Specific Objectives Research Articles

Attention to object categories: Selection history determines the breadth of attentional tuning during real-world object search.

People excel at learning the statistics of their environments. For instance, people rapidly learn to pay attention to locations that frequently contain visual search targets. Here, we investigated how frequently finding specific objects as search targets influences attentional selection during real-world object search. We investigated how learning that a specific object (e.g., a coat) is task-relevant affects searching for that object and whether a previously frequent target would influence search more broadly for all items of that target's category (e.g., all coats). Across five experiments, one or more objects from a single category were likely targets during a training phase, after which objects from many categories became equally likely to be targets in a neutral testing phase. Participants learned to find a single frequent target object faster than other objects (Experiment 1, N = 44). This learning was specific to that object, with no advantage in finding a novel category-matched object (Experiment 2, N = 32). In contrast, learning to prioritize multiple exemplars from one category spread to untrained objects from the same category, and this spread was comparable whether participants learned to find two, four, or six exemplars (Experiment 3, N = 72). These differences in the breadth of attention were due to variability in the learning environment and not differences in task (Experiment 4, N = 24). Finally, a set-size manipulation showed that learning affects attentional guidance itself, not only postselective processing (Experiment 5, N = 96). These experiments demonstrate that the breadth of attentional tuning is flexibly adjusted based on recent experience to effectively address task demands. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The problems of the theory of the reference of signs in modern linguistics

The subject of the study is the history of the development and current state of the theory of reference, which reflects a variety of approaches and concepts. Some researchers believe that reference is a purely semantic phenomenon, while others emphasize the importance of context and pragmatics for understanding referentiality. The issue of the influence of cultural and social factors on reference in various languages is also being actively investigated. The history of the development of the theory of reference began with the works of Gottlob Frege and Bertrand Russell at the beginning of the 20th century. Frege introduced the concept of semantic reference, arguing that every word has its own referent in the world. Russell developed this idea by proposing a theory of descriptions, according to which words indicate objects by describing their characteristics. The works of Ludwig Wittgenstein and Donald Davidson played an important role in the further development of the theory of reference. Wittgenstein proposed the concept of language games, arguing that reference depends on the context and use of a word in a certain situation. Davidson developed the theory of truth, according to which reference is determined through the connection between language and the world. The pragmatic and cognitive aspects of research methodology are important for the successful conduct of scientific research. The referential nature of signs plays a key role in this process, ensuring effective presentation and communication of research results. The scientific novelty of the research lies in a comprehensive examination of the classical works of philosophers, semiotics and linguists, including Plato, Aristotle, Charles Pierce, Ferdinand de Saussure and Umberto Eco, on the theory of reference. One of the key concepts in the study of sign systems is referential. Referentiality allows to associate words with specific objects and understand their meaning. One of the most relevant areas of research in the field of reference is the study of ways in which linguistic units interact with the context and situation of communication. This allows for a deeper understanding of the processes of notation and interpretation in language, as well as the development of new theoretical models explaining these processes. Thus, scientific novelty in the study of the referentiality of signs continues to be relevant and important for the development of linguistics and related disciplines. The author's contribution in this area may lead to new discoveries and understanding of the mechanisms of linguistic communication.

The originality of Chinoiserie-style garden architecture in England in the XVIII century

