Visual Method Research Articles

The Potential of AI in Information Provision in Energy-Efficient Renovations: A Narrative Review of Literature

<p>Energy-efficient renovation (EER) is a complex process essential for reducing emissions in the built environment. This research identifies homeowners as the main decision-makers, whereas intermediaries and social interactions between peers are highly influential in home renovations. It investigates information and communication barriers encountered during the initial phases of EERs. The study reviews AI tools developed within the EERs domain to assess their capabilities in overcoming these barriers and identifies areas needing improvement. This research examines stakeholders, barriers, and the AI tools in the literature for EERs. The discussion compares the functionalities of these tools against stakeholder needs and the challenges they face. Findings show that tools often overlook methodologies in human–computer interaction and the potential of textual and visual AI methods. Digital tool development also lacks insights from social science and user feedback, potentially limiting the practical impact of these innovations. This article contributes to the EERs literature by proposing an AI-supported framework and outlining potential research areas for future exploration, particularly improving tool effectiveness and stakeholder engagement to scale up the EER practice.</p>

Соціальна політика як чинник забезпечення життєстійкості держави

In the conditions when Ukrainian statehood is facing the most serious challenge in its recent history, issues regarding state policy priorities in the social and economic sphere are becoming relevant. The goal of the article is to study the place and role of social policy in the process of ensuring the sustainability of the state. General scientific methods of analysis and synthesis, generalization, classification, system analysis, as well as a graphic method for visualization of the obtained results were used to form the main theses of the study. The work analyzes the modern scientific and theoretical platform and basic definitions of the concept of «sustainability», investigates the specifics and permanent context of the use of terms related to it — viability, stability and resilience, offers a systemic view of the state’s viability and identifies its components. Social policy is considered as an element of the management system of social processes, which directly affects the sustainability of Ukrainian society. At the current stage of development of the global economy, human is the main source of economic growth, and the quality of human potential is the key to the state’s competitiveness. Taking this into account, the importance of social and humanitarian policy is growing many times over. In economic policy, making a profit does not become a self-sufficient activity, instead the policy should be aimed at the person, unlocking the potential of the individual and forming the quality human potential of the state. Therefore, when assessing social policy, one should remember the value of a person, in particular the economic one, and the need for appropriate investments — in safety, health care system, and improving working conditions for the development of this resource. The work highlights the main elements of the state’s sustainability, shows the connection between its higher-level elements, such as the sustainability of the economy and the sustainability of society. It is in society — in the family, community, at the place of study — that the personality is formed and acquires a certain physical condition, education, professional skills and qualifications, and social connections. In this way, human and social capital is formed as a resource for the development of the economy and, accordingly, its stability and sustainability. At the same time, the economy generates material and financial resources that are necessary for both physical existence and development of an individual, as well as for the existence of society as a whole, in particular, social and engineering infrastructure facilities. The conducted analysis clearly demonstrates the importance of social policy as a social institution of sustainability in the state.

A novel Tree-augmented Bayesian network for predicting rock weathering degree using incomplete dataset

The precise forecasting of the weathering degree of surrounding rock holds paramount importance for the scientific design and secure execution of tunnel engineering. The apparent features of the surrounding rock serve as critical indicators for evaluating its weathering degree. This paper endeavors to quantify the rock apparent features based on an improved Computer vision model and establish a multi-source heterogeneous dataset encompassing 10 parameters, thereby facilitating data-driven predictions of the weathering degree. Specifically, the rock appearance parameters are quantified and segmented by an improved Tunnel face feature segmentation (TFFSeg) model, which is tailored to the unique characteristics of groundwater, fractures, and interlayers. Concurrently, the TFFSeg model exhibits significantly enhanced performance for these rock features compared to other widely employed Computer vision methods. Subsequently, this multi-source dataset is further enriched by incorporating rock physical and mechanical parameters as well as tunnel design parameters. Nevertheless, the issue of data incompleteness persists within this dataset. To achieve precise prediction of the weathering degree based on this incomplete dataset, a novel Tree-augmented Bayesian network (TAN-BN) is designed, which is capable of learning from incomplete datasets. The predictive outcomes demonstrate that the proposed TAN-BN surpasses other currently utilized meta models and ensemble models, such as ANN, GBRT, and Naive BN. Finally, sensitivity analysis is conducted to determine the importance rankings of the 10 parameters, offering valuable insights for on-site evaluation of the rock weathering degree at the tunnel face.

