Real Images Research Articles

DEVELOPMENT OF LEAK DETECTION METHODS FOR USE IN FIELD PIPELINE TRANSPORT

Today, most existing leak detection methods are effective only for stationary operating conditions of pipelines. During transient (dynamic) pumping modes, a significant decrease in the sensitivity of the methods occurs, or inoperability of leak detection systems (LDS) based on them is observed.Accordingly, for the operation of the LDS in the presence of transient processes in pipelines, it is necessary to use special approaches that take into account the features and interrelation of the monitored indicators of the dynamically changing pumping regime with each other. One solution to this problem is the use of a dynamic thermal-hydraulic model of the pipeline, which makes it possible to calculate pumping indicators at intermediate and boundary points of the pipeline at each moment in time.The article presents the developed digital model of a pipeline transporting single-phase flows, as a system of equations obtained from the laws of conservation of mass, momentum and energy. The model describes all significant processes and effects during the movement of the pumped flow through a pipeline and allows you to calculate the main characteristics of the flow, including finding the pressure and temperature distributions along the pipeline route.The method for detecting leaks from field pipelines operating under transient (dynamic) conditions is to compare the actual measured flow rate at the end point of the pipeline with the flow rate calculated using a dynamic model of this pipeline, into which the measured pumping indicators at the inlet of a real object are previously transferred. If there are significant deviations between the calculated pumping flow rate at the outlet of the pipeline in the model and the flow rate measured at the pipeline, a leak is concluded.In order to check the performance of the proposed solutions, modeling of leaking pipelines was carried out in specialized software at various flow rates, media viscosities and leak volumes. Based on the data obtained, a conclusion was made about the efficiency of the developed method.Thus, thanks to the proposed approach, it will be possible to promptly detect and eliminate leaks and withdrawals through unauthorized taps on field pipelines operating in unsteady mode, which in turn will significantly reduce operating costs associated with eliminating the consequences of failures.

The importance of multisensory-motor learning on subsequent visual recognition.

Speed of visual object recognition is facilitated after active manual exploration of objects relative to passive visual processing alone. Manual exploration allows viewers to select important information about object structure that may facilitate recognition. Viewpoints where the objects' axis of elongation is perpendicular or parallel to the line of sight are selected more during exploration, recognized faster than other viewpoints, and afford the most information about structure when object movement is controlled by the viewer. Prior work used virtual object exploration in active and passive viewing conditions, limiting multisensory structural object information. Adding multisensory information to encoding may change accuracy of overall recognition, viewpoint selection, and viewpoint recognition. We tested whether the known active advantage for object recognition would change when real objects were studied, affording visual and haptic information. Participants interacted with 3D novel objects during manual exploration or passive viewing of another's object interactions. Object recognition was tested using several viewpoints of rendered objects. We found that manually explored objects were recognized more accurately than objects studied through passive exploration and that recognition of viewpoints differed from previous work.

3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows.

The Impact Of Consumer Environmental Consciousness On Hotel Consumption Intention: A Case Study Of Democratic Republic Of Congo Consumers

The rising interest in "green" hotels is indicative of a broader trend toward eco-consciousness in the hospitality sector. Environmental concerns are currently capturing the interest of Congo's academics, businesses, and governments. Sustainable development is seen to necessitate this. The study looked at how buyers' beliefs, realistic objectives, money, and the hotel's responsibility affected their propensity to spend more for eco-friendly hotels. The purpose of this research was to identify the factors (such as consumer awareness, environmental control policies, and consumer behavior and preferences) that affect the amount that guests are prepared to pay for environmentally friendly hotel rooms. The investigation, which took place in the DRC, used a quantitative approach by analyzing data obtained from 700 hotel visitors using regression analysis. This study contributes to the literature on sustainable tourism by shedding light on the variables that influence financial backing for environmentally conscious hotel projects. It also lays the groundwork for further research, especially in understudied areas like the DRC. There was a strong correlation between environmental consciousness and the results showing that consumers' perceptions of their own efficacy significantly impacted their norms and intentions to do positively. There was a negative moderating effect of perceived price, an external cost, on the link between consumers' behavioral intentions and their personal norms

Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on contrast-free CMR are desirable to overcome the limitations associated with contrast enhancement. We introduce a novel deep generative learning method, termed cine-generated enhancement (CGE), which transforms standard contrast-free cine CMR into LGE-equivalent images for MI assessment. CGE features with multislice spatiotemporal feature extractor, enhancement contrast modulation, and sophisticated loss function. Data from 430 patients with acute MI from 3 centers were collected. After image quality control, 1525 pairs (289 patients) of center I were used for training, and 293 slices (52 patients) of the same center were reserved for internal testing. The 40 patients (401 slices) of the other 2 centers were used for external testing. The CGE robustness was further tested in 20 normal subjects in a public cine CMR data set. CGE images were compared with LGE for image quality assessment and MI quantification regarding scar size and transmurality. The CGE method produced images of superior quality to LGE in both internal and external data sets. There was a significant (P<0.001) correlation between CGE and LGE measurements of scar size (Pearson correlation, 0.79/0.80; intraclass correlation coefficient, 0.79/0.77) and transmurality (Pearson correlation, 0.76/0.64; intraclass correlation coefficient, 0.76/0.63) in internal/external data set. Considering all data sets, CGE demonstrated high sensitivity (91.27%) and specificity (95.83%) in detecting scars. Realistic enhancement images were obtained for the normal subjects in the public data set without false positive subjects. CGE achieved superior image quality to LGE and accurate scar delineation in patients with acute MI of both internal and external data sets. CGE can significantly simplify the CMR examination, reducing scan times and risks associated with gadolinium-based contrasts, which are crucial for acute patients.

A Method for Creating Realistic Synthetic Images Using a Generative Deep Learning Model for Classifying Anomalies in Panoramas

A Method for Creating Realistic Synthetic Images Using a Generative Deep Learning Model for Classifying Anomalies in Panoramas

3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation.

In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs. While plenty of works extend unconditional generative models and achieve some levels of controllability, it is still challenging to ensure multi-view consistency, especially in large poses. In this work, we propose a network that generates 3D-aware portraits while being controllable according to semantic parameters regarding pose, identity, expression and illumination. Our network uses neural scene representation to model 3D-aware portraits, whose generation is guided by a parametric face model that supports explicit control. While the latent disentanglement can be further enhanced by contrasting images with partially different attributes, there still exists noticeable inconsistency in non-face areas when animating expressions. We solve this by proposing a volume blending strategy in which we form a composite output by blending dynamic and static areas, with two parts segmented from the jointly learned semantic field. Our method outperforms prior arts in extensive experiments, producing realistic portraits with vivid expression in natural lighting when viewed from free viewpoints. It also demonstrates generalization ability to real images as well as out-of-domain data, showing great promise in real applications.

The Effects of Inquiry-Based Learning Environment with Augmented Reality Integration on Spatial Reasoning for Topic of Space / Kesan Persekitaran Pembelajaran Berasaskan Inkuiri dengan Integrasi Realiti Terimbuh terhadap Penaakulan Spatial bagi Tajuk Ruang

Students' mastery of the topic of Space in the field of Geometry is also influenced by their spatial reasoning. The use of technology such as augmented reality applications is necessary in assisting students in develop these skills through its ability to display real objects virtually that can be manipulated freely. However, the effectiveness of integrating augmented reality applications requires the implementation of suitable teaching strategies. This is because learning involving difficult concepts requires students not only to receive information directly but also to actively engage in learning activities to generate knowledge and skills. Therefore, this study aims to design an inquiry-based learning environment with the integration of augmented reality and examine its effects on students' spatial reasoning for the topic of Space in Year Three Mathematics. This study used a pre-experimental quantitative research design involving 25 Year Three students. The purposive sampling method was used to select the school as the location of the study and convenience sampling was used to select students as the study respondents. Pre-tests and post-tests were used to measure students' spatial reasoning after the intervention. The results of the descriptive analysis show that there is an increase in the mean score of 6.16 for students' spatial reasoning. The results of the inferential analysis also prove that there is a significant difference in the mean score (Asymp. Sig. (2-tailed) &lt;0.001) between the pre-test and the post-test on students' spatial reasoning. Therefore, the results of this study have had a positive effect on students' spatial reasoning. In conclusion, this study is expected to encourage teachers to apply 21st-century learning strategies along with the integration of appropriate technology tools to design a learning environment of Mathematics learning among primary school students.

