valuing Methods for Learner Value Acquisition in National Secondary Schools in Kenya

There are rising cases of disrespect in Kenyan society amid efforts by Christian Religious Education (CRE) in secondary schools to make the learner appreciate and respect their own, and other people’s cultural beliefs and practices. The morals of the secondary school graduates enrolled in colleges and society do not reflect the acquisition of values learnt through CRE. Despite the exposure of students to the Christian Religious Education curriculum, cases of disrespect, for instance, drug abuse, abortion, arson in schools, hate speech, religious and ethnic hatred continue being witnessed everywhere. The purpose for this study was to examine the Christian Religious Education teachers’ perception on the use of valuing methods for learner value acquisition in National Secondary Schools in Kenya. The objective of the study was to establish the Christian Religious Education teachers’ perception on the use of valuing methods for learner value acquisition and to find out the challenges they face in the process. The study was based on the Theory of Development of values. The study adopted mixed methods under the convergent parallel design. The target population of the study was 95 national secondary schools in Kenya with a total of 224 CRE teachers. However, 30% of schools and teachers were sampled. The study sample size was 67 CRE teachers. The study employed stratified and simple random sampling to select the schools for study and purposive sampling technique to select CRE teachers to participate in the study. Data was collected through the questionnaire, document analysis, and interview and observation schedules. The validity and reliability of data collection instruments was done through use of experts and piloting respectively. The quantitative data was analyzed using descriptive statistics presented in frequencies and percentages while the qualitative data was analyzed in thematic coding. The study revealed that CRE teachers are conscious of certain values that the CRE content is to facilitate to learners and are aware that particular content may not only transmit a single value depending on how it is facilitated they are however not keen on being specific on relating particular content to particular values. The main reasons perceived by the teachers to this non-use was noted as overcrowded curriculum, insufficient in-service training and the seeming neglect of the affective domain in teaching and examinations. Much as majority of the teachers do not use valuing methods, they however perceive them as having positive characteristics that can enrich content acquisition by learners. Study recommends that the syllabus be explicit in mentioning particular values in relation to particular teaching content. On this note the study recommends that KICD should work very closely with CRE subject specialists to ensure that the affective domain in the teaching of CRE is given adequate guidance.

Lightweight all-focused light field rendering

This paper proposes a novel real-time method for high-quality view interpolation from light field. The proposal is a lightweight method, which can be used with consumer GPU, reaching same or better quality than existing methods, in a shorter time, with significantly smaller memory requirements. Light field belongs to image-based rendering methods that can produce realistic images without computationally demanding algorithms. The novel view is synthesized from multiple input images of the same scene, captured at different camera positions. Standard rendering techniques, such as rasterization or ray-tracing, are limited in terms of quality, memory footprint, and speed. Light field rendering methods often produce unwanted artifacts resembling ghosting or blur in certain parts of the scene due to unknown geometry of the scene. The proposed method estimates the geometry for each pixel as an optimal focusing distance to mitigate the artifacts. The focusing distance determines which pixels from the input images are mixed to produce the final view. State-of-the-art methods use a constant-step pixel matching scan that iterates over a range of focusing distances. The scan searches for a distance with the smallest color dispersion of the contributing pixels, assuming that they belong to the same spot in the scene. The paper proposes an optimal scanning strategy of the focusing range, an improved color dispersion metric, and other minor improvements, such as sampling block size adjustment, out-of-bounds sampling, and filtering. Experimental results show that the proposal uses less resources, achieves better visual quality, and is significantly faster than existing light field rendering methods. The proposal is 8× faster than the methods in the same category. The proposal uses only four closest views from the light field data and reduces the necessary data transfer. Existing methods often require the full light field grid, which is typically 8 × 8 images large. Additionally, a new 4K light field dataset, containing scenes of various types, was created and published. An optimal novel method for light field acquisition is also proposed and used to create the dataset.

Importance of sex and gender differences in enrollment and interpretation of stroke clinical trials