Introduction to special issue: youth, rural places and marginalisation

ABSTRACT This Special Issue on Youth, Rural Places and Marginalisation, brings together nine papers from six countries – Australia, England, Denmark, Finland, Norway and Sweden – to explore young people's experiences of living in rural locations. We focused on rural youth for two main reasons: firstly, there are relatively few sociological studies of young people's experience of rural life and, secondly, academics and policymakers have tended to frame discussions about young people and rural places in negative terms. Together the papers here use a wide range of methods – quantitative and qualitative longitudinal approaches, questionnaire surveying, face-to-face and online qualitative interviews, ethnography, visual methods, ‘mobile probes', ‘walk-along' and ‘drive-along' interviews – in order to understand young people's everyday life. We were particularly interested in the stories of young people who appeared to resist ‘the mobility imperative' and chose to stay rather than leave their rural homes. Our introduction focuses on ‘rethinking concepts of staying'; ‘gender and the risks of staying' and ‘geographies of rural opportunities'. We conclude with a call for a sociological imagination of rural youth that avoids romanticizing narratives of ‘the rural idyll’ or which paints rural places and populations as backward or lacking.

Development and optimization of immunomagnetic bead separation-convective PCR (IMBS-CPCR) for on-site detection of four common Vibrio species in aquaculture

Foodborne infectious diseases, particularly those caused by pathogenic Vibrio species in aquaculture, are a significant concern in the field of seafood safety. They not only hinder the economic development of the industry but also pose a major threat to public health. Traditional detection methods are often too slow, insensitive, or impractical for portable use, highlighting the urgent need for a sensitive, on-site detection method for Vibrio that prioritizes convenience and speed. This study developed a visual IMBS-CPCR method for the detection of four common pathogenic Vibrio species: Vibrio alginolyticus, Vibrio anguillarum, Vibrio fluvialis, and Vibrio harveyi. The method achieved high capture rates for target bacteria (82.6%–83.3%) and low capture rates for non-target bacteria (below 11%), demonstrating good sensitivity by detecting bacteria at concentrations as low as 10 CFU/mL. Optimal amplification conditions were determined for each species, with specificity tests confirming that only target bacteria produced amplification bands. Sensitivity tests revealed detection limits of 101 CFU/mL for V. alginolyticus, V. anguillarum, and V. fluvialis, and 102 CFU/mL for V. harveyi. The combined techniques improved detection sensitivity in simulated fish samples by 10–100 fold compared to direct CPCR or PCR methods.

CRISPR/Cas13a-mediated visual detection: A rapid and robust method for early detection of Nosema bombycis in silkworms

The sericulture industry faces a significant threat from the Pebrine disease of silkworms, caused by Nosema bombycis. Nonetheless, the current microscopic diagnostic methods can be time-consuming, labor-intensive, and lacking sensitivity and accuracy. Therefore, it is crucial to develop a novel detection approach that is efficient, highly sensitive, and low-cost. In this regard, the CRISPR/Cas system has the potential to be a fast, accurate, and highly specific method of detection. Herein, using a microplate reader, a portable fluorescence detection device, and test strips as signal output tools respectively, we have efficiently developed three rapid and facile visual detection methods for N. bombycis using a CRISPR/Cas13a system with conjugation of Recombinase polymerase amplification (RPA). We evaluated the sensitivity of this combined technology by comparing it with the positive plasmid standard and the genome standard of N. bombycis. Remarkably, the sensitivity of the CRISPR/Cas13a system for N. bombycis positive plasmid standard based on the microplate reader, portable fluorescence detection device, and test strips was 1 copy/μL, 10 copies/μL, and 1 copy/μL, respectively, while for the N. bombycis genome standards, the detection sensitivity was 10 fg/μL, 10 fg/μL, and 1 fg/μL, respectively. In addition, extensive evaluations have demonstrated that the established technology can accurately detect N. bombycis without cross-reactivity with other pathogens, ensuring a specificity rate of 100%. In brief, this study will provide a practical, efficient, and affordable method for early and rapid detection of N. bombycis in various settings.

