Real Images Research Articles

Patient-Perceived Disability Following Transtibial Amputation and Prosthetic Limb Fitting

ABSTRACT Introduction Patients’ perception of their disability following lower-limb amputation is currently measured using patient-reported outcomes (PROs) instruments. The validity is determined by comparing instruments designed to measure patient-perceived impact on amputation-specific health-related quality of life. The goal of this investigation was to compare patient-perceived disability from an outcomes instrument specific to individuals with amputation to a well-accepted outcomes instrument used to measure disability and response to treatment in generic musculoskeletal disease. Materials and Methods Twenty-one patients completed the Prosthesis Evaluation Questionnaire (PEQ) and the Short Musculoskeletal Functional Assessment (SMFA) at a minimum of 3 months following transtibial amputation while undergoing prosthetic limb fitting and gait training. A functional measure of their walking ability based on Medicare ambulatory function (Amputee Mobility Predictor [AMP]) was scored by a trained rehabilitation professional. Results The results of the outcomes scores provided initial evidence that the PEQ and SMFA assessments are correlated (i.e., provide some evidence of convergent validity). For every 100-point improvement in patients’ PEQ score, their SMFA Bother score declined by approximately 7 points (b = −6.94; P = 0.001) and Dysfunction score declined by approximately 7 points (b = −6.74; P < 0.001). Conversely, the SMFA and PEQ subscores were not associated with the AMP K-level (all P's > 0.05). Conclusions The findings suggested that the PEQ and SMFA PRO instruments are correlated in their ability to measure impact on health-related quality of life. Additionally, we found that PROs (PEQ and SMFA) did not predict physical ability as measured by the AMP K-level. The value of this investigation suggests that the easy-to-administer SMFA correlates well with the accepted PEQ, making testing both less cumbersome and time-consuming. Neither measure correlated with eventual ambulatory function. Rehabilitation following lower-limb amputation demands patient-specific goals. Available tools appear valuable in assisting the rehabilitation team in developing realistic objective tools for rehabilitation following lower-limb amputation. Clinical Relevance The simple-to-use SMFA appears to be comparable to the more cumbersome PEQ in assessing patient outcomes following transtibial amputations. Neither outcome instrument is correlated with functional task performance in this patient population.

Social construction of geographicity: A vision from humanistic geography

Society is a human construct in a continuous historical process, hence, also geographical. The Social is always composed of a temporal and spatial dimension and, therefore, necessarily endowed with historicity and geographicity. According to Dardel (2022), geographicity is the existential connection of the human being with the earth, that is, the spatial experience obtained by the subjective apprehension of the objective world. This article aims to establish a revaluation of the concept of geographicity in the social sciences debate, discerning it as a social construct by considering theoretical foundations from humanistic geography, phenomenology, and the sociology of knowledge, linked as a proposal for an epistemological alternative in the field of theoretical geography itself. The concepts of space, landscape, place, perception of space, material space, conceived space, and lived space are the key concepts that interlace the dialectical relationship between the objective and subjective manifestations of reality, as a way of socially constructing geography. The result is, therefore, the evident epistemic fertility of humanistic geography, presented in this work through the theoretical review regarding a social construction of geographicity.

Spectrum Reconstruction Model Based on Multispectral Electrochromic Devices.

Reconstructing the visible spectra of real objects is critical to the spectral camouflage from emerging spectral imaging. Electrochromic materials exhibit unique superiority for this goal due to their subtractive color-mixing model and structural diversity. Herein, a simulation model is proposed and a method is developed to fabricate electrochromic devices for dynamically reproducing the visible spectrum of the natural leaf. Over 20 kinds of pH-dependent leuco dyes have been synthesized/prepared through molecular engineering and offered available spectra/bands to reconstruct the spectrum of the natural leaf. More importantly, the spectral variance between the device and leaf is optimized from an initial 98.9 to an ideal 10.3 through the simulation model, which means, the similarity increased nearly nine-fold. As a promising spectrum reconstruction approach, it will promote the development of smart photoelectric materials in adaptive camouflage, spectral display, high-end encryption, and anti-counterfeiting.

