Mapping-based Approach Research Articles

Transposase-Assisted RNA/DNA Hybrid Co-Tagmentation for Target Meta-Virome of Foodborne Viruses.

Foodborne diseases are major public health problems globally. Metagenomics has emerged as a widely used tool for pathogen screening. In this study, we conducted an updated Tn5 transposase-assisted RNA/DNA hybrid co-tagmentation (TRACE) library construction approach. To address the detection of prevalent known foodborne viruses and the discovery of unknown pathogens, we employed both specific primers and oligo-T primers during reverse transcription. The method was validated using clinical samples confirmed by RT-qPCR and compared with standard RNA-seq library construction methods. The mapping-based approach enabled the retrieval of nearly complete genomes (>95%) for the majority of virus genome segments (86 out of 88, 97.73%), with a mean coverage depth of 21,494.53× (ranging from 77.94× to 55,688.58×). Co-infection phenomena involving prevalent genotypes of Norovirus with Astrovirus and Human betaherpesvirus 6B were observed in two samples. The updated TRACE-seq exhibited superior performance in viral reads percentages compared to standard RNA-seq library preparation methods. This updated method has expanded its target pathogens beyond solely Norovirus to include other prevalent foodborne viruses. The feasibility and potential effectiveness of this approach were then evaluated as an alternative method for surveilling foodborne viruses, thus paving the way for further exploration into whole-genome sequencing of viruses.

Cultivating visual literacy and critical thinking tendency with technological knowledge organizing supports: a concept mapping-based online problem-posing approach

Cultivating visual literacy and critical thinking tendency with technological knowledge organizing supports: a concept mapping-based online problem-posing approach

Initial-Value-Free Nonlinear Mapping-Based Approach to Practical Finite/Fixed-Time Consensus Control for Constrained Multiagent Systems

Initial-Value-Free Nonlinear Mapping-Based Approach to Practical Finite/Fixed-Time Consensus Control for Constrained Multiagent Systems

Comprehending complex chemistry problems in a structured and enjoyable manner: A concept mapping-based contextual gaming approach

Comprehending complex chemistry problems in a structured and enjoyable manner: A concept mapping-based contextual gaming approach

An Efficient Multi-Scale Attention Feature Fusion Network for 4K Video Frame Interpolation

Video frame interpolation aims to generate intermediate frames in a video to showcase finer details. However, most methods are only trained and tested on low-resolution datasets, lacking research on 4K video frame interpolation problems. This limitation makes it challenging to handle high-frame-rate video processing in real-world scenarios. In this paper, we propose a 4K video dataset at 120 fps, named UHD4K120FPS, which contains large motion. We also propose a novel framework for solving the 4K video frame interpolation task, based on a multi-scale pyramid network structure. We introduce self-attention to capture long-range dependencies and self-similarities in pixel space, which overcomes the limitations of convolutional operations. To reduce computational cost, we use a simple mapping-based approach to lighten self-attention, while still allowing for content-aware aggregation weights. Through extensive quantitative and qualitative experiments, we demonstrate the excellent performance achieved by our proposed model on the UHD4K120FPS dataset, as well as illustrate the effectiveness of our method for 4K video frame interpolation. In addition, we evaluate the robustness of the model on low-resolution benchmark datasets.

Participatory Action Research for Urban Connectivity: Bridging Inequality in Metropolitan Monterrey

This paper argues for participatory action research as a potent method for urban connectivity, surpassing government or expert-led methods. It introduces two approaches: integrating dialogic interviews to foster reflexivity and adopting an Ecosystem mappingbased approach encompassing social, economic, physical, and cultural dimensions. Dialogic interviews promote inclusivity by enabling diverse perspectives to shape research outcomes. Ecosystem mapping visually captures intricate connectivity dynamics. By combining participatory action research, dialogic interviews, and Ecosystem mapping, a robust framework emerges to address urban connectivity challenges. Empowering communities and embracing diverse axes of connectivity, this approach transforms urban planning practices, fostering contextually relevant and sustainable solutions. A co-created, resilient urban future becomes achievable through engagement and active stakeholder involvement.