A high-precision visual detection method for rail profile based on Scheimpflug condition

Rail profile is an important indicator of rail service status, which should be always kept in good condition. How to efficiently collect rail profiles with high precision is a critical issue for their condition-based maintenance. This paper focuses on the method for accurate visual detection of rail full profile (RFP), including optical modeling, visual calibration, and error compensation. The proposed optical unit, featuring double line structured-light sensors, introduces an innovative method under the Scheimpflug condition to avoid the limitation of depth of field in RFP measurement. Subsequently, double visual sensors are calibrated together using the common reference target to extract the internal and external parameters respectively. The dynamic errors resulting from the impact of vehicle vibrations are ultimately investigated to rectify the distorted profiles to the normal. Both laboratory and field tests are performed to verify the effectiveness of the proposed system in terms of precision and efficiency.

Headspace-single drop microextraction based visual colorimetry for non-chromatographic speciation analysis of nitrite and nitrate in environmental water sample

In this work, a visual colorimetric method based on headspace-single drop microextraction (HS-SDME) was developed for the non-chromatographic speciation analysis of nitrite and nitrate. The method adequately exploited the individual chemical vapor conditions of nitrite and nitrate that were converted to NO by specific reductants. Gaseous NO underwent a diazotization reaction with chromogenic reagent through the HS-SDME process to change from colorless to purplish-red. Instead of using large, time-consuming and relatively complicated chromatographic equipment for separation, the species of nitrite and nitrate could be analyzed by simply changing the reagents. Meanwhile, the extraction method effectively mitigated the matrix interference of solution and improved the sensitivity of method, with intuitive discrimination of color by the naked eye. The limits of detection were 0.07 μg/mL for nitrite and 0.2 μg/mL for nitrate, respectively. This method has been applied to analysis of real environmental water samples, which also effectively extended the application scope of HS-SDME device.

Vision Transformers for identifying asteroids interacting with secular resonances

Currently, more than 1.4 million asteroids are known in the main belt. Future surveys, like those that the Vera C. Rubin Observatory will perform, may increase this number to up to 8 million. While in the past identification of asteroids interacting with secular resonances was performed by a visual analysis of images of resonant arguments, this method is no longer feasible in the age of big data. Deep learning methods based on Convolutional Neural Networks (CNNs) have been used in the recent past to automatically classify databases of several thousands of images of resonant arguments for resonances like the ν6, the g−2g6+g5, and the s−s6−g5+g6. However, it has been shown that computer vision methods based on the Transformer architecture tend to outperform CNN models if the scale of the image database is large enough. Here, for the first time, we developed a Vision Transformer (ViT) model and applied it to publicly available databases for the three secular resonances quoted above. ViT architecture outperforms CNN models in speed and accuracy while avoiding overfitting concerns. If hyper-parameter tuning research is undertaken for each analyzed database, ViT models should be preferred over CNN architectures.

ESFuse: Weak Edge Structure Perception Network for Infrared and Visible Image Fusion

Infrared and visible image fusion (IVIF) fully integrates the complementary features of different modal images, and the fused image provides a more comprehensive and objective interpretation of the scene compared to each source image, thus attracting extensive attention in the field of computer vision in recent years. However, current fusion methods usually center their attention on the extraction of prominent features, falling short of adequately safeguarding subtle and diminutive structures. To address this problem, we propose an end-to-end unsupervised IVIF method (ESFuse), which effectively enhances fine edges and small structures. In particular, we introduce a two-branch head interpreter to extract features from source images of different modalities. Subsequently, these features are fed into the edge refinement module with the detail injection module (DIM) to obtain the edge detection results of the source image, improving the network’s ability to capture and retain complex details as well as global information. Finally, we implemented a multiscale feature reconstruction module to obtain the final fusion results by combining the output of the DIM with the output of the head interpreter. Extensive IVIF fusion experiments on existing publicly available datasets show that the proposed ESFuse outperforms the state-of-the-art(SOTA) methods in both subjective vision and objective evaluation, and our fusion results perform well in semantic segmentation, target detection, pose estimation and depth estimation tasks. The source code has been availabled.

Feminist visualisation challenges: Methodological innovation, opportunities, and lessons learned

AbstractIn this paper, we detail the process of organising and facilitating a visualisation challenge as part of a larger project centring visual methods. We explore how the visualisation challenge specifically operated to highlight feminist epistemological and methodological principals, and practically, what worked and what didn't. We conclude that visualisation challenges offer exciting potential to jumpstart creative and innovative project development, but if a challenge is to be successful, context matters, and so too do practical and logical considerations. We believe that feminist visualisation challenges offer exciting models to share findings and data, learn from emerging research practices, and build community within and beyond the academy.