A Rapid Head Organ Localization System Based on Clinically Realistic Images: A 3D Two Step Progressive Registration Method with CVH Anatomical Knowledge Mapping.

Rapid localization of ROI (Region of Interest) for tomographic medical images (TMIs) is an important foundation for efficient image reading, computer-aided education, and well-informed rights of patients. However, due to the multimodality of clinical TMIs, the complexity of anatomy, and the deformation of organs caused by diseases, it is difficult to have a universal and low-cost method for ROI organ localization. This article focuses on actual concerns of TMIs from medical students, engineers, interdisciplinary researchers, and patients, exploring a universal registration method between the clinical CT/MRI dataset and CVH (Chinese Visible Human) to locate the organ ROI in a low-cost and lightweight way. The proposed method is called Two-step Progressive Registration (TSPR), where the first registration adopts "eye-nose triangle" features to determine the spatial orientation, and the second registration adopts the circular contour to determine the spatial scale, ultimately achieving CVH anatomical knowledge automated mapping. Through experimentation with representative clinical TMIs, the registration results are capable of labeling the ROI in the images well and can adapt to the deformation problem of ROI, as well as local extremum problems that are prone to occur in inter-subject registration. Unlike the ideal requirements for TMIs' data quality in laboratory research, TSPR has good adaptability to incomplete and non-thin-layer quality in real clinical data in a low-cost and lightweight way. This helps medical students, engineers, and interdisciplinary researchers independently browse images, receive computer-aided education, and provide patients with better access to well-informed services, highlighting the potential of digital public health and medical education.

UKRAINIANS IN POLAND AND UKRAINE: IS THE WAR CRISIS ABLE TO CHANGE PATTERNS OF FINANCIAL BEHAVIOUR

The russian war against Ukraine caused a large wave of displaced people both inside and outside the country. According to UNHCR as of 2023, more than 1.6 million Ukrainian citizens applied for protection status in Poland and 4.9 million people received internally displaced status in Ukraine. This paper aims to analyse the data from a 2023 survey of internally displaced Ukrainians and those who are considered refugees to identify how their financial patterns have changed and what measures can be applied for adjustment to a new environment. The results show that there are no significant changes in patterns of financial behaviour, however, both groups demonstrate coping strategies: more than 50% in both groups shorten their financial horizon plans. Their expenditures are focused mostly on essential needs. In both countries, Ukrainians invest less because of the decrease in their income. As well, the majority in both groups do not invest at all. Bank deposits remain the most popular investment for Ukrainians (in Ukraine is more than 25%, and in Poland is more than 15%). The real estate objects in Ukraine are less attractive because of security as before 2022. At the same time, Ukrainians are searching for alternative ways of investment. Ukrainians demonstrate a high level of using different digital technologies for different purposes, including personal finance. The fintech solutions are recommended to facilitate better decision-making in personal finance and change the patterns of financial behaviour. Fintech can increase the financial inclusion of IDPs in Ukraine and migrants in Poland and direct money flows for investment for further their pension payments.

Image inpainting algorithm based on inference attention module and two-stage network

Current image inpainting techniques often yield distorted structures or blurred textures that clash with the surrounding context, particularly when addressing extensive missing regions or highly textured images. These methods often struggle to reconstruct realistic and coherent image structures. To address this challenge, we introduce a two-stage network image restoration approach that leverages an inferential attention mechanism. In the first stage, an edge generation network is employed to produce plausible phantom edge information. Subsequently, an image complementation network is utilized to complete the image restoration process. To enhance the visual realism and restoration accuracy of the generated images, we incorporate an inferential attention mechanism within the image complementation network. This mechanism effectively mitigates inconsistencies in the generated features, leading to the production of more meaningful and effective information. We evaluate our proposed method on benchmark datasets, including Places2 dataset, CelebFaces Attributes dataset, and Paris StreetView dataset. The experimental results can demonstrate that the proposed method achieves better Structural Similarity Index and Peak Signal-to-noise Ratio than others. These metrics indicate superior performance compared to existing image inpainting methods, delivering higher image inpainting accuracy and more realistic image reconstruction effect.

