Real Images Research Articles

The ground for necessary defence: analysis of legal positions of the Supreme Court

The article analyzes and summarizes the positions of the Supreme Court on the ground for necessary defence. It is noted that the Supreme Court has identified two elements of the ground for necessary defence (the commission of a socially dangerous trespass and the need to immediately avert or stop it) and has repeatedly referred to the interpretation of various aspects of each of them. The article mentions various definitions of “socially dangerous trespass” provided by the Supreme Court in its rulings. It is noted that the Supreme Court has not addressed the possibility of recognizing a socially dangerous act of an insane person or a person under the age of criminal liability as a socially dangerous trespass as a ground for necessary defence, but in one of its rulings it admitted that such a trespass was committed by a mentally ill victim. The author mentions the conclusion of the Supreme Court that the right to necessary defence cannot arise against lawful acts of another person, even if such acts cause damage to legally protected interests. It is mentioned that, in general, the Supreme Court does not recognize provoked violence as a trespass within the meaning of Article 36 of the Criminal Code of Ukraine, but the exception is when the degree of provoked violence is clearly higher than what a person could have predicted. It is noted that the Supreme Court identifies the objective reality of a socially dangerous trespass as its mandatory characteristic for necessary defence. The author quotes the Supreme Court’s explanation of when it is necessary to immediately avert or stop a socially dangerous trespass. The conclusions of the Supreme Court regarding the time limits of the right to necessary defence are analyzed. In particular, the Supreme Court identifies the initial and final moments of lawful defence. The ruling is mentioned, in which the Supreme Court reminds of the legal prohibition to take into account the possibility of a person to avoid a socially dangerous trespass or to request assistance of other persons or authorities when deciding on the existence or absence of a state of necessary defence.

Значение категории «земля» как отправная точка правового регулирования

The legally established meaning of land is the starting point not only for land law, but also for public policy in general. The modern meaning of land covers three aspects: land as a natural object, land as a natural resource and land as a real estate object. Ways to improve land legislation from the point of view of specifying the meaning of land are seen in ensuring the rational use and protection of land, the priority of protecting human life and health when making decisions on the use of land, and the participation of citizens in resolving issues related to the use of land in their place of residence. It is proposed to expand the meaning of land in the future by recognizing the emotional and moral connection of a person with the land as a value subject to protection, as well as regulating the rights of future generations in relation to the land.

Generation of NIR Spectral Band from RGB Image with Wavelet Domain Spectral Extrapolation Generative Adversarial Network

Near-infrared (NIR) imaging exhibits outstanding penetration capabilities and robust anti-interference characteristics. However, acquiring high-resolution and high-fidelity NIR images is difficult due to the limited mobility, high cost, and low resolution of NIR imaging hardware. In this paper, we proposed a new end-to-end Wavelet Domain Spectral Extrapolation Generative Adversarial Network (WSEGAN) to generate highly realistic NIR images from RGB images. Since RGB and NIR images have different types of noise and artifacts, which affects the quality of NIR images generation. We design a generator with a discrete wavelet transform and an attention mechanism to capture multi-resolution contextual information, and a multi-scale discriminator to capture detailed and global features. Then, the proposed approach is evaluated by three different datasets to achieve optimal results regarding both visual effects and quantitative evaluation. The normalized difference vegetation index (NDVI) is utilized to validate the effectiveness of the generated NIR images. The result demonstrates a strong correlation between the generated images and the actual vegetation distribution. More importantly, the proposed network is testified to generate NIR images to be fused with the RGB image source for agricultural target detection tasks. In dark conditions, results show that using multi-modal data instead of RGB images improves mAP0.5 detection accuracy by 4% on the CAPSICUM dataset and by 8% on the KIWI dataset across five object detection methods. This follows the physical significance of the NIR imaging and demonstrates the potential use of the NIR images generated by the proposed method.

Overview of textile tensile surface structures with the largest uniform area

The innovative design of tensile surface structures is often a distinguishing feature of the entire construction project. The use of membranes gives buildings a sense of lightness and often a futuristic look. The construction materials used in the design of membrane roofing are, in addition to the steel cables tensioning and supporting the roof membrane, technical fabrics. This article focuses on describing structures made of continuous tensioned membranes spanning a rope or bar network with a total uniform area of more than 20,000 m2. Five structures with the largest uniform load-bearing area are described. The most commonly used yarn materials and coatings are described. The basic strength parameters of the most commonly used fabrics are summarized. The principles of shaping membrane surfaces were described. Attention was drawn to the difficulty of transferring laboratory test results to real objects. It is very likely that, for reasons of usability and manufacturing technology, continuous membrane surfaces with larger areas than described will no longer be designed.