Facilitating creativity, collaboration, and computational thinking in group website design: A concept mapping-based mobile flipped learning approach

Facilitating creativity, collaboration, and computational thinking in group website design: A concept mapping-based mobile flipped learning approach

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

We propose an algorithm for generating a panoramic image of a pipe’s inner surface based on inverse perspective mapping (IPM). The objective of this study is to generate a panoramic image of the entire inner surface of a pipe for efficient crack detection, without relying on high-performance capturing equipment. Frontal images taken while passing through the pipe were converted to images of the inner surface of the pipe using IPM. We derived a generalized IPM formula that considers the slope of the image plane to correct the image distortion caused by the tilt of the plane; this IPM formula was derived based on the vanishing point of the perspective image, which was detected using optical flow techniques. Finally, the multiple transformed images with overlapping areas were combined via image stitching to create a panoramic image of the inner pipe surface. To validate our proposed algorithm, we restored images of pipe inner surfaces using a 3D pipe model and used these images for crack detection. The resulting panoramic image of the internal pipe surface accurately demonstrated the positions and shapes of cracks, highlighting its potential utility for crack detection using visual inspection or image-processing techniques.

A Novel Method of Invisible Video Watermarking Based on Index Mapping and Hybrid DWT-DCT

Watermarking is widely used in multimedia preservation and communication, which comprises, and is therefore not limited to, data security and validation. Security, readability, imperceptibility, and resilience are some of the key advantages of this technology. However, there are still certain issues that need to be addressed, such as the ability to withstand a variety of assaults without substantially affecting the quality and value of embedded data. Based on its operational domain, the watermarking technology may be divided into two groups: spatial and frequency watermarking. An index mapping-based watermarking approach for copyright protection of multi-media color videos is presented in this paper. We offer a hybrid discrete wavelet transform and discrete cosine transform (DCT) watermarking algorithm for digital video watermarking of a color video watermark. Peak signal to noise ratio (PSNR), similarity structure index measure (SSIM), have been used to analyze the distortion produced by watermarking. It is suggested that the proposed video watermarking method provides greater imperceptibility in conjunction with each other with the human visual system and it offers higher robustness against different attacks.

Mapping population distribution from open address data: application to mainland Portugal

ABSTRACT Mapping population distribution remains a common need in various fields of studies. Several approaches and methodologies have been adopted to obtain high-resolution population distribution grids. The use of addresses data to obtain gridded population distribution maps emerges as one of the more recent and accurate approaches. The increasing dissemination and availability of geo-data and more specifically address data allow us to obtain updated, granular and high spatial resolution population distribution maps. This paper describes a bottom-up open addresses data mapping-based approach of gridded population distribution with a fine spatial resolution. Through a QGIS plugin, an adaptation of the housing unit methodology was implemented to obtain 500 m × 500 and 250 m × 250 m population grids for mainland Portugal. The results showed that the use of reliable addresses databases can generate gridded population distribution maps with a high degree of adjustment to reality.

A Waveform Mapping-Based Approach for Enhancement of Trunk Borers' Vibration Signals Using Deep Learning Model.

Simple SummaryTrunk-boring insects belong to one of the most destructive forest pests. Larvae in some groups are particularly difficult to detect since they make their living in trunks and no obvious sign can be found from outside. To deal with this problem, a new method is presented here, which is the embedding of a vibration probe into the tree trunk to pick up vibrations caused by larvae and the use of a model to distinguish whether the tree is infected. However, this procedure can experience severe interference from environmental noise, which is simultaneously picked up with vibrations. Thus, it is necessary to add a noise suppression process before discrimination. Previous examples have proved that the application of the analysis intended for sounds to boring vibrations is feasible. Therefore, we took advantage of deep learning-based speech enhancement and further improved it to develop a boring vibration enhancement model. The training data used in this research contains boring vibrations recorded within pieces of trunks and noise, which are common for trees’ living environment. The experimental results indicate that the enhancement procedure provided by our model substantially increases the accuracy of several well-known classification models, guaranteeing a more practical larvae detection.The larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity according to the vibrations. Clean boring vibration signals without noise are critical for accurate judgement. Unfortunately, these environments are filled with natural or artificial noise. To address this issue, we constructed a boring vibration enhancement model named VibDenoiser, which makes a significant contribution to this rarely studied domain. This model is built using the technology of deep learning-based speech enhancement. It consists of convolutional encoder and decoder layers with skip connections, and two layers of SRU++ for sequence modeling. The dataset constructed for study is made up of boring vibrations of Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae) and environmental noise. Our VibDenoiser achieves an improvement of 18.57 in SNR, and it runs in real-time on a laptop CPU. The accuracy of the four classification models increased by a large margin using vibration clips enhanced by our model. The results demonstrate the great enhancement performance of our model, and the contribution of our work to better boring vibration detection.