A formalization of Descartes’ causal argument for existence of God

In this paper I attempt a formalization of Descartes’ causal argument for the existence of God presented in the Third Meditation. By taking into account the philosophical setting of Descartes’ Meditations, I argue that the best logical theory to carry out the formalization is quantified modal logic where free logic is used for the first-order axioms. This fact is related to Descartes’ distinction between formal and objective reality which — to my mind — suggests both the adoption of modality (e.g. true in formal vs true in objective reality) and the possibility of using empty names (i.e. names which lack reference in “formal reality”). After presenting the formal system and the reconstruction of Descartes’ proof, I will discuss some issues related to the philosophical import of the formalization, such as the understanding of rigid designation and lack of reference. I will argue that the formalization of Descartes’ argument has an intrinsic philosophical value for it sheds light on the structure of argument, its implicit difficulties, and the use of logic itself to understand the philosophical tradition.

Four-Dimensional Digital Monitoring and Registering of Historical Architecture for the Preservation of Cultural Heritage

Preserving cultural heritage through monitoring, registering, and analyzing damage in historical architectural structures presents significant financial and logistical burdens. Developed approaches for monitoring and registering 4D (4-dimensional)-scanned range and raster images of damaged objects were investigated in a case study of historical Baron Palace in Egypt. In the methodology, we first prepared and observed the damaged historical models. The damaged historical models were scanned using a laser scanner at a predetermined date and time. Simultaneously, digital images of the models were captured (by a calibrated digital camera) and stored on a researcher’s tablet device. By observing and comparing the scanned models with the digital images, geometric defects and their extent are identified. Then, the observed data components were detected on the map. Then, damaged statue materials were investigated using system of energy dispersive (SEM; scanning electron microscope, Gemini Zeiss-Ultra 55) and XRF (X-ray fluorescence) spectroscopic analysis to identify the statue’s marble elements, and the results indicate that SEM-EDX and XRF analyses accurately identify major and minor compositions of the damaged statue. Then, the damaged models were registered in two stages. In the registration stages, the corresponding points were determined automatically by detecting the closest points in the clouds and ICP (iterative closest point) algorithm in RiSCAN. The point clouds (of the Palace and damaged statues) gave very detailed resolutions and more realistic images in RiSCAN, but it is a costly program. Finally, the accuracies of the registration tasks were assessed; the standard deviations are within acceptable limits and tend to increase irregularly as the number of polydata observations used in the registration calculations increase.

Usability of visualizing position and orientation deviations for manual precise manipulation of objects in augmented reality

Manual precise manipulation of objects is an essential skill in everyday life, and Augmented Reality (AR) is increasingly being used to support such operations. In this study, we investigate whether detailed visualizations of position and orientation deviations are helpful for AR-assisted manual precise manipulation of objects. We developed three AR instructions with different visualizations of deviations: the logical deviation baseline instruction, the precise numerical deviations-based instruction, and the intuitive color-mapped deviations-based instruction. All three instructions visualized the required directions for manipulation and the logical values of whether the object met the accuracy requirements. Additionally, the latter two instructions provided detailed visualizations of deviations through numerical text and color-mapping respectively. A user study was conducted with 18 participants to compare the three AR instructions. The results showed that there were no significant differences found in speed, accuracy, perceived ease-of-use, and perceived workload between the three AR instructions. We found that the visualizations of the required directions for manipulation and the logical values of whether the object met the accuracy requirements were sufficient to guide manual precise manipulation. The detailed visualizations of the real-time deviations could not improve the speed and accuracy of manual precise manipulation, and although they could improve the perceived ease-of-use and user experience, the effects were not significant. Based on the results, several recommendations were provided for designing AR instructions to support precise manual manipulation.