ObjectivesThis review aims to reinforce the importance of improving sex balance in preclinical trials and sex and gender diversity and proportional balance in clinical trials enrollment and how this influences interpretation of stroke clinical trials. It also aims to identify strategies for improvement in data collection. Materials and MethodsA PubMed search was conducted of publications in English, using MeSH terms sex, sex characteristics, gender identity, transgender, gender-nonconforming persons, clinical trials as topic, stroke. Of 249 search results, 217 were human or animal studies related to stroke, the majority of which were reviews, secondary analyses of stroke clinical trials, meta analyses, or retrospective studies, subject to the methods of sex and gender acquisition per the primary data source. Articles were reviewed, noting inclusion or absence of sex and gender definitions and trial design. Selected articles were supplemented with United States Food and Drug Administration, National Institutes of Health, and National Academy of Science, Engineering, and Medicine publications. ResultsThe majority of preclinical studies continue to report sex as a binary variable, and the majority of stroke clinical trials report sex and gender as interchangeable and binary. Mindful trial design and statistical analysis can improve accuracy in the interpretation of sex and gender differences. Guidance exists to improve reporting on currently accepted sex and gender definitions, recommended data collection instruments, and appropriate statistical analyses. ConclusionsDespite acknowledgement of having failed to achieve diverse and proportionally balanced enrollment, sex and gender imbalance across the research continuum remains prevalent. Responsible incorporation of sex and gender in stroke clinical trials can be achieved through thoughtful study design, use of contemporary sex and gender definitions, inclusive prospective data collection, balanced enrollment with prespecified goals, and appropriate statistical analysis.

Evaluation of isothermal and isofield designs of a temperature sensor using magnetic phase transition

Digitalisation and related concepts such as cyber-physical systems and Industry 4.0 have gained focus in the past few decades with the increasing need for real-time data acquisition methods for large-scale use. This study investigates the magnetic flux change throughout a magnetic phase transition and the potential of changing the magnetic moment in a temperature sensor. Magnetothermal properties are experimentally measured using gadolinium to examine characteristics that have strong implications for the proposed temperature sensor. The results are used to propose different device designs for changing magnetic flux. The magnetothermal characteristics identified in the previous investigation are also reconsidered under the scope of these device designs. The results show that while certain characteristics need to be carefully considered throughout material development and the actual device designing process, the magnetic flux behaviour shows potential for temperature sensor application.

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

Efficient Knowledge Acquisition: Standard Essential Patent Analysis Based on Elephant Herding Optimized Kernel-Adaptive Support Vector Machine

A significant area of research is the effective acquisition of knowledge via patent analysis, especially in the fast-developing sector of electric cars. As they can quickly and effectively assess massive volumes of patent data, machine learning approaches can be very useful because they can recognize patterns and developments that are challenging to spot manually. This article provides a unique elephant herding optimized kernel-adaptive support vector machine (EHO-KSVM) method for effective knowledge acquisition utilizing machine learning (ML) on electric car patent data. To evaluate the efficacy of the suggested EHO-KSVM approach, data from the G06F patents were first gathered. In this work, the raw data is first pre-processed using NLP methods, and then the important data is extracted from patent databases using principal component analysis (PCA). Then, we analyze the data using the EHO-KSVM approach to spot patterns and trends that can be utilized to forecast technological developments and offer perceptions into new markets. The outcomes show that the suggested strategy can greatly reduce the time and effort needed to acquire knowledge and provide insightful information for decision-makers in electric car technology.

Talus Osteochondral Defect Treatment With Biological Scaffold.

Talus Osteochondral defects (OCDs) are challenging and there is no consensus in literature regarding which is the best method of treatment. New techniques coming from regenerative medicine are being considered good alternatives of treatment and are being used exponentially in orthopaedic surgery. Platelet-rich fibrin (PRF) is the second generation of platelet concentrates. It has a convenient method of acquisition and can be used to create a biological scaffold which is able to seal up cavitary lesions. In this article, the authors describe a talus OCD treated with a biological scaffold, reporting the technique details and its results clinical and radiological results. The case report objective is to portray the use of this kind of biological material, its advantages, and limitations.Level of Evidence: Level 5.

Task offloading scheme of vehicular cloud edge computing based on Digital Twin and improved A3C

In traditional vehicle edge computing research, the limited computing resources of edge servers are not fully considered. At the same time, when using deep reinforcement learning (DRL) to solve vehicle edge computing problems, it is not fully considered that DRL requires a large amount of real-time data and is prone to gradient explosion or disappearance, over-fitting and local optimal solution problems. Therefore, this article proposes a digital twin (DT)-assisted vehicular cloud edge computing offloading solution. To solve the problem of limited edge server computing resources, this article proposes a vehicle, cloud, and edge server collaboration solution. In response to the problem that DRL requires a large amount of real-time data, this paper proposes a real-time data acquisition method based on DT technology. Aiming at the gradient explosion or disappearance, overfitting, and local optimal solution problems of the asynchronous dominant actor–critic (A3C) algorithm, this paper proposes an improved algorithm of A3C, which introduces the ɛ-greedy strategy and dynamic baseline to increase the possibility of exploration and increase the generalization ability of the model through Dropout technology. During the training process, this article also uses the stochastic gradient descent (SGD) algorithm to accelerate the training process and reduce the computational complexity. The simulation results show that compared with other solutions, the improved algorithm proposed in this paper can effectively reduce the delay and energy consumption of offloading tasks at the vehicular edge cloud.