Visualization of integrated failure consequences of hazardous chemical leakage and explosion

Dangerous chemicals are widely present in various aspects of people's lives, but they often pose risks of leakage and explosion during transportation and storage, resulting in severe casualties and property damage. Firstly, this paper proposes a visualization method applicable to comprehensive failure consequences of hazardous chemical storage tanks. This method considers the toxic hazard range of chemical leaks and the blast overpressure injury range of leak clouds and visualizes the comprehensive failure consequences. Secondly, FLACS is employed to simulate a major leakage and explosion accident at Jinyu Petrochemical Co., Ltd. in Linyi City. The simulation reproduces the leakage and explosion processes and verifies the reliability of the simulation results. Finally, the proposed method is used to visualize the comprehensive failure consequences of this accident, providing comprehensive visual results. The method presented in this paper can serve as a theoretical reference for predicting the development of accidents and clarifying their consequences.

Low-Power Field-Deployable Interdigital Transducer-Based Scanning Laser Doppler Vibrometer for Wall-Thinning Detection in Plates

Lamb waves have become a focal point in ultrasonic testing owing to their potential for long-range and inaccessible detection. However, accurately estimating the flaws in plates using Lamb waves remains challenging because of scattering, mode conversion, and dispersion effects. Recent advances in laser ultrasonic wave techniques have introduced innovative visualization methods that exploit the dispersion effect of Lamb waves to visualize defects via, for example, acoustic wavenumber spectroscopy. In this study, we developed an interdigital transducer (IDT)-based scanning laser Doppler vibrometer (SLDV) system without a power amplifier using a low-power IDT fabricated from lead magnesium niobate–lead zirconate titanate single crystals. To validate the proposed low-power IDT-based SLDV, four different defective plates were measured for defects. A comparison between a conventional IDT-based SLDV, a dry-coupled IDT-based SLDV, and the proposed method demonstrated that the latter is highly reliable for measuring thin plate defects.

A method for measuring hairline length and discriminating hairline recession grades based on the BiSeNet model

Abstract Hair plays an important role in a person’s appearance. According to a survey by the World Health Organization, approximately 70% of adults have scalp and hair prob lems. Doctors currently make hairline recession diagnoses based on hair loss criteria, but this approach is subjective. This paper proposes a novel method for objectively assessing hairline recession grades. First, the Bilateral Segmentation Network model (BiSeNet) is utilized to obtain a facial segmentation image. Second, this paper utilizes the connected components method to improve the facial segmentation results. Next, the labeling key points method is used to extract part of the features of the eyebrow and facial region and calculate the related values. Finally, the judgment of hairline length and hairline recession grade is realized by combining these features with camera calibration. In this paper, front-face images of 50 volunteers were collected for hair line recession grade determination. The judgment results of expert doctors on hairline length and hairline recession grade were compared with the judgment results of this method. The results showed a 1.3 cm difference in the average length of the hairline and about 80% similarity in hairline recession grade judgments. In conclusion, using machine vision methods to measure the height of the hairline provides objective and repeatable results.

High-fidelity Image Restoration of Large 3D Electron Microscopy Volume.

Volume electron microscopy (VEM) is an essential tool for studying biological structures. Due to the challenges of sample preparation and continuous volumetric imaging, image artifacts are almost inevitable. Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used contrast limited adaptive histogram equalization (CLAHE) can alter the essential relative contrast information about some biological structures. We developed an image-processing pipeline to remove the artifacts and enhance the images without CLAHE. We apply our method to VEM datasets of a Microwasp head. We demonstrate that our method restores the images with high fidelity while preserving the original relative contrast. This pipeline is adaptable to other VEM datasets.

Brachial and Lumbosacral Plexopathies.

The brachial and lumbosacral plexuses are complex neural structures that transmit sensory, motor, and autonomic information between the spinal cord and the extremities. Plexus disorders can be particularly disabling because lesions in the plexus usually affect large groups of nerve fibers originating from several spinal levels. Electrodiagnostic studies are often required to confirm a plexus lesion and determine the extent of injury and prognosis. Magnetic resonance is the imaging modality of choice for detecting intrinsic nerve abnormalities; recently, high-resolution ultrasound has emerged as an alternative method for dynamic evaluation and visualization of internal nerve architecture. Once a plexopathy is confirmed, the list of possible etiologies is relatively limited and includes traumatic and nontraumatic causes. Treatment relies on symptom management and physical rehabilitation unless a treatable underlying condition is found. Surgical approaches, including nerve grafts or tendon transfers, may improve limb function when spontaneous recovery is suboptimal.