The article "The originality of Chinoiserie-style garden architecture in England in the XVIII century" explores the development and specifics of Chinoiserie style in the landscape architecture of England. Chinoiserie style refers to the use of Chinese motifs and stylistic techniques in European architecture. The aim of the work is to identify the specifics of the arrangement of English gardens and parks in the Chinoiserie style. The subject of the study is the stylistic features of the embodiment of Chinoiserie gardens and parks. The object of the study is parks and garden architecture in the Chinoiserie style in England. The study covers the 18th century, revealing the contribution of individual architects and the influence of Chinese art on the development of garden architecture. The presented analysis demonstrates how architectural styles of Western and Eastern culture can mix. English gardens and parks are evidence of the influence of Chinese aesthetics on landscape gardening, which later spread to continental Europe. In the course of the research, the following methods were mainly used: historical, historical-genetic, historical-comparative. In the study of specific objects of landscape architecture, methods of art criticism analysis were applied — formal, stylistic and comparative methods. The article examines specific English monuments of landscape architecture of the XVIII century taken as examples the Chinoiserie style. The novelty of the study lies in the fact that the features of the Chinoiserie style in the landscape architecture of England were highlighted and a comparison of English architectural objects in the Chinoiserie style with Chinese analogues was carried out. The conducted research revealed the English specifics in the approach to interpreting Chinese designs of gardens and parks and architectural projects in the XVIII century. English gardeners and architects were inspired by Chinese aesthetics and used oriental elements for decoration, but did not follow all architectural canons and traditions. This gave rise to a new Chinoiserie style, a combination of Chinese and European landscape art that spread throughout Europe.

Disease Prediction Using CNN Algorithm

In this project, I have endeavored to revolutionize disease detection by harnessing the power of artificial intelligence, allowing users to effortlessly and accurately identify three specific diseases from the comfort of their own homes with just a few clicks. This innovative approach eliminates the need to endure days of anticipation for traditional diagnostic reports, ensuring that treatment can commence promptly. The cornerstone of this project lies in the application of Convolutional Neural Networks (CNNs), a cutting-edge deep learning technology. These networks are designed to take input images, meticulously analyze them, and allocate importance to various elements within the images, such as specific features or objects. By learning from vast datasets, CNNs become proficient in distinguishing between different elements, which, in our case, are essential for the accurate identification of diseases. In conclusion, this project represents a significant step forward in democratizing healthcare and disease detection. By leveraging AI and CNNs, it empowers individuals to proactively manage their health, obtain rapid diagnoses, and access prompt treatment. As the project continues to evolve and adapt, its potential to revolutionize disease detection becomes increasingly evident, promising a future where technology and healthcare work hand in hand to improve patient outcomes and overall well-being. Key Words: Disease prediction, CNN.

THE METHODS OF THE SORBED QUANTITY OF THE DIFFUSION COEFFICIENTS AND POLYMERS LIQUID AREAS

The article presents the application of a method for assessing the qualitative state of bridges based on the use of qualimetry methods. The proposed approach considers a wide range of parameters that affect the functioning and reliability of road bridges. The method involves the use of a qualimetric model that allows systematizing and structuring of information about the physical condition of individual bridge elements and functional indicators. The weighting of the parameters was determined using expert and estimated methods, which increased the accuracy and objectivity of the assessment. The development of a qualitative condition model includes the following stages: identification of bridge elements, determination of assessment parameters, development of a qualimetric model, determination of weighting coefficients for each criterion, analysis of the results of a specific object inspection, and determination of the overall operational condition of the bridge. The results of the study show that the proposed methodology can be effectively used to assess the condition of bridges, providing a sufficiently high accuracy and completeness of the assessment. This contributes to improving the safety and reliability of bridge structures, reducing accidents and optimizing their maintenance costs. The proposed assessment method has prospects for implementation in the practice of bridge condition management and can be further applied in the system of monitoring the condition of transport facilities.

Conservation as a Tool for Cultural Rights in the Democratic Republic of the Congo: The We have gaps Project

ABSTRACT This paper outlines the aim, methodology, and initial results of a joint initiative between two universities (Université de Kinshasa in Kinshasa, Democratic Republic of the Congo and Université de Liège in Liège, Belgium), two conservation training courses (Academy of Fine Arts of Kinshasa and École Supérieure des Arts Saint-Luc in Liège), and the Institute of National Museums of Congo. The Nous avons des lacunes (We have gaps) project plays on the notion of loss – both material and of knowledge – to involve communities in the construction of endogenous knowledge on the conservation of objects: a task to be carried out by the Congolese, for the Congolese. For many Congolese, concepts such as ‘museum’ and ‘conservation’ sound foreign, and the idea of cultural rights remains highly abstract and theoretical. The challenge is to transform the manifestation of these rights through conservation into a real tool for empowerment. The exhibition concept is two-fold: conservators explain what loss or material alteration has occurred in a specific object, and how they remedy it, thus arousing interest in a little-known profession. At the same time, they show and explain their own gaps in knowledge, and ask the public for information about the technique, function, use, naming, or names of the mystery object in the four national languages and numerous Congolese idioms. The Nous avons des lacunes project aims to highlight the role and involvement of communities and different social entities as active guardians of their heritage, by humbly admitting the lack of knowledge among professionals.