Reactive UAV-based automatic tunnel surface defect inspection with a field test

This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions.

Health Monitoring System for CNC Machine

This paper introduces a novel Health Monitoring System (HMS) tailored for CNC machines, addressing the critical need for maintaining their optimal performance and preventing unexpected breakdowns in modern manufacturing settings. The system integrates various sensors and data acquisition methods to continuously monitor key parameters like temperature, vibration, and tool wear. By employing advanced data analytics and machine learning algorithms, the HMS can swiftly identify anomalies in real-time, facilitating proactive maintenance and minimizing operational downtime. Additionally, the system features a user- friendly interface for visualizing machine health status and creating predictive maintenance schedules. Experimental validation conducted on a CNC machining center validates the efficacy and reliability of the developed HMS in enhancing machine efficiency, prolonging equipment lifespan, and curbing maintenance expenses. In summary, this Health Monitoring System offers a robust solution for ensuring the seamless operation and longevity of CNC machines in modern manufacturing environments.

Intelligent data-driven acquisition method for user requirements

Intelligent data-driven acquisition method for user requirements

A Review and Prospect of Corporate Text Information Tone Management

With the development of society and technological progress, on the one hand, the academic and practical circles are paying more attention to the text information such as the non-financial report of enterprises. On the other hand, the progress of computer technology has made it possible to obtain and quantify text information. Tone is one of the important characteristics of text information, which refers to the positive or negative emotional tendencies while disclosing text information. Enterprises manipulate text messages for profit through tone management, which has certain economic consequences. This paper systematically reviews the acquisition and measurement methods of text information tone, and reviews the existing literature on the influence factors and economic consequences of tone management, so as to provide direction and reference for future research on text information and tone management.

Targeted and untargeted screening for impurities in losartan tablets marketed in Germany by means of liquid chromatography/high resolution mass spectrometry

Technical advances in the field of quality analysis allow an increasingly deeper look into the impurity profile of drugs. The ability to detect unexpected impurities in addition to known impurities ensures the supply of high-quality drugs and can prevent recalls due to the detection of harmful unexpected impurities, as has happened recently with the N-nitrosamine and azido impurities in losartan (LOS) drug products. In the present study, the LC-MS/HRMS approach described by Backer et al. was applied to an even more complex system, being the investigation of 35 LOS drug products and combination preparations purchased in 2018 and 2022 in German pharmacies. The film-coated tablets were analysed by means of four LC-MS/HRMS method variants. For the separation a Zorbax RR StableBond C18 column (3.0 ×100 mm, particle size of 3.5 µm, pore size of 80 Å), a gradient elution and for mass spectrometric detection a qTOF mass spectrometer with electrospray ionization in positive and negative mode was used. An information-dependent acquisition method was applied for the acquisition of high-resolution mass spectrometry data. The combination of an untargeted and a targeted screening approach revealed the finding of eight impurities in total. Beside the five LOS related compounds, LOS impurity F, J, K, L, M, and related compound D from amlodipine besilate, LOS azide and an unknown derivative thereof were detected. Identification and structure elucidation, respectively, were successfully performed using in silico fragmentation. Differences in the impurity profiles of drug products from 2018 and 2022 could be observed. This study shows that broad screening approaches like this are applicable to the analysis of drug products and can be an important enhancement of the quality assurance of medicinal products.

A sonication-filtration method for acquisition of integrated bacterial communities from phycosphere

Bacteria in phycosphere engage in intricate interactions with microalgae by exchanging organic and inorganic matter. However, elucidating the primary roles of bacteria in phycosphere has been a big challenge, due to the lack of adequate methods for separating tightly associated bacteria from microalgal cells. In this study we evaluated several isolation methods including centrifugation, filtration, sonication combined with filtration, and tween lysis followed by sonication and filtration, aiming to efficiently acquire complete bacterial communities from phycosphere. The results demonstrated that the sonication-filtration approach maximally preserves the original characteristics of the bacterial communities. This method will facilitate the acquisition and further analysis of future experimental data.

Study on Real-Time Spatial Distance Acquisition Method of Transmission Line Based on Laser Point Cloud and Image Monitoring

This paper introduces the principle of safety distance diagnosis of transmission lines by using airborne LiDAR detection technology. This project intends to realize the effective segmentation of ground objects and non-ground objects through the point cloud automatic filtering technology considering the surface fluctuation characteristics. Then, this paper realizes the initial recognition of ground objects based on spatial dimension information. Then this paper extracts the ground object information based on the image data with high precision. The 3D nodes of transmission lines are obtained by Hough transform and block centroid algorithm, and the floor features are extracted by area growth algorithm. The plant feature is extracted by using plant vertex and boundary point. The distance between the power line and the surrounding objects can be measured, and the elevation of the wire and the surrounding environment can be judged accordingly. The algorithm is tested by using the observation data of unmanned airborne LiDAR, and the simulation results show that the proposed method is feasible. Through the identification of buildings, plants and other objects along the line, the harm degree of them on the power line is judged and the effective treatment is carried out. This ensures the safe operation of transmission lines.

Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.

Amide proton transfer-weighted imaging with a short acquisition time based on a self B0 correction using the turbo spin echo-Dixon method: A phantom study

PurposeConventional amide proton transfer (APT)-weighted imaging requires a chemical exchange saturation transfer (CEST) sequence with multiple saturation frequency offsets and a B0 correction sequence, plus a long acquisition time that can be reduced by applying the conventional method using CEST images with seven radiation pulses (i.e., the seven-points method). For a further reduction of acquisition times, we propose fast two-dimensional (2D) APT-weighted imaging based on a self B0 correction using the turbo spin echo (TSE)-Dixon method. We conducted a phantom study to investigate the accuracy of TSE-Dixon APT-weighted imaging. MethodsWe prepared two types of phantoms with six samples for a concentrationdependent evaluation and a pH-dependent evaluation. APT-weighted images were acquired by the conventional, seven-points, and TSE-Dixon methods. Linear regression analyses assessed the dependence between each method's APT signal intensities (SIs) and the concentration or pH. We performed a one-way analysis of variance with Tukey's honestly significant difference post hoc test to compare the APT SIs among the three methods. The agreement of the APT SIs between the conventional and seven-points or TSE-Dixon methods was assessed by a Bland- Altman plot analysis. ResultsThe APT SIs of all three acquisition methods showed positive concentration dependence and pH dependence. No significant differences were observed in the APT SIs between the conventional and TSE-Dixon methods at each concentration. The Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in 0.42% bias and narrow 95% limits of agreement (LOA) (0.93%–0.09%) compared to the conventional method. The APT SIs measured using the TSE-Dixon method showed 0.14% bias and similar 95% LOA (−0.33% to 0.61%) compared with the seven-points method. The APT SIs of all three methods showed positive pH dependence. At each pH, no significant differences in the APT SIs were observed among the methods. Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in low bias (0.03%) and narrow 95% LOA (−0.30% to 0.36%) compared to the conventional method. The APT SIs measured by the TSE-Dixon method showed slightly larger bias (0.29%) and similar 95% LOA (from −0.15% to 0.72%) compared to those measured by the seven-points method. ConclusionThese results demonstrated that our proposed method has the same concentration dependence and pH dependence as the conventional method and the seven-points method. We thus expect that APT-weighted imaging with less influence of motion can be obtained in clinical examinations.

Energy-Saving Data Acquisition Model of Wireless Sensor Network Based on Nonlinear Algorithm

Wireless sensor network significant data acquisition has a high cost, long completion time, and low accuracy. This paper adopts a sensor network data acquisition method based on a nonlinear algorithm to solve the above problems. In this paper, a distributed data acquisition method based on nonlinear regression is established by combining the time series relationship of data. First, this paper uses nonlinear regression analysis technology to establish the sensing data model and retain the characteristics of the sensing data. This makes the node pass only the parametric data of the regression model. This paper uses this method to replace the transmission of actual monitoring sensory data information. Experiments show that this method can effectively reduce data redundancy and network traffic under any conditions. The method has been verified in practical WSN applications.

The influence of an urban hukou and its acquisition methods on older adults’ happiness: an analysis based on the Chinese General Social Survey

The hukou system is a population management policy in China. However, existing research has paid little attention to the relationships between Chinese urban hukou, its acquisition methods, and older adults’ happiness. This study was based on 10,954 samples from the Chinese General Social Survey in 2013, 2015, and 2017. An ordinary least squares model was used to analyze the influence and differential influence of an urban hukou and its access paths on happiness among older adults. Compared with having a rural hukou, having an urban hukou is correlated with higher levels of happiness in older adults. Moreover, those who had actively converted to an urban hukou or were born with it were happier, while passive conversion to an urban hukou was not significantly correlated with happiness. Additionally, actively converting to an urban hukou and being born with urban hukou influence the happiness of older adults by affecting their class identity and income. To improve older adults’ happiness, it is necessary to narrow the welfare gap between an urban and rural hukou, balance the allocation of public resources in urban and rural areas, and make targeted assessments to solve the difficulties of older adults who passively convert to an urban hukou.

Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}10 times faster data acquisition and achieving ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 10 times reduction in data size.