Usability of visualizing position and orientation deviations for manual precise manipulation of objects in augmented reality

Manual precise manipulation of objects is an essential skill in everyday life, and Augmented Reality (AR) is increasingly being used to support such operations. In this study, we investigate whether detailed visualizations of position and orientation deviations are helpful for AR-assisted manual precise manipulation of objects. We developed three AR instructions with different visualizations of deviations: the logical deviation baseline instruction, the precise numerical deviations-based instruction, and the intuitive color-mapped deviations-based instruction. All three instructions visualized the required directions for manipulation and the logical values of whether the object met the accuracy requirements. Additionally, the latter two instructions provided detailed visualizations of deviations through numerical text and color-mapping respectively. A user study was conducted with 18 participants to compare the three AR instructions. The results showed that there were no significant differences found in speed, accuracy, perceived ease-of-use, and perceived workload between the three AR instructions. We found that the visualizations of the required directions for manipulation and the logical values of whether the object met the accuracy requirements were sufficient to guide manual precise manipulation. The detailed visualizations of the real-time deviations could not improve the speed and accuracy of manual precise manipulation, and although they could improve the perceived ease-of-use and user experience, the effects were not significant. Based on the results, several recommendations were provided for designing AR instructions to support precise manual manipulation.

MODERNIZATION OF REAL ESTATE VALUАTION METHODOLOGY BY COMPARATIVE METHOD

This study is devoted to the issue of determining the price of a real estate object based on the use of statistical market evaluations of similar objects. In the course of the research, a technique for determining the price of a real estate object was developed using parametric equations of the least squares method, which allows using an unlimited amount of input information and obtaining equalized values of price-forming factors. In contrast to the existing methods of the comparative approach to determining the price of the object of assessment, the proposed method allows you to obtain the value of the price-forming factors, and then the price of the object without establishing the difference between the factors influencing the price, as is the case with the classical approach of processing the input database. Approbation of the developed methodology showed the effectiveness of the possibility of its application in practice.

MODERNIZATION OF REAL ESTATE VALUАTION METHODOLOGY BY COMPARATIVE METHOD

This study is devoted to the issue of determining the price of a real estate object based on the use of statistical market evaluations of similar objects. In the course of the research, a technique for determining the price of a real estate object was developed using parametric equations of the least squares method, which allows using an unlimited amount of input information and obtaining equalized values of price-forming factors. In contrast to the existing methods of the comparative approach to determining the price of the object of assessment, the proposed method allows you to obtain the value of the price-forming factors, and then the price of the object without establishing the difference between the factors influencing the price, as is the case with the classical approach of processing the input database. Approbation of the developed methodology showed the effectiveness of the possibility of its application in practice.

Mask-guided dual-perception generative adversarial network for synthesizing complex maize diseased leaves to augment datasets

In practice, acquiring and annotating data in specialized domains can be costly, thereby constraining the performance and applicability of deep learning. Utilizing generative models to synthesize data proves to be an effective augmentation technique. Therefore, this research proposes a diseased leaf generation pipeline to diversify the maize disease datasets. We introduce the Dual-Perception Cycle-Consistent Generative Adversarial Network (DP-CycleGAN). During training, incorporating our proposed Structure Perception (SP) loss and Texture Perception (TP) loss functions. These losses guide the model's attention areas through activation reconstruction and mask mechanisms, thereby improving the overall perceptual quality of the generated images and the realism of the disease lesions. We constructed a maize leaf mixed disease dataset to simulate the complex conditions of real-world disease occurrence. Experimental results show that the DP-CycleGAN generates higher-quality and more realistic diseased leaf images. Compared to CycleGAN and state-of-the-art method, DP-CycleGAN shows a 29.6% and 15.7% reduction in Fréchet Inception Distance (FID) scores and a 125.3% and 61.5% increase in Structural Similarity (SSIM) values, respectively. Simultaneously, by incorporating synthetic data during training, our approach significantly enhances the performance of the recognition model in scenarios of both data abundance and scarcity, with improvement rates exceeding two times those of existing state-of-the-art methods. This contributes to the application of Artificial Intelligence (AI) in agricultural production practices.

Predictive Assessment of the Interior Quality of Apartments Based on Multimodal Data with Variable Visual Input

Multimodal analyses in the context of automated real estate valuation (AVM) offer the possibility of enriching the models with additional information, which benefits the accuracy of the models. However, this variety of data can overwhelm common machine learning models, which generally only process certain data modalities and only a fixed data quantity. This leads to a bottleneck in information processing, as in many cases much more information is available per observation, of which only a single selection or sample is used to train the algorithm and the remaining information is disregarded. We propose a multimodal network architecture that incorporates both textual and visual inputs and fuses their information. Furthermore, we introduce a training strategy that can take advantage of a variable number of input images for each real estate object. In our experiments, we test and compare several unimodal (baseline) models with our multimodal architecture. Our approach shows several advantages in terms of model performance over unimodal approaches. The results show the best performance for the multimodal model with a variable number of visual inputs, as well as improved prediction for the underrepresented classes of indoor quality, mitigating the effects of unbalanced data. With the presented approach, which efficiently combines and merges multiple data modalities, we have shown how such a method can be easily adapted to an AVM for the extraction of supplementary information.