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN.

Diabetic retinopathy (DR) arises from blood vessel damage and is a leading cause of blindness on a global scale. Clinical professionals rely on examining fundus images to diagnose the disease, but this process is frequently prone to errors and is tedious. The usage of computer-assisted techniques offers assistance to clinicians in detecting the severity levels of the disease. Experiments involving automated diagnosis employing convolutional neural networks (CNNs) have produced impressive outcomes in medical imaging. At the same time, retinal image grading for detecting DR severity levels has predominantly focused on spatial features. More spectral features must be explored for a more efficient performance of this task. Analysing spectral features plays a vital role in various tasks, including identifying specific objects or materials, anomaly detection, and differentiation between different classes or categories within an image. In this context, a model incorporating Wavelet CNN and Support Vector Machine has been introduced and assessed to classify clinically significant grades of DR from retinal fundus images. The experiments were conducted on the EyePACS dataset and the performance of the proposed model was evaluated on the following metrics: precision, recall, F1-score, accuracy, and AUC score. The results obtained demonstrate better performance compared to other state-of-the-art techniques.

Implementation of Text to Image using Diffusion Model

Text-to-image generation is a transformative field in artificial intelligence, aiming to bridge the semantic gap between textual descriptions and visual representations. This presents a comprehensive approach to tackle this challenging task. Leveraging the advancements in deep learning, natural language processing (NLP), and computer vision, this proposes a cutting-edge model for generating high-fidelity images from textual prompts. Trained on a vast and varied dataset of written descriptions and related images, this model combines an image decoder and a text encoder within a hierarchical framework. To enhance realism, this incorporates attention mechanisms and fine-grained semantic parsing. The model's performance is rigorously evaluated through both quantitative metrics and qualitative human assessments. Results demonstrate its ability to produce visually compelling and contextually accurate images across various domains, from natural scenes to specific object synthesis. This further explores applications in creative content generation, design automation, and virtual environments, showcasing the potential impact of our approach. Additionally, this releases a user-friendly API, empowering developers and designers to integrate our model into their projects, and fostering innovation and creativity. Key Words: image generation model, Deep learning, Natural language processing.

Equivalent Thermal Conductivity of Topology-Optimized Composite Structure for Three Typical Conductive Heat Transfer Models

Composite materials and structural optimization are important research topics in heat transfer enhancement. The current evaluation parameter for the conductive heat transfer capability of composites is effective thermal conductivity (ETC); however, this parameter has not been studied or analyzed for its applicability to different heat transfer models and composite structures. In addition, the optimized composite structures of a specific object will vary when different optimization methods and criteria are employed. Therefore, it is necessary to investigate a suitable method and parameter for evaluating the heat transfer capability of optimized composites under different heat transfer models. Therefore, this study analyzes and summarizes three typical conductive heat transfer models: surface-to-surface (S-to-S), volume-to-surface (V-to-S), and volume-to-volume (V-to-V) models. The equivalent thermal conductivity (keq) is proposed to evaluate the conductive heat transfer capability of topology-optimized composite structures under the three models. A validated simulation method is used to obtain the key parameters for calculating keq. The influences of the interfacial thermal resistance and size effect on keq are considered. The results show that the composite structure optimized for the V-to-S and V-to-V models has a keq value of only 79.4 W m−1 K−1 under the S-to-S model. However, the keq values are 233.4 W m−1 K−1 and 240.3 W m−1 K−1 under the V-to-S and V-to-V models, respectively, which are approximately 41% greater than those of the in-parallel structure. It can be demonstrated that keq is more suitable than the ETC for evaluating the V-to-S and V-to-V heat transfer capabilities of composite structures. The proposed keq can serve as a characteristic parameter that is beneficial for heat transfer analysis and composite structural optimization.