A Hierarchical Machine Learning Method for Detection and Visualization of Network Intrusions from Big Data

Machine learning is regarded as an effective approach in network intrusion detection, and has gained significant attention in recent studies. However, few intrusion detection methods have been successfully applied to detect anomalies in large-scale network traffic data, and low explainability of the complex algorithms has caused concerns about fairness and accountability. A further problem is that many intrusion detection systems need to work with distributed data sources in the cloud. In this paper, we propose an intrusion detection method based on distributed computing to learn the latent representations from large-scale network data with lower computation time while improving the intrusion detection accuracy. Our proposed classifier, based on a novel hierarchical algorithm combining adaptability and visualization ability from a self-structured unsupervised learning algorithm and achieving explainability from self-explainable supervised algorithms, is able to enhance the understanding of the model and data. The experimental results show that our proposed method is effective, efficient, and scalable in capturing the network traffic patterns and detecting detailed network intrusion information such as type of attack with high detection performance, and is an ideal method to be applied in cloud-computing environments.

Visual Communication Method of Multi-Frame Film and Television Special Effects Images Based on Deep Learning

In the view of art form change, there is a perspective problem in the detail part of multi-frame film and television special effects (TSEs) images in the special effects rendering module, which leads to the greater ambiguity of multi-frame film and TSEs images. In order to advance the talent of identifying the detail structures and characteristics of multi-frame film and TSEs images, a visual communication technology of multi-frame film and TSEs images in the view of art form change grounded on deep learning is suggested. The transmission relation model of detail characteristics of multi-frame film and TSEs images in the field of artistic form change is constructed, and the attenuation treatment of detail action special effects of multi-frame film and TSEs images in the field of artistic form change is carried out with the assistance of deep learning approaches. The detailed information and color of multi-frame film and TSEs images are obtained by using prior map knowledge instead of rendering basic color. Over the technique of filtering and preserving, the multi-frame film and TSEs images are directed to screen the detailed feature quantity of the input film and TSEs images in the field of artistic form change. In addition, the Sobel operator is practiced to perceive the scrawny edge information of multi-frame film and TSEs images, and block fitting interpolation and depth filtering analysis are used in the edge area to realize the visual communication and color feature parameter identification and amplification of multi-frame film and TSEs images in the field of artistic form change, so as to improve the fuzzy enhancement of multi-frame film and TSEs images. The test results and results indicate that the average Peak signal-to-noise ratio (PSNR) of the proposed method is about 37.7166[Formula: see text]dB, which is 49.08% and 52.08% higher than the PSNRs of the Harris method and SUFR method, respectively. It has been confirmed that this scheme can successfully solve the edge blurring and distortion problems of multi-frame movies and image images in the field of art form change.

Rastermap: a discovery method for neural population recordings.

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed 'Rastermap', a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

Super‐Resolved Protein Imaging Using Bifunctional Light‐Up Aptamers

AbstractEfficient labeling methods for protein visualization with minimal tag size and appropriate photophysical properties are required for single‐molecule localization microscopy (SMLM), providing insights into the organization and interactions of biomolecules in cells at the molecular level. Among the fluorescent light‐up aptamers (FLAPs) originally developed for RNA imaging, RhoBAST stands out due to its remarkable brightness, photostability, fluorogenicity, and rapid exchange kinetics, enabling super‐resolved imaging with high localization precision. Here, we expand the applicability of RhoBAST to protein imaging by fusing it to protein‐binding aptamers. The versatility of such bifunctional aptamers is demonstrated by employing a variety of protein‐binding aptamers and different FLAPs. Moreover, fusing RhoBAST with the GFP‐binding aptamer AP3 facilitates high‐ and super‐resolution imaging of GFP‐tagged proteins, which is particularly valuable in view of the widespread availability of plasmids and stable cell lines expressing proteins fused to GFP. The bifunctional aptamers compare favorably with standard antibody‐based immunofluorescence protocols, as they are 7‐fold smaller than antibody conjugates and exhibit higher bleaching‐resistance. We demonstrate the effectiveness of our approach in super‐resolution microscopy in secondary mammalian cell lines and primary neurons by RhoBAST‐PAINT, an SMLM protein imaging technique that leverages the transient binding of the fluorogenic rhodamine dye SpyRho to RhoBAST.