DIGAN: distillation model for generating 3D-aware Terracotta Warrior faces

Utilizing Generative Adversarial Networks (GANs) to generate 3D representations of the Terracotta Warriors offers a novel approach for the preservation and restoration of cultural heritage. Through GAN technology, we can produce complete 3D models of the Terracotta Warriors’ faces, aiding in the repair of damaged or partially destroyed figures. This paper proposes a distillation model, DIGAN, for generating 3D Terracotta Warrior faces. By extracting knowledge from StyleGAN2, we train an innovative 3D generative network. G2D, the primary component of the generative network, produces detailed and realistic 2D images. The 3D generator modularly decomposes the generation process, covering texture, shape, lighting, and pose, ultimately rendering 2D images of the Terracotta Warriors’ faces. The model enhances the learning of 3D shapes through symmetry constraints and multi-view data, resulting in high-quality 2D images that closely resemble real faces. Experimental results demonstrate that our method outperforms existing GAN-based generation methods.

Analysis of students' mathematical communication skills in terms of learning styles

The purpose of this research is to obtain a description of the mathematical communication skills in terms of the learning styles of Grade XI TKR 3 students at SMK Negeri 1 Tanara for the 2023-2024 academic year. This research is qualitative in nature. Six subjects were selected, representing each learning style, from Grade XI TKR 3 students at SMK Negeri 1 Tanara, Tanara District, Serang Regency. The data collection techniques used were observation, interviews, documentation, and tests based on the indicators of mathematical communication skills, namely: (1) the ability to express mathematical symbols through everyday events, (2) the ability to determine the solution set by relating real objects to mathematical ideas, (3) the ability to explain mathematical problems related to elimination, (4) the ability to analyze mathematical problems related to substitution, and (5) the ability to evaluate mathematical thinking and strategies used in problems according to the observed object. The results showed that (1) subjects with a visual learning style have good mathematical concept comprehension on indicators 1, 2, 3, 4, and 5; (2) subjects with an auditory learning style have good mathematical communication on indicators 1, 2, and 5, but performed poorly on indicators 3 and 4; and (3) subjects with a kinesthetic learning style have good mathematical communication skills on indicators 1 and 2, but performed poorly on indicators 3, 4, and 5.

ConIQA: A deep learning method for perceptual image quality assessment with limited data

Effectively assessing the realism and naturalness of images in virtual (VR) and augmented (AR) reality applications requires Full Reference Image Quality Assessment (FR-IQA) metrics that closely align with human perception. Deep learning-based IQAs that are trained on human-labeled data have recently shown promise in generic computer vision tasks. However, their performance decreases in applications where perfect matches between the reference and the distorted images should not be expected, or whenever distortion patterns are restricted to specific domains. Tackling this issue necessitates training a task-specific neural network, yet generating human-labeled FR-IQAs is costly, and deep learning typically demands substantial labeled data. To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data. This makes ConIQA well-suited for contexts with scarce labeled data. To validate ConIQA, we considered the example application of Computer-Generated Holography (CGH) where specific artifacts such as ringing, speckle, and quantization errors routinely occur, yet are not explicitly accounted for by existing IQAs. We developed a new dataset, HQA1k, that comprises 1000 natural images each paired with an image rendered using various popular CGH algorithms, and quality-rated by thirteen human participants. Our results show that ConIQA achieves superior Pearson (0.98), Spearman (0.965), and Kendall’s tau (0.86) correlations over fifteen FR-IQA metrics by up to 5%, showcasing significant improvements in aligning with human perception on the HQA1k dataset.

Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.

The Influence Mechanism of Internet Celebrity Phenomenon on Career Selection View of Generation Z in China--based on the Cultivation Theory

With the continuous development of information technology, television, as a mainstream media is gradually being replaced by new media, and this new mainstream media is also subtly influencing the career views of ‘Generation Z’ in China. This paper uses the cultivation theory proposed by American scholar Gerbner to analyze how the ‘symbolic reality’ constructed by the Internet celebrity industry affects the construction of ‘subjective reality’ in individuals' minds, and provides a brief overview of the ‘objective reality’ of the Internet celebrity industry in China.