A concept mapping-based self-regulated learning approach to promoting students’ learning achievement and self-regulation in STEM activities

ABSTRACT STEM (Science, Technology, Engineering, and Mathematics) education is attracting increasing attention, but how to improve students’ STEM learning achievement is still a big challenge. In the process of learning, students actively set learning goals, organize learning content, set learning standards, and independently adapt learning strategies and monitoring measures in order to achieve their own learning goals and to help them learn STEM knowledge and skills. Concept mapping is considered to be an effective visualized knowledge organizing tool that helps students to manage and organize their knowledge systematically and visually. This study proposes a concept mapping-based self-regulated learning approach. A quasi-experiment was conducted using a concept mapping-based self-regulated learning system in order to evaluate the effectiveness of the approach. The experimental results show that the proposed approach significantly improved students’ STEM skills, but not their STEM knowledge compared to the conventional self-regulated learning approach. The proposed learning approach helped to improve students’ self-regulation compared to the conventional self-regulation learning approach. In short, the concept mapping-based self-regulation learning approach enhances students’ self-efficacy and self-regulation, which in turn helps students to learn STEM skills effectively.

A partially linearized spin-mapping approach for nonadiabatic dynamics. I. Derivation of the theory

We present a new partially linearized mapping-based approach for approximating real-time quantum correlation functions in condensed-phase nonadiabatic systems, called the spin partially linearized density matrix (spin-PLDM) approach. Within a classical trajectory picture, partially linearized methods treat the electronic dynamics along forward and backward paths separately by explicitly evolving two sets of mapping variables. Unlike previously derived partially linearized methods based on the Meyer-Miller-Stock-Thoss mapping, spin-PLDM uses the Stratonovich-Weyl transform to describe the electronic dynamics for each path within the spin-mapping space; this automatically restricts the Cartesian mapping variables to lie on a hypersphere and means that the classical equations of motion can no longer propagate the mapping variables out of the physical subspace. The presence of a rigorously derived zero-point energy parameter also distinguishes spin-PLDM from other partially linearized approaches. These new features appear to give the method superior accuracy for computing dynamical observables of interest when compared with other methods within the same class. The superior accuracy of spin-PLDM is demonstrated in this paper through application of the method to a wide range of spin-boson models as well as to the Fenna-Matthews-Olsen complex.

Resolving the Spatial Profile of Figure Enhancement in Human V1 through Population Receptive Field Modeling.

The detection and segmentation of meaningful figures from their background is one of the primary functions of vision. While work in nonhuman primates has implicated early visual mechanisms in this figure-ground modulation, neuroimaging in humans has instead largely ascribed the processing of figures and objects to higher stages of the visual hierarchy. Here, we used high-field fMRI at 7 Tesla to measure BOLD responses to task-irrelevant orientation-defined figures in human early visual cortex (N = 6, four females). We used a novel population receptive field mapping-based approach to resolve the spatial profiles of two constituent mechanisms of figure-ground modulation: a local boundary response, and a further enhancement spanning the full extent of the figure region that is driven by global differences in features. Reconstructing the distinct spatial profiles of these effects reveals that figure enhancement modulates responses in human early visual cortex in a manner consistent with a mechanism of automatic, contextually driven feedback from higher visual areas.SIGNIFICANCE STATEMENT A core function of the visual system is to parse complex 2D input into meaningful figures. We do so constantly and seamlessly, both by processing information about visible edges and by analyzing large-scale differences between figure and background. While influential neurophysiology work has characterized an intriguing mechanism that enhances V1 responses to perceptual figures, we have a poor understanding of how the early visual system contributes to figure-ground processing in humans. Here, we use advanced computational analysis methods and high-field human fMRI data to resolve the distinct spatial profiles of local edge and global figure enhancement in the early visual system (V1 and LGN); the latter is distinct and consistent with a mechanism of automatic, stimulus-driven feedback from higher-level visual areas.