Уголовно-правовое значение экономической деятельности при теоретическом моделировании уголовного закона

The article conducts a comprehensive theoretical and applied study of the universal criminal law category “object of criminal law protection” as a basic concept for the formation of a theoretical model of criminal law protection. The author determines what should be understood by the object of criminal legal protection in the process of theoretical modeling of the criminal law, distinguishes the definition in question from related concepts, and identifies two functional meanings of the object of criminal legal protection - actual and normative. The identification of a general category - an object of criminal legal protection allows us to formulate proposals on the concept and classification of the specific object of crimes in the field of economic activity, which can be used as the basis for the structure and content of the criminal law on liability for economic crimes.

Theoretical Aspects of Determining the Place of Energy (Energy Resources) in the System of Civil Rights Objects

The paper is devoted to determining the role of energy in the system of civil rights objects. The author highlights two main theories. Representatives of the first theory recognize energy as a thing and, accordingly consider it possible to apply the property law regime in relation to it. Currently, the number of supporters of this theory has increased due to the fact that the legislator consolidated regulatory norms regarding the energy supply contract in the chapter of the Civil Code of the Russian Federation devoted to purchase-and-sale transactions. The doctrine also attempts to justify the extension of the property right to energy through the category of «disembodied things». The second theory denies the attribution of energy to things. Representatives of this theory try to substantiate the legal nature of energy based on the existing systematization of objects of civil rights. According to the author, energy is an independent object of civil rights, a special object of a physical nature, having no sign of materiality, capable of generation and, under certain conditions, transfer in economic relations of civil turnover participants, and considered as a commodity with property value and high socioeconomic importance. Thus, energy perfectly fits into the category reserved by the law-maker for similar new objects and classified as «other property». Denying the substantive qualification of energy, the author supports the position of researchers who consider energy exclusively as an object of binding rights, since, given its physical nature and the possibility of generation and transmission exclusively through technical infrastructure, energy as an independent object of civil rights can act exclusively as a subject of binding legal relations

Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation

Given an object of interest, visual navigation aims to reach the object’s location based on a sequence of partial observations. To this end, an agent needs to (1) acquire specific knowledge about the relations of object categories in the world during training and (2) locate the target object based on the pre-learned object category relations and its trajectory in the current unseen environment. In this article, we propose a Category Relation Graph (CRG) to learn the knowledge of object category layout relations and a Temporal-Spatial-Region attention (TSR) architecture to perceive the long-term spatial-temporal dependencies of objects, aiding navigation. We establish CRG to learn prior knowledge of object layout and deduce the positions of specific objects. Subsequently, we propose the TSR architecture to capture relationships among objects in temporal, spatial, and regions within observation trajectories. Specifically, we implement a Temporal attention module (T) to model the temporal structure of the observation sequence, implicitly encoding historical moving or trajectory information. Then, a Spatial attention module (S) uncovers the spatial context of the current observation objects based on CRG and past observations. Last, a Region attention module (R) shifts the attention to the target-relevant region. Leveraging the visual representation extracted by our method, the agent accurately perceives the environment and easily learns a superior navigation policy. Experiments on AI2-THOR demonstrate that our CRG-TSR method significantly outperforms existing methods in both effectiveness and efficiency. The supplementary material includes the code and will be publicly available.

Partial Endorsement in Commercial Paper: Reasons for Rejection and Justifications for Acceptance (Critical Study)

This research paper strives to demonstrate how commercial paper trades through endorsement and for this trading to be correct and effective for its impact, the endorsement must meet the necessary objectives and formal conditions for that. One of the special objective conditions for endorsement is that it should be on the full immutable right in the commercial paper, but opinions have emerged from jurisprudence and the judiciary considered that partial endorsement is valid and effective to raise, so this study is done to elaborate the reasons for the endorsement invalidity in general and to study partial endorsement in specific, to find out the motives adhered to by jurisprudence to consider partial endorsement invalid, as well as provided a response by balancing between acceptance of endorsement and the reasons for rejection. In conclusion, the introduction of a new exception to the rules of endorsement will not be unusual.