FastFaceCLIP: A lightweight text‐driven high‐quality face image manipulation

AbstractAlthough many new methods have emerged in text‐driven images, the large computational power required for model training causes these methods to have a slow training process. Additionally, these methods consume a considerable amount of video random access memory (VRAM) resources during training. When generating high‐resolution images, the VRAM resources are often insufficient, which results in the inability to generate high‐resolution images. Nevertheless, recent Vision Transformers (ViTs) advancements have demonstrated their image classification and recognition capabilities. Unlike the traditional Convolutional Neural Networks based methods, ViTs have a Transformer‐based architecture, leverage attention mechanisms to capture comprehensive global information, moreover enabling enhanced global understanding of images through inherent long‐range dependencies, thus extracting more robust features and achieving comparable results with reduced computational load. The adaptability of ViTs to text‐driven image manipulation was investigated. Specifically, existing image generation methods were refined and the FastFaceCLIP method was proposed by combining the image‐text semantic alignment function of the pre‐trained CLIP model with the high‐resolution image generation function of the proposed FastFace. Additionally, the Multi‐Axis Nested Transformer module was incorporated for advanced feature extraction from the latent space, generating higher‐resolution images that are further enhanced using the Real‐ESRGAN algorithm. Eventually, extensive face manipulation‐related tests on the CelebA‐HQ dataset challenge the proposed method and other related schemes, demonstrating that FastFaceCLIP effectively generates semantically accurate, visually realistic, and clear images using fewer parameters and less time.

Generative adversarial networks for handwriting image generation: a review

AbstractHandwriting synthesis, the task of automatically generating realistic images of handwritten text, has gained increasing attention in recent years, both as a challenge in itself, as well as a task that supports handwriting recognition research. The latter task is to synthesize large image datasets that can then be used to train deep learning models to recognize handwritten text without the need for human-provided annotations. While early attempts at developing handwriting generators yielded limited results [1], more recent works involving generative models of deep neural network architectures have been shown able to produce realistic imitations of human handwriting [2–19]. In this review, we focus on one of the most prevalent and successful architectures in the field of handwriting synthesis, the generative adversarial network (GAN). We describe the capabilities, architecture specifics, and performance of the GAN-based models that have been introduced to the literature since 2019 [2–14]. These models can generate random handwriting styles, imitate reference styles, and produce realistic images of arbitrary text that was not in the training lexicon. The generated images have been shown to contribute to improving handwriting recognition results when augmenting the training samples of recognition models with synthetic images. The synthetic images were often hard to expose as non-real, even by human examiners, but also could be implausible or style-limited. The review includes a discussion of the characteristics of the GAN architecture in comparison with other paradigms in the image-generation domain and highlights the remaining challenges for handwriting synthesis.

Beyond the revised atlanta classification: a comprehensive review of the imaging assessment of acute pancreatitis and its complications.

While the Revised Atlanta Classification outlines the primary complications of acute pancreatitis, it is crucial to recognize additional factors that contribute to morbidity and mortality associated with acute pancreatitis. In this review, we discuss the imaging-based classification and staging of acute pancreatitis as described by the Revised Atlanta Classification, but also provide a comprehensive understanding of the pancreatic anatomy and its relation to surrounding structures, which is essential for imaging-based assessment of both acute pancreatitis and its complications. We further extend the discussion beyond common complications such as pseudocysts and walled-off necrosis to include lesser-known but significant complications such as peripancreatic infection, disconnected ductal disconnection syndrome, thrombosis, hemorrhage, and gastrointestinal complications. Additionally, illustrative examples are presented to highlight relevant points pertaining to real-life imaging assessment of acute pancreatitis and its complications.

