Similar Objects Research Articles

Digital empathy in behaviour change interventions: A survey study on health coach responses to patient cues.

Digital health coaching interventions for behaviour change (BC) are effective in addressing various health conditions. Implementing these requires accurate descriptions of components and health coaches (HC) delivery methods, alongside understanding patients' perceptions of these interactions. The HC-patient relationship significantly influences BC outcomes. Here, empathy is an important driver that enables HCs to offer tailored advice that resonates with patients' needs, fostering motivation. Yet, defining and measuring empathy remains a challenge. In this study, we draw on various BC frameworks and Pounds' empathy appraisal approach to categorise HCs responses to patient cues and explore the interplay between empathy and BC. Using a two-round survey, we collected responses from 11 HCs to 10 patient messages from the Bump2Baby and Me trial in a simulated interaction. We analysed 88 messages to identify empathic responses and behaviour change techniques. Patients' implicit empathy opportunities showed higher response rates than explicit ones. HCs prioritised positive reinforcement and employed various strategies to achieve similar objectives. The most common empathic response was 'Acceptance' for patients' implicit positive expressions of self-judgement. HCs emphasised relatedness-support and competence-promoting techniques for implicit negative feelings and judgements, such as 'Show unconditional regard' and 'Review behaviour goals', and 'Action planning and Problem-solving' techniques to address explicit negative appreciations and feelings. The use of different techniques with the same objective highlights the complexity of BC interactions. Further research is needed to explore the impact of this variability on patient outcomes and programme fidelity.

CMPF-UNet: a ConvNeXt multi-scale pyramid fusion U-shaped network for multi-category segmentation of remote sensing images

Most U-shaped convolutional neural network (CNN) methods have the problems of insufficient feature extraction and fail to fully utilize global/multi-scale context information, which makes it difficult to distinguish similar objects and shadow occluded objects in remote sensing images. This article proposes a ConvNeXt multi-scale pyramid fusion U-shaped network (CMPF-UNet). In this work, we first propose a novel backbone network based on ConvNeXt to enhance image feature extraction, and use ConvNeXt bottleneck blocks to reconstruct the decoder. Furthermore, a scale aware pyramid fusion (SAPF) module and Residual Atrous Spatial Pyramid Pooling (RASPP) module are proposed to dynamically fuse the rich multi-scale context information in advanced features. Finally, multiple Global Pyramid Guidance (GPG) modules are embedded in the network, aiming to provide different levels of global context information for the decoder by reconstructing skip-connections. Experiments on the Vaihingen and Potsdam datasets indicate that the proposed CMPF-UNet segmentation achieves more accurate results.

Художественный мир философской сказки: реалии в творчестве Г. М. Цыферова и С. Г. Козлова

The article is devoted to the study of the types and functions of realia presented in the philosophical fairy tales written by G. M. Tsyferov and S. G. Kozlov. Their works, focusing on moral issues, depict the world through contrasting the existential and the everyday, which necessitates the study of the authors’ ways of representing the signs of their epoch. A motive analysis carried out in the paper takes into account the cultural and historical situation and shows that S. Kozlov’s cycle of fairy tales represents the home space and the corresponding household realia, gaining a cosmic scale and thereby affirming the idea of the universe as a common home, while G. Tsyferov’s works depict similar objects in connection with the problem of growing up and becoming a socially active person, prioritizing the study of modern city realia which convey the idea of striving for goodness and beauty as the basis of historical transformations. Both writers turn to the sphere of culture, but there is a distinction: G. Tsyferov appeals to the life experience of the addressee, encourages them to rethink familiar objects, building associative chains and actualizing the didactic function of humor, while S. Kozlov attracts the reader’s attention to absolutely new information, noting the specifics of the realia of different cultures, thereby stimulating interest in a different type of worldview and explaining the possibility of contacting this worldview on the basis of common values. The paper concludes that the characteristics of realia introduced into the artistic world of a philosophical fairy tale change depending on the psychological and age characteristics of the addressee and the ideological and aesthetic views of the author, but in any case, the establishment of such a connection with reality makes it possible to reveal the ideological component of works devoted to existential problems.

АНАЛИЗ ПАРАМЕТРОВ СИСТЕМ ПОДЗЕМНОЙ ОТРАБОТКИ МЕСТОРОЖДЕНИЯ "КУПОЛ" И ПОРЯДКА ОТРАБОТКИ БЛОКА NE 3 35-3

The article presents the results of studies of mining-geological and mining-geometric parameters of the underground mining of the indigenous gold-silver deposit «Dome», located on the border of the Anadyr and Bilibinsky districts of the Chukotka Autonomous District, It is established that the preparation of the block for cleaning works is carried out by mining-preparatory and rifled mining workings, passed at the horizons of 35 m and 50 m, with the accepted cross-section rectangular-vaulted shape. It is determined that an objective assessment of the mining-geological and mining-geometric parameters of the object makes it possible to determine effective mining systems. The research results can be useful in analyzing the validity of decisions made on ore mining technology with the approximation of experience to similar objects.