Faceify- Human Face Sketch to Real

The Human Face Sketch to Real Image Application using deep learning presents the development of an innovative application designed to convert human face sketches into realistic image using advanced deep learning techniques. The primary objective of this research is to bridge the gap between manual sketching and digital realism, offering a powerful tool for artists, law enforcement, and digital content creators. The proposed system leverages generative adversarial networks (GANs), particularly a variant known as the Sketch-to-Image GAN (SI-GAN), which is optimizes for translating sparse and abstract sketch lines into high-fidelity facial representations. The architecture consists of a generator network that refines sketches into realistic images, and a discriminator network that distinguishes between generated images and real photographs, continuously enhancing the model’s output. To ensure high accuracy and detail preservation, the model is trained on a diverse dataset comprising thousands of paired sketch and real face images. Advanced techniques such as attention mechanisms and multi-scale feature extraction are incorporated to maintain the integrity of facial features and textures. Additionally, a user friendly interface has been developed, allowing users to input sketches and receive high-quality realistic images in real-time. Evaluation metrics, including structural similarity index (SSIM) and Frechet Inception Distance (FID), Indicate that the proposed model outperforms existing state-of-the-art methods in both quality and realism. Keywords: Image Translation, Conditional Generative Adversarial Networks (CGANs), Deep Learning, Mobile Application Development, Backend Integration, Face Sketch, Photorealistic Image.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

The Effectiveness of Manipulating Real Objects in Enhancing Retention Among Grade 7 Students Struggling With Concepts of Angles

The Effectiveness of Manipulating Real Objects in Enhancing Retention Among Grade 7 Students Struggling With Concepts of Angles

Two-stage deep learning framework for occlusal crown depth image generation

The generation of depth images of occlusal dental crowns is complicated by the need for customization in each case. To decrease the workload of skilled dental technicians, various computer vision models have been used to generate realistic occlusal crown depth images with definite crown surface structures that can ultimately be reconstructed to three-dimensional crowns and directly used in patient treatment. However, it has remained difficult to generate images of the structure of dental crowns in a fluid position using computer vision models. In this paper, we propose a two-stage model for generating depth images of occlusal crowns in diverse positions. The model is divided into two parts: segmentation and inpainting to obtain both shape and surface structure accuracy. The segmentation network focuses on the position and size of the crowns, which allows the model to adapt to diverse targets. The inpainting network based on a GAN generates curved structures of the crown surfaces based on the target jaw image and a binary mask made by the segmentation network. The performance of the model is evaluated via quantitative metrics for the area detection and pixel-value metrics. Compared to the baseline model, the proposed method reduced the MSE score from 0.007001 to 0.002618 and increased DICE score from 0.9333 to 0.9648. It indicates that the model showed better performance in terms of the binary mask from the addition of the segmentation network and the internal structure through the use of inpainting networks. Also, the results demonstrated an improved ability of the proposed model to restore realistic details compared to other models.

Using immersive virtual environments for teaching and socializing children with autism spectrum disorder

The authors' research interests are focused on the development of preschool children with autism spectrum disorder and the potential of virtual reality technologies for teaching and developing children in a gaming environment. The article reviews articles published in peer-reviewed journals and posted in the Scopus database and the Google Scholar search engine for scientific publications over the past 10 years. The objective of the review is to identify international research experience regarding the impact of virtual environments and hardware on children with autism spectrum disorder, their potential effectiveness, recommended conditions and limitations of using virtual reality for social adaptation, development of communication, cognitive and motivational skills. The articles analyzed in the review focus on the following issues: the potential and results of using different types of virtual reality technologies for developing various skills and abilities in children with autism; using virtual reality to work with problems and risks; as well as the features, limitations and safety of using virtual reality headsets by children with autism spectrum disorder. It is recommended to use immersive virtual environments for children with autism under the following conditions: the presence of close and trusted people (caregivers or parents), the presence of real game objects for switching children after exiting the virtual environment and distracting attention from possible negative effects of using gadgets, continuing communication with the child after a virtual reality session in a normal physical space to practice the skill being taught, specific time limits for staying in the virtual environment, etc. It is concluded that the results of using virtual reality technologies remain contradictory, but valid data from scientific research allow us to assert that the use of immersive virtual environments is acceptable, can be effective for teaching children with autism spectrum disorder and, if certain conditions are met, is adequately perceived by them.