From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation

Persistent issues of schedule deviations and cost overruns within large construction projects aggravate the construction industry’s global productivity concerns. However, how holistic, data-oriented methods can effectively be leveraged for investigating project performance and identifying potential bottlenecks during the construction phase remains unanswered. Our research addresses this issue with a novel approach encompassing data acquisition, object detection, geometric projection, and graph-based linking. Image data, continuously captured by crane-camera systems, gets transformed into higher-level information using an end-to-end deep learning-based pipeline that covers the detection of specific on-site objects and integrates it in a knowledge graph. The knowledge graph facilitates extracting precise construction metrics, identifying spatiotemporal irregularities, like work hotspots characterized by high activity and intensive work concentrations, but also phases with low activity. The proposed method improves learning from past construction data, aiding stakeholders and inspiring further research into real-time monitoring, predictive analytics, and data-integrated decision-making systems to reshape construction practices.

Word Formation Processes of COVID-19 Related Terms in Jakarta Post, BBC, and CNN Portals

The global pandemic known as Coronavirus Disease 2019 (COVID-19) has an impact on the use of language. The language used in COVID-19 news is frequently unfamiliar to the general public or even made up just for the pandemic. The goal of this study is to examine the word formation of COVID-19-related terms used in The Jakarta Post, BBC, and CNN portals. There are two research questions in this study, they are: (1) What are the types of word formation processes found in terms related to COVID-19? (2) How are the words related to COVID-19 formed? Moreover, this research uses Lieber’s theory (2016). This study uses a qualitative methodology. The findings of this study show that there are seven types of word formation: affixation, compounding, coinage, backformation, blending, clipping, and acronyms and initialisms. The conversion type is the process of word formation that has not been discovered. Words related to COVID-19 are formed by adding or removing an affix, combining or blending two words, expressing a specific object, and abbreviating a word. It can be concluded that many terms related to COVID-19 are the result of a word formation process that aims to form new words that will certainly give different meanings and different word classes from the original word. In the future, this research can be useful for readers who are looking for definitions of terms related to COVID-19, as well as for those who are interested in morphology or the field of word formation processes.

Other tokens matter: Exploring global and local features of Vision Transformers for Object Re-Identification

Object Re-Identification (Re-ID) aims to identify and retrieve specific objects from images captured at different places and times. Recently, object Re-ID has achieved great success with the advances of Vision Transformers (ViT). However, the effects of the global–local relation have not been fully explored in Transformers for object Re-ID. In this work, we first explore the influence of global and local features of ViT and then further propose a novel Global–Local Transformer (GLTrans) for high-performance object Re-ID. We find that the features from last few layers of ViT already have a strong representational ability, and the global and local information can mutually enhance each other. Based on this fact, we propose a Global Aggregation Encoder (GAE) to utilize the class tokens of the last few Transformer layers and learn comprehensive global features effectively. Meanwhile, we propose the Local Multi-layer Fusion (LMF) which leverages both the global cues from GAE and multi-layer patch tokens to explore the discriminative local representations. Extensive experiments demonstrate that our proposed method achieves superior performance on four object Re-ID benchmarks. The code is available at https://github.com/AWangYQ/GLTrans.

DEVELOPMENT OF CLASSIFIERS OF DEVIATIONS FROM THE REQUIREMENTS OF REGULATORY DOCUMENTS OF THE OIL AND GAS INDUSTRY AND COMPENSATORY MEASURES