Effects of a multi-level concept mapping-based question-posing approach on students’ ubiquitous learning performance and perceptions

The literature indicates that the use of mobile devices, wireless communication and sensing techniques can enhance students' learning in authentic environments and improve their learning effectiveness. Good learning strategy guides are essential for successful inquiry learning, and the question-posing strategy has been acknowledged as a type of high-level cognitive strategy that plays an important role in facilitating students' knowledge integration in the inquiry learning process. However, question posing seems challenging for many students and may lead to heavy cognitive load when appropriate scaffolding is not available. To solve this problem, the present research imported a concept mapping knowledge representation model as an auxiliary strategy for question-posing activities based on the research finding that visual knowledge representations are conducive to high-order thinking. It also developed a ubiquitous learning system which integrated multilevel concept mapping and question-posing strategy guidance to investigate the application effects of this learning model. A quasi-experimental design was applied, and an experiment was conducted to measure 5th graders’ learning of a plant unit of the natural science curriculum. Students in the experimental group learned with the proposed ubiquitous learning system based on the multilevel concept mapping and question-posing approach, while those in the control group learned with the ubiquitous learning system based on the question-posing approach. The results showed that the students in the experimental group significantly outperformed those in the control group from the perspectives of learning achievement and question-posing performance, but no significance was found in their cognitive load, meta-cognitive awareness, and science learning strategies.

Mapping-Based Dosage of Gait Modification Selection for Multi-Parameter, Subject-Specific Gait Retraining

Gait retraining to reduce knee loading has been proposed as a conservative treatment option for early-stage knee osteoarthritis. Mounting evidence suggests that a subject-specific approach may be most effective for ensuring consistent knee loading reductions across all individuals within a population. However, it is currently unclear how to determine the required gait modification dosage selection type and amount to both reduce knee loading and satisfy individual preferences. To overcome this challenge, we introduce a novel, mapping-based dosage selection approach to systematically determine multi-parameter gait modifications to reduce knee loading while maintaining individual user preference. In this approach, individuals first explore different dosages of multi-parameter gait modifications, and then a resulting visual map is displayed with a subject-specific dosage selection zone for the target knee loading reduction. Subjects then self-select a preferred gait within their dosage selection zone. To evaluate the feasibility of this approach, fifteen healthy subjects and one knee OA patient performed walking trials on a treadmill involving various dosages of gait modifications to foot progression angle and step width. Subjects quickly selected the subject-specific gait modifications via mapping-based dosage selection during a single 6 min trial, which reduced the knee adduction moment by an average of 14.2%. Resulting subject-specific gait modifications varied, with 6 subjects selecting only toe-in, 5 subjects selecting both toe-in and increased step width, 2 subjects selecting only toe-out, 1 subject selecting both toe-out and increased step width and 1 subject selecting only increased step width. Average perceived exertion was “fairly light” (index was 10.5±2.2). The knee OA patient selected only toe-in and reduced the knee adduction moment by 12.8%. The presented mapping-based dosage selection approach could provide a systematic and practical means to determine subject-specific gait modifications while maintaining individual preferences.

Analytical modelling solution of producer mobility support scheme for named data networking

Named Data Networking (NDN) is a clean-slate future Internet architecture proposed to support content mobility. However, content producer mobility is not supported fundamentally and faces many challenges such as, high handoff latency, signaling overhead cost and unnecessary Interest packet losses. Hence, many approaches indirection-based approach, mapping-based approach, locator-based approach and control/data plane-based approach were proposed to address these problems. Mapping-based and control/data plane-based approach deployed servers for name resolution serveces to provide optimal data path after handoff, but introduces high handoff latency and signalling overhead cost. Indirection-based and locator-based approach schemes provide normal handoff delay, but introduces sub-optimal or tiangular routing path. Therefore, there is needs to provide substantial producer mobility support that minimizes the handoff latency, signaling cost and improve data packets delivery via optimal path once a content producer relocates to new location. This paper proposed a scheme that provides optimal data path using mobility Interest packets and broadcasting strategy. Analytical investigation result shows that our proposed scheme outperforms existing approaches in terms of handoff latency, signaling cost and path optimization.