Science, Fiction, and Contemporary Media: Blurred Borders in Knowledge Dissemination in Michael Crichton’s The Andromeda Strain

This study examines the technical methods used by Michael Crichton to blur the lines between factual information and fictional elements in his novel The Andromeda Strain. The creative use of scientific terminology and artistic documentation that supports a sense of reality plays a role in creating a certain blurring that works to enhance the blurring of the boundaries between reality and imagination. The study aims to explore the novel within the framework of postmodern literary theory, questioning the concept of absolute truth, and emphasizing the role of language and literary discourse in constructing reality. Through a critical analysis of the novel, this study seeks to clarify the methods followed by Crichton to employ language and discourse to create an atmosphere of authenticity that contributes greatly to blurring the line between reality and imagination. In addition, the study comprehensively analyses modern media platform technologies that are used to erode the boundaries between objective reality and fictional narratives, and their consequent effects on the dissemination of information. The study concluded that Crichton's novel The Andromeda Strain embodies the successful integration of scientific concepts with fantasy elements, which resulted in a captivating and intellectually stimulating narrative.

A facile approach towards large-scale synthesis of TiO2 nanoparticles derived from egg shell waste with enhanced UV shielding, nano priming and fingerprint real time object detection through YOLOv8x

A facile approach towards large-scale synthesis of TiO2 nanoparticles derived from egg shell waste with enhanced UV shielding, nano priming and fingerprint real time object detection through YOLOv8x

Application of improved deep learning algorithm in obstacle avoidance of UAV

Abstract To enhance the intelligent obstacle avoidance capabilities of unmanned aerial vehicles (UAVs), we introduce a refined version of the You Only Look Once (YOLO) algorithm, termed Improved YOLO (I-YOLO). This novel algorithm not only maintains the swift detection speed inherent in the original YOLO but also augments its feature enhancement network. Specifically, we propose a three-branch parallel feature pyramid network (TBPFP) to capture richer semantic information pertaining to the context of small obstacles. Furthermore, we incorporate a generative adversarial network (GAN) in a cascaded manner to synthesize more realistic super-resolution images, thereby bolstering detection accuracy. Experimental evaluations demonstrate that, in comparison to the baseline YOLO algorithm, our I-YOLO variant exhibits superior detection precision and feature extraction capabilities in the perception of intricate environmental targets. While there is a modest decrement in detection speed, it remains adequate to fulfill real-time requirements, particularly in the context of video stream processing.

Re-imagen: Generating coherent background activity in synthetic scenario-based forensic datasets using large language models

Due to legal and privacy-related restrictions, the generation of synthetic data is recommended for creating datasets for digital forensic education and training. One challenge when synthesizing scenario-based forensic data is the creation of coherent background activity besides evidential actions. This work leverages the creative writing abilities of large language models (LLMs) to generate personas and actions that describe the background usage of a device consistent with the created persona. These actions are subsequently converted into a machine-readable format and executed on a virtualized device using VM control automation. We introduce Re-imagen, a framework that combines state-of-the-art LLMs and a recent unintrusive GUI automation tool to produce synthetic disk images that contain arguably coherent “wear-and-tear” artifacts that current synthesis platforms lack. While, for now, the focus is on the coherence of the generated background activity, we believe that the proposed approach is a step toward more realistic synthetic disk image generation.

DSViT: An Enhanced Transformer Model for Deepfake Detection

The rapid development of artificial intelligence and deep learning models has enabled the creation of highly realistic fake images and videos, posing significant threats to information security and safety. Accurate detection of these forged contents is crucial to prevent the spread of misinformation and to protect the integrity of digital media. Although several advanced studies in this field, such as Vision Transformer (ViT) and Convolutional Vision Transformer (CViT), have been conducted, there remain limitations that need to be addressed. In this paper, we introduce a novel model, improved from CViT, designed to optimize the process of deepfake detection, named DSViT (Deepfake Detection with SC-based Convolutional Vision Transformer). This model judiciously integrates Convolutions and a SCConvolution block with the ViT architecture. We conducted experiments on the Deepfake Detection Challenge (DFDC) dataset and compared the results with the CViT model to demonstrate the effectiveness of the proposed model