The paper analyzes the problem of ensuring safety at various stages of the life cycle of main pipelines and steel vertical tanks in cases of deviations from the requirements of current design standards. To effectively solve this problem, independent and easily modifiable classifiers of deviations from the requirements of regulatory documents, as well as measures aimed at compensating for these deviations, have been developed. The faceted method was chosen as the classification method, which allows us to divide a large object of study into smaller characteristics independent of each other, which facilitates the decision-making process and allows us to take into account many aspects.The resulting classifiers are a tool for systematization of information obtained during the analysis and development of special technical conditions necessary to ensure the safety of main pipelines and steel vertical tanks. Due to their ability to be easily modified, they can be adapted to specific objects and conditions, which makes them a versatile and flexible tool for developing and making engineering decisions.An important aspect of the work is the practical testing of the developed classifiers by applying them to a real object of the oil and gas industry. This stage of testing demonstrated the high efficiency and practical applicability of the proposed classifiers, which indicates their potential aimed at improving the safety of oil and gas transportation and storage facilities, as well as products of their processing.In addition, the systematization of information on deviations from the requirements of regulatory documents and ways to compensate for them allows not only to increase the level of safety during the construction, reconstruction and operation of pipelines and reservoirs, but also to reduce the consequences of possible emergencies.The results obtained represent an important step in the development of an approach to ensuring the safety of main pipelines and steel vertical tanks, and also have both practical and scientific value, and can be recommended for use by specialists in the field of design, technosphere safety and the development of special technical conditions.

Ghost polarimetry with a spatial light modulator for creation of structured illumination patterns

Subject of study. The spatial distribution of polarization properties for objects with linear dichroism is studied. Aim of study. A ghost polarimetry system using a spatial light modulator to measure the optical fields illuminating an object with linear dichroism is developed. Method. Ghost polarimetry is used to measure the integral intensity of radiation passing through an object. The intensity correlation functions are numerically calculated for two orthogonally polarized sets of optical fields for subsequent calculation of the modulus of the anisotropy azimuth for an object with linear dichroism. Main results. A ghost polarimetry device using a spatial light modulator is developed to measure the characteristics of structured optical fields. Images showing the polarization properties of three objects with linear dichroism are obtained. Practical significance. Ghost polarimetry has several advantages over traditional methods of obtaining polarization images when imaging objects with low radiation intensity, when imaging in high-turbulence environments, or when imaging in spectral ranges for which traditional measuring instruments are expensive or unavailable. The inclusion of a spatial light modulator in the ghost polarimetry device means it is no longer necessary to record the fields generated. This modification will enable the characteristics of optical fields for specific objects to be varied for microbiological and medical research.

Real Time Object Detection System Using Yolov

Improving object detection models in computer vision is a crucial area of focus. Object detection represents a more sophisticated approach to image classification, where a neural network identifies objects within an image and delineates them using bounding boxes. YOLO (You Only Look Once) revolutionized object detection by introducing an end-to-end neural network architecture that simultaneously predicts bounding boxes and class probabilities. Unlike earlier methods that adapted classifiers for detection, YOLO's approach streamlines the process by integrating classification and localization tasks, accurately delineating objects with bounding boxes. The project mainly focuses on augmenting YOLOv4, a state-of-the-art object detection architecture, to address the limitations like small object detection, adaptive lighting handling, occlusion challenges, detecting objects at different angles, and specialized object features. Additionally, we aim to improve the model's ability to recognize specialized object features for superior classification. Furthermore, by incorporating functionalities like license plate recognition using Tesseract OCR, object counting (total and class-wise), and detection cropping, this project extends the model's applicability to real-world scenarios. These functionalities make the model valuable in areas like autonomous vehicles, traffic monitoring, and industrial automation. The augmented model's improvements aim to overcome drawbacks identified in existing research, enhancing its efficacy in various scenarios.

Castle and the Seasons

This article develops the concept of the “geometaphor” by considering the life and work of Loris Jacopo Bononi, poet-doctor-collector-inventor, through two elements of his landscapes: the castle of Castiglione del Terziere with its contents (a desire of eternity) and the fleeting seasons in the surrounding woods (the astonishment of the ephemeral). Two are the main lenses: Bononi’s relationship with his castle in Lunigiana and with specific objects preserved there (the contents of his bedroom and library, especially the incunabula collection); and his poetic production spanning the last years of his life, in the first decade of the XXI century. This is the first article that deals with Bononi’s poetic production in English.