Impacts of a mind mapping-based contextual gaming approach on EFL students’ writing performance, learning perceptions and generative uses in an English course

To situate students in authentic English learning and practicing contexts so as to facilitate their writing performance, in this study, a mind mapping-based contextual gaming approach was proposed. A quasi-experiment was implemented by recruiting two English tourism classes of students from a university of science and technology in northern Taiwan. One class was scheduled to learn with the proposed approach, and the other was arranged to learn with the conventional contextual gaming approach. The students’ writing performance, learning perceptions and generative uses were measured to evaluate the effectiveness of the proposed approach. It was found that, compared to the control group students; the experimental group students who learned with the proposed learning approach had better writing performance in the aspects of fluency and elaboration with a small effect size. Additionally, it was found that most students considered the game to be beneficial and interesting, and they were in favor of the mind mapping strategy. Moreover, it was also found that the proposed learning approach is more likely to help students produce positive thoughts and feelings, generate diverse ideas, create a sense of exoticism and express actions or intent of actions. Finally, the limitations of the study and some suggestions for improvement of this proposed gaming learning environment and approaches are provided.

Efficient Producer Mobility Management Model in Information-Centric Networking

Information centric networking is a clean-slate Internet architecture designed as a future Internet to supplement and completely replace the current Internet architecture. The future Internet information-centric networking was proposed in different kind of approaches to offered vast advantages ranging from security, catching, and content mobility support. However, some of the approaches support only content consumer, while content producer mobility faces many challenges, such as unnecessary Interest packet losses, high handoff latency, scalability issues, and high signaling overhead. Therefore, many approaches, protocols, and schemes were proposed to address the problem. The schemes such as indirection-based approach, locator-based approach, rendezvous-based, forwarding-based, and control/data plane-based, and mapping-based approach were investigated using either analytical or simulation investigation. However, in many cases of the formulation of an analytical investigation for information-centric networking mobility support management, the determination and incorporation of producers' movement behavior are neglected, whereas the movement pattern of mobile content producer plays a vital role in mobility performance analysis of the wireless mobile network. This paper proposed a producer mobility management model for information-centric networking with a random waypoint and random walk mobility model incorporated, to measure the movement pattern effect and proved the uniformity of the proposed model to support any kind of mobility support approaches or schemes. The performance analysis result of our proposed model revealed that movement pattern does affect the mobile producer handoff performance. Hence, for the accuracy of handoff latency, handoff signaling cost, and data packets delivery cost investigation, mobility models must be selected carefully based on the application domain.

A Mapping-Based Sharing Approach for Heterogeneous XML Data Supporting User Personalization

With the development of computer science and technology, there is an urgent demand on integration of heterogeneous data focus on personalized information service. In this paper, we explore two aspects on the usage of the heterogeneous data stored in the form of XML, and propose a mapping-based sharing approach for heterogeneous data supporting user personalization. First, a method is proposed to automatically standardize the heterogeneous XML data, which are provided by different users. The standardization process includes two steps: 1) An XML document is first parsed to a DOM tree, and then the DOM tree is transformed to a reduction tree. By introducing synonym table, the mapping relation can be obtained after the process that the reduction tree is transformed to a standard tree. 2) With that mapping relation, the XSLT file can be generated, with which the standard XML document containing standard data can be easily generated. Second, a method is proposed to support user personalization on data display. The mapping relation mentioned above is first stored in a well-defined form, which can be used to transform the standard XML document into a personalized XML document so as to display users' personalized data. The methodology proposed in this paper has been applied to an application of a sharing platform for the multi-source agricultural spatial data. The user-friendly interface and experience of the platform show that the methodology proposed is feasible.