Evolution of realistic neutron star in the framework of [formula omitted] gravity

This work analyses and evaluates a few realistic compact objects in the presence of a gravitational interaction between two particles with a nonmetricity Q. In the f(Q) gravity framework, we have selected the anisotropic equation of motion and have determined f(Q) to be a linear function of nonmetricity Q. To evaluate the field equations in our work, we have opted to employ the Krori-Barua metric. We calculated the anisotropic factor for each of the four compact objects and found that the anisotropic component is positive and increases monotonically and interpreted that the nuclear force can oppose the gravitational attraction. At last, the relationship between mass and radius has been determined and illustrated visually. We have noted that the compactness of the pulsars LMC X-4, SMC X-4, Cen X-3, and Vela X-1 is inside the Buchdahl’s limit for varying values of a. This has led to the interpretation that these pulsars are neutron stars in a modified gravity background of f(Q). In addition, we calculated the model mass and, using thirty distinct choices of a, ran the Chi-Square test to see if there was a noticeable difference between the observed and model-generated masses. We have also looked at how the surface redshift has changed over time and whether the compact objects in our model that were previously described are compact.

Theory of similarity and ultrasonic testing

Application of similarity theory (dimensional analysis) to ultrasonic (UT) testing is discussed using as an illustration the example of concrete blocks. The concept of dimensional analysis makes it possible to represent a physical system via a minimal set of variables, the so-called π groups, which are the dimensionless quantities. These dimensionless parameters, characterizing the respective phenomenon, should have approximately equal values for the real object and for the model. Small concrete samples have been tested in the lab conditions using various UT probes and techniques. Two dimensionless criteria, valid and similar for the real field case and for the laboratory samples, have been introduced: total attenuations of UT waves and ratios of void size to the UT wavelength. To get total attenuations in the field conditions for thick concrete block, similar to the total attenuations measured in lab experiments on small samples, one should apply the low-frequency UT waves 60-100kHz. However, these UT waves will be able to reliably detect only the large inhomogeneities (e.g. voids), whose size is about half of the used UT wave, i.e. inhomogeneities with dimensions larger than 20-40mm.

Model for atomization droplet size and energy distribution ratio at the distal end of an electrostatic nozzle

Electrostatic atomization minimum quantity lubrication (EMQL) employs the synergistic effect of multiple physical fields to atomize minute quantities of lubricant. This innovative methodology is distinguished by its capacity to ameliorate the atomization attributes of the lubricant substantially, which subsequently augments the migratory and infiltration proficiency of the droplets within the complex and demanding milieu of the cutting zone. Compared with the traditional minimum quantity lubrication (MQL), the EMQL process is further complicated by the multiphysical field influences. The presence of multiple physical fields not only increases the complexity of the forces acting on the liquid film but also induces changes in the physical properties of the lubricant itself, thus making the analysis of atomization characteristics and energy distribution particularly challenging. To address this objective reality, the current study has conducted a meticulous measurement of the volume average diameter, size distribution span, and the percentage concentration of inhalable particles of the charged droplets at various intercept positions of the EMQL nozzle. A predictive model for the volume-averaged droplet size at the far end of the EMQL nozzle was established with the observed statistical value F of 825.2125, which indicates a high regression accuracy of the model. Furthermore, based on the changes in the potential energy of surface tension, the loss of kinetic energy of gas, and the electric field work at different nozzle orifice positions in the EMQL system, an energy distribution ratio model for EMQL was developed. The energy distribution ratio coefficients under operating conditions of 0.1 MPa air pressure and 0 to 40 kV voltage on the 20 mm cross-section ranged from 3.094‰ to 3.458‰, while all other operating conditions and cross-sections had energy distribution ratios below 2.06‰. This research is expected to act as a catalyst for the progression of EMQL by stimulating innovation in the sphere of precision manufacturing, providing theoretical foundations, and offering practical guidance for the further development of EMQL technology.