Гражданско-правовое регулирование возникновения долевой собственности в объектах недвижимости

The article examines the features and mechanisms of the emergence of shared ownership in real estate objects based on an analysis of scientific literature and law enforcement practice. In the course of the study, the author revealed that for the emergence of legal relations of common shared ownership, it is necessary to comply with formal conditions and the direct expression of the will of the sole owner of the property, or the consent of all co-owners of such common property. In Russian law, the mechanism of commingling real estate is not possible, as it results in the legal demise of the property. Meanwhile, in doctrine and law enforcement practice, there are concepts of inseparable improvements in property, which also do not entail the emergence of common shared ownership. This pertains to changes made by a lessee to the landlord's real estate. Conclusion dwells upon the fact that the grounds for the emergence of shared ownership are clearly defined by current Russian legislation and do not imply any expansion.

Symmetrical Siamese Network for pose-guided person synthesis

Pose-Guided Person Image Synthesis (PGPIS) aims to generate a realistic person image that preserves the appearance of the source person while adopting the target pose. Various appearances and drastic pose changes make this task highly challenging. Due to the insufficient utilization of paired data, existing models face difficulties in accurately preserving the source appearance details and high-frequency textures in the generated images. Meanwhile, although current popular AdaIN-based methods are advantageous in handling drastic pose changes, they struggle to capture diverse clothing shapes imposed by the limitation of global feature statistics. To address these issues, we propose a novel Symmetrical Siamese Network (SSNet) for PGPIS, which consists of two synergistic symmetrical generative branches that leverage prior knowledge of paired data to comprehensively exploit appearance details. For feature integration, we propose a Style Matching Module (SMM) to transfer multi-level region appearance styles and gradient information to the desired pose for enriching the high-frequency textures. Furthermore, to overcome the limitation of global feature statistics, a Spatial Attention Module (SAM) is introduced to complement the SMM for capturing clothing shapes. Extensive experiments show the effectiveness of our SSNet, achieving state-of-the-art results on public datasets. Moreover, our SSNet can also edit the source appearance attributes, making it versatile in wider application scenarios.

A Smith Predictor Modified with a Pseudo Feedforward Control for the Charge-Coupled Device-Based Optoelectronic Tracking System.

In the high-precision optoelectronic tracking system (OTS) based on a charge-coupled device (CCD), the boresight error extracted from the tracking image contains an undeniable delay, which directly limits the control bandwidth of visual tracking. High bandwidth means high response speed and tracking accuracy. Generally, a model-based delay compensation control method called the Smith predictor is utilized to separate time delay from the closed loop to promote the control bandwidth. However, due to the existence of errors between the established model and the real object, the improvement in the bandwidth is still limited to ensure system stability, resulting in insufficient tracking performance. In this paper, to solve the problem, a Smith predictor modified with pseudo feedforward control for the OTS is proposed. The experimental results demonstrate that the proposed method achieves significant improvements in tracking performance, reducing the maximum residual error at 1 Hz from 365 arcseconds (using the classic Smith predictor) to 283 arcseconds, a 22.5% improvement. Across the main frequency band (0.2 Hz to 2 Hz), the residual errors were consistently lower using the proposed method.

Building Face Ageing Model Using Face Synthesis

Advancements in face synthesis technology have enabled innovative methods for modeling facial aging. This research paper focuses primarily on creating a robust face aging model using deep learning and Generative Adversarial Networks (GANs), trained on a diverse dataset of facial images. The proposed approach captures both global features and local textures to produce realistic age-progressed images while preserving the subject's identity. This paper also examines face synthesis techniques, with specific emphasis for the various practical usage of GANs. The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age- progressed face images. We evaluated the model using quantitative metrics and qualitative assessments, demonstrating its effectiveness. Additionally, we address ethical considerations, proposing guidelines for responsible use. Our study offers a novel framework with significant applications in security, forensics, and entertainment, and suggests future research directions to improve accuracy and ethical standards.