RD-FGM: A novel model for high-quality and diverse food image generation and ingredient classification

Food image generation plays a crucial role in evaluating multiple food ingredients, predicting dietary preferences, recommending food, and computing dietary nutrition. However, this task is challenging due to the large variation in the appearance of recipe components, the difficulty in aligning multi-modal features, and the lack of diversity in generated data. To address these challenges, we propose a novel RecipeCLIP-Diffusion Food Generation Model (RD-FGM), which facilitates high-quality diverse image generation while accomplishing multi-modal feature alignment. Specifically, the RecipeCLIP model implements a multi-ingredient embedding of image-text pairs for aligning contextual features. Additionally, we devise a multi-conditional guided diffusion model that achieves data distribution learning and generation control. We evaluate RD-FGM on the large-scale Recipe1M and VIREO Food-172 Chinese datasets. Many experiments including quantitative analyses, qualitative analyses, and ablation studies demonstrate the effectiveness of RD-FGM. In addition, we further model migration experiments to evaluate its scalability for other downstream tasks such as ingredient classification. The capability to generate realistic food images from textual recipes opens up new avenues for exploring culinary creations, food and ingredients classification, promising various applications in the food industry and beyond.

SCGAN: Semi-Centralized Generative Adversarial Network for image generation in distributed scenes

Distributed Generative Adversarial Networks (GANs) are pivotal for generative tasks in distributed data environments. Conventional architectures, whether centralized or decentralized, inadequately balance global and local data distribution integration. We propose the Semi-Centralized Distributed GAN (SCGAN), a hybrid framework combining the merits of both approaches. SCGAN employs dual generators—one decentralized, one centralized—and a feature extractor that optimizes their outputs by dynamically calculating feature distances, thereby achieving a balanced semi-centralized generator. Extensive experiments on five benchmark image datasets reveal that SCGAN surpasses existing models, delivering superior Fréchet Inception Distance (FID) scores and generating highly realistic images. Moreover, SCGAN’s robustness and versatility are demonstrated through its efficacy in downstream applications, including image augmentation and distributed privacy protection. Notably, SCGAN’s generated images were evaluated using a distributed classifier, a quality metric (IS), and human observation, consistently outperforming existing methods. This study highlights SCGAN’s significant advancements in distributed generative modeling and its nuanced approach to managing complex data distributions, particularly excelling in privacy-preserving scenarios.

CORRELATION OF THE TERMS "CLASSIFICATION ATTRIBUTE", "MOTIVATIONAL ATTRIBUTE", "SEMANTIC ATTRIBUTE"

The problem of distinguishing the terms "classification attribute", "motivational attribute", "semantic attribute" arose in connection with the active development of cognitive linguistics and the expansion of its terminology. The article describes for the first time approaches to understanding terms in a comparative aspect. The author analyzes dictionary definitions, consistently identifying classification, motivational, semantic features of lexemes, demonstrating the differences between these features. The author comes to the conclusion that the terms "classification attribute", "motivational attribute", "semantic attribute" differ functionally, their use is due to the paradigm and the field of linguistics in which the researcher works. Purpose. The purpose of the study is to distinguish the terms "classification attribute", "motivational attribute", "semantic attribute". Materials and methods. The material for the study was contexts with these terms extracted from scientific papers on cognitive linguistics, word formation, theoretical semantics, as well as dictionary entries. The descriptive and comparative methods, and method of theoretical analysis, were used. Results. The results of the study showed that the terms "motivational attribute", "semantic attribute", "classification attribute" differ functionally. The study of motivational attribute is aimed at identifying linguistic patterns. The study of semantic attribute is aimed at describing lexical (linguistic) meanings. The analysis of classification attribute aims to identify extralinguistic, namely cognitively significant information about the objects of reality. Practical implications. The results of the study can be used in lexicographic and translation practice, in research on issues of nomination, lexical meaning and linguistic categorization.