Synthetic Meets Authentic: Leveraging LLM Generated Datasets for YOLO11 and YOLOv10-Based Apple Detection through Machine Vision Sensors

Training machine learning (ML) models for artificial intelligence (AI) and computer vision-based object detection process typically requires large, labeled datasets, a process often burdened by significant human effort and high costs associated with imaging systems and image acquisition. This research aimed to simplify image data collection for object detection in orchards by avoiding traditional fieldwork with different imaging sensors. Utilizing OpenAI's DALLE, a large language model (LLM) for realistic image generation, we generated and annotated a cost-effective dataset. This dataset, exclusively generated by LLM, was then utilized to train two state-of-the-art deep learning models: YOLOV10 and YOLO11. The YOLO11 model for apple detection was trained with its five configurations (YOLO11n, YOLO11s, YOLO11m, YOLO11l and YOLO11x), and YOLOv10 model with its six configurations (YOLOv10n, YOLOv10s, YOLOv10m, YOLOv10b, YOLOv10l and YOLOv10x), which was then tested with real-world (outdoor orchard) images captured by a digital (Nikon D5100) camera and a consumer RGB-D camera (Microsoft Azure Kinect). YOLO11 outperformed YOLOv10 as YOLO11x and YOLO11n exhibited superior precision of 0.917 and 0.916, respectively. Furthermore, YOLO11l demonstrated the highest recall among its counterparts, achieving a recall of 0.889. Likewise, the YOLO11n variant excelled in terms of mean average precision (mAP@50), achieving the highest value of 0.958. Validation tests against actual images collected through a digital camera (Nikon D5100) over Scilate apple variety in a commercial orchard environment showed a highest precision of 0.874 for YOLO11s, recall of 0.877 for YOLO11l and mAP@50 of 0.91 for YOLO11x. Additionally, validation test against actual images collected through a Microsoft Azure camera over the same orchard showed a highest precision, recall and mAP@50 respectively of 0.924, 0.781 and 0.855 with YOLO11x. All variants of YOLO11 surprisingly demonstrated a pre-processing time of just 0.2 milliseconds (ms), which was faster than any variant of YOLOv10. The fastest inference time for the YOLO11n model using the training dataset generated by the language model was 3.2 ms, while YOLOv10n, fastest among YOLOv10 variants, had a longer inference time of 5.5 ms. Likewise, the fastest inference time for the sensor-based images was 7.1 ms (for Nikon D5100 camera images) and 4.7 ms (for Azure images) with YOLO11n. This study presents a pathway for generating large image datasets using LLM in challenging agricultural fields with minimal or no labor-intensive efforts in field data-collection, which could accelerate the development and deployment of computer vision and robotic technologies in orchard environments.

Анализ рынка жилой недвижимости академического района города Екатеринбург

Real estate as an economic category is a tangible asset, the value equivalent of which is determined by how effectively it is used as an economic good, commodity or source of income. The real estate market is of constant relevance, both for investors, builders, lawyers, and for ordinary citizens who need their own housing to live. The analysis of the real estate market helps to understand where the real estate market may move in the future and what awaits it, taking into account the specifics according to which it is formed. This article analyzes the secondary real estate of the Academic district of Yekaterinburg. Information on real estate objects was selected from the sites of Avito and Domclick, services for housing search and real estate sale. The existing infrastructure of the district is analyzed. To analyze the dynamics of the price per square meter of residential real estate, calculations were made for a time period of 4 months in 2023-2024 and data on the cost of a square meter were presented.

РАЗРАБОТКА ПРОГРАММЫ РАСЧЕТА ПАРАМЕТРОВ СХЕМЫ ЗАМЕЩЕНИЯ ТРЕХФАЗНОГО АСИНХРОННОГО ЭЛЕКТРОДВИГАТЕЛЯ

The article is devoted to developing a program for calculating the parameters of a T-shaped equivalent circuit of three-phase asynchronous electric motors. The relevance of this work is due to the need to obtain reliable results of computer simulation modelling of electric drive systems in software packages. The aim of the study is to implement in a convenient software interface an analytical technique for calculating the parameters of an asynchronous motor equivalent circuit with minimal deviations from the catalogue data. For the software calculation of the equivalent circuit parameters the paper considers analytical approximation and iterative counting techniques, since they do not require experiments on industrial electrical equipment. A comparative analysis of such techniques is performed using the example of a three-phase asynchronous motor brand BA280S4 with a power of 110 kW. The results of the compar-ative analysis show that the highest reliability of the mechanical and electromechanical characteristics is achieved in the case of using the iterative counting technique with minimizing the objective function of the sum of the weighted squares of the deviations of the control point values from the catalogue data. The authors implement the program for calculating the equivalent circuit parameters according to the selected calculation method in the Embarcadero Delphi environment. The reliable values of the electric motor parameters calculated by the program will allow for computer to model electric drive systems that best matches real objects.