CORRELATION OF THE TERMS "CLASSIFICATION ATTRIBUTE", "MOTIVATIONAL ATTRIBUTE", "SEMANTIC ATTRIBUTE"

The problem of distinguishing the terms "classification attribute", "motivational attribute", "semantic attribute" arose in connection with the active development of cognitive linguistics and the expansion of its terminology. The article describes for the first time approaches to understanding terms in a comparative aspect. The author analyzes dictionary definitions, consistently identifying classification, motivational, semantic features of lexemes, demonstrating the differences between these features. The author comes to the conclusion that the terms "classification attribute", "motivational attribute", "semantic attribute" differ functionally, their use is due to the paradigm and the field of linguistics in which the researcher works. Purpose. The purpose of the study is to distinguish the terms "classification attribute", "motivational attribute", "semantic attribute". Materials and methods. The material for the study was contexts with these terms extracted from scientific papers on cognitive linguistics, word formation, theoretical semantics, as well as dictionary entries. The descriptive and comparative methods, and method of theoretical analysis, were used. Results. The results of the study showed that the terms "motivational attribute", "semantic attribute", "classification attribute" differ functionally. The study of motivational attribute is aimed at identifying linguistic patterns. The study of semantic attribute is aimed at describing lexical (linguistic) meanings. The analysis of classification attribute aims to identify extralinguistic, namely cognitively significant information about the objects of reality. Practical implications. The results of the study can be used in lexicographic and translation practice, in research on issues of nomination, lexical meaning and linguistic categorization.

VISUALIZATION OF THREATS IN CIVIL SECURITY USING THREE-DIMENSIONAL SPATIAL MODELING

The article analyzes the main concepts and methods of modeling used in civil security to solve the issues of analyzing the prerequisites for the occurrence of hazards, by forecasting, preventing and managing their components. On-site research, in particular, direct measurements, can only be carried out on an already existing object. Modeling today is one of the important methods, the main methodology of which is to learn the essence of natural and social phenomena of various levels of complexity. It provides an opportunity to demonstrate various systems related to civil security using methods of applied mathematics, mathematical statistics and optimization. The task of analysis and forecasting on any object can be reduced to building a model that reflects the behavior of a natural object. The main tasks of this article are to establish modeling principles and methods for their use in civil security using the example of modeling noise maps from various sources, one of which is construction in a dense residential area. The task of analysis and forecasting on almost any object can be reduced to building a model that adequately reflects the behavior of a natural object. The basic principles on which modeling is based are defined. The principle of information sufficiency – building its model is impossible in the complete absence of information about the object. Feasibility principle – the model should ensure the achievement of the research goal with a non-zero probability value. The principle of multiplicity of models - the model should reflect exactly those properties of a real object or system that are of interest to the researcher. The principle of aggregation - the system is better decomposed as a set of subsystems, for the description of which standard schemes can be used. Principle of parameterization – the model must be built in the form of a known system, the parameters of which are unknown. Crisis simulation training is a common organizational tool for improving civil security preparedness. Although today its general advantages cannot be denied, the conditions under which these simulations offer opportunities for improvement often remain unclear.

AESTHETIC ASPECTS OF IDEAL IMAGE IN THE PHILOSOPHICAL CONCEPTS OF RENAISSANCE

The article analyzes the aesthetic aspects of the ideal as a reflection of the subjective concept within the philosophical concepts of Renaissance thinkers N. Kuzansky, M. Ficino, L. Alberti, L. da Vinci. The author considered certain aesthetic aspects in the context of the categorical trinity "ideal-beautiful-beautiful". This study can become the basis for the study of the Renaissance individual in terms of his active worldview of the essential and pragmatic basis of existence, having absolute vision, capable of creating ideal images. The relevance of the work is due to the interest in the study of this aspect of applied aesthetics both in our country and abroad in connection with the scientific development of the study of human capabilities. The purpose is to analyze and study the concepts of Renaissance philosophers to reveal the meaning of the trinity "ideal-beautiful-beautiful" in the context of actualization of the study of the ideal image at subjective-individual visual perception. Materials and methods. The method of research is comparative analysis; method of interpretation of scientific philosophical works; method of synthesis and analogy. Results. The works of Renaissance thinkers were studied in order to clarify the concept of ideal image. It is shown that ideal images materialize in the subjective space of an individual in objects, as their pragmatic value-oriented component is highlighted in a number of objects similar to it. The signs of the ideal or absolute in the object, which became part of the essential basis of objective reality in the Renaissance, are analyzed, the qualities of the person of this era are highlighted, and his ideality in the subjective space is defined.