A hybrid spectral-spatial fusion technique for hyperspectral object classification

<span>In the field of object classification, hyperspectral imaging (HSI) has been widely used, due to its spectral-spatial, and temporal resolution of larger areas. The HSI is generally used to identify the objects physical properties in accurate manner and as well as to identify similar object with acceptable spectral signatures. Thus, the HSI has been widely used for object identification applications in different fields such as precision agriculture, environmental study, crop monitoring, and surveillance. However, the object classification is time consuming due to extremely large size; thus, the feature fusion of both spectral and spatial have been done. The current feature fusion method fails to retain semantic object intrinsic feature; further, current classification technique induces higher misclassification. In addressing the research issues this paper introduces a hybrid spectral-spatial fusion (HSSF) technique to reduce feature size and retains object intrinsic properties. Finally, in reducing misclassification a soft-margins kernel is introduced in support vector machine (SVM). Experiment is conducted on standard Indian Pines dataset; the result shows the HSSF-SVM model attain much higher accuracy and Kappa coefficient performance.</span>

Selection of Similar Objects While Valuation and Forensic Structural Engineering to Determine Market Value of Civil Buildings

The article is devoted to problematic issues faced by forensic experts during evaluation of civil buildings with premises of various functional purposes. This article purpose is to determine essence, content, general methodological principles and specifics of a unified approach while determining market value of civil objects, including premises of various functional purposes. Attention is focused on the fact that usually forensic experts can use market and non-market types of values. Characteristics of consumer and exchange forms of manifestation of real estate value are given. Current National Property Valuation Standards are listed that are mandatory for appraisers to perform while appraising real estate of all forms of ownership and in all cases. A list of the main types of values and methodical approaches to evaluation of civil buildings with premises of various functional purposes is provided. In addition, the main types of free-standing buildings and the peculiarities of selecting analogues of buildings for sale including premises of various functional purposes, while determining their market value are outlined. Peculiarities of the application of the method of building identification according to the state classifier of buildings and structures: ДК 018-2000 and the algorithm of actions of forensic experts for selecting similar objects are highlighted with an illustrative example of its application.

Regulation of Merger of Associations in Nigeria

A notable provision in the Companies and Allied Matters Act (CAMA) 2020 is the merger of associations. Section 849 provides that 'Two or more associations with similar aims and objects may merge under terms and conditions as the Commission may prescribe by regulation.' This novel provision sets the wheels in motion for the merger of associations under Nigerian law. Curiously, the procedure for merger of associations pursuant to section 849 appears not to cut the mustard, as it is particularly silent on certain key issues such as the content of the scheme of merger, treatment of liabilities, and rights of dissenting members; these issues being features of mergers of companies. The paper examines the procedure for merger of associations under the CAMA 2020, comparing same with the procedures for similar mergers in the UK and South Africa respectively. The paper, among other things, recommends that the procedure for merger of associations in Nigeria be modified to address the essential elements which it omitted so as to promote due diligence and best practices, ensure fairness of the scheme of merger, and hold trustees liable for their actions.

Aksi Kewargaan Membela Hak Ekologi Warga Trenggalek: Analisis Ideologis, Lingkungan, dan Teologis

The background of this research aims to publish two reasons. First, to show a new perspective of common opinion about the social movement that is based on the class struggle. In this research, the environmental movement is run by the ecological ideology, spirituality, and theology motives. Second, the protests to reject the mining exploitation agenda are not to fight the state nor hinder the development agenda. However, these are the acts of citizenship as a part of participation in a democratic country. Based on the two points above, the research adapts two concepts those are the acts of citizenship from Engin Isin and humanistic Islam from Anna Gade. The data mining process uses ethnographic or observational participatory method that has done in the field of movement as become part of them. The discussion divides the actors of movement into three categories, two of them are Islamic-based organization and the left one is Javanese spiritual group. Each of them works on their own way to defend the ecological rights, but they all have the similar objectives of movement that is ecological sustainability. To develop the study of human rights movement in Indonesia, the approach of ethnography study needs to be deepened and doubled so that the local perspective about their own construction about rights could be mapped.

Hybrid AI for panoptic segmentation: An informed deep learning approach with integration of prior spatial relationships knowledge

Panoptic segmentation is a computer vision task that aims to identify and analyze all objects present in an image. While semantic segmentation focuses on labeling each pixel in an image with a category label, panoptic segmentation goes further by not only assigning semantic labels but also identifying and distinguishing individual instance of objects. This task is valuable for various applications, such as robotics, surveillance systems or autonomous vehicle navigation. In this work, we propose a new informed deep learning approach that combines the strengths of deep neural networks for panoptic segmentation with additional knowledge about spatial relationships between objects. This is particularly important as spatial relationships can provide useful cues for resolving ambiguities, distinguishing between overlapping or similar object instances, and capturing the holistic structure of the scene. We propose a novel training methodology that integrates knowledge directly into the deep neural network optimization process. Our approach includes a process for extracting and representing spatial relationships knowledge, which is incorporated into the training using a specially designed loss function. The effectiveness of the proposed method is evaluated and validated on various challenging datasets, namely CityScapes, KITTI, IDD and COCO datasets.

Multitechnique study of the B[e] supergiant RMC 82

ABSTRACT B[e] supergiants (sgB[e]) are rare objects whose evolutionary stage remains uncertain. Observationally, they display strong Balmer emission lines, infrared excess, and intrinsic polarization, indicating a non-spherical circumstellar envelope. We present a study of the sgB[e] RMC 82, using new spectropolarimetric data complemented by photometry from the ultraviolet (UV) to the mid-infrared. Our two-component model comprises a slow, dense equatorial wind wherein dust grains form and a fast polar wind. We applied the hdust radiative transfer code and Bayesian statistics to infer the parameters from a grid of 3240 pre-computed models. The model accurately reproduces the spectral energy distribution and polarized spectrum, but struggles to match the H α emission. Our results suggest a large mass-loss rate of $6.6 \times 10^{-6}\, \mathrm{{\rm M}_{\odot }\, yr^{-1}\, sr^{-1}}$. The dense wind is confined within an opening angle of 11°. The hottest dust grains are located at 277 R* with a temperature of 870 K. The dust grains are porous, with a density of 0.051 $\rm {g\, cm^{-3}}$. The central star was found to be significantly hotter than previous estimates (Teff = $27\, 000$ K). By comparing models with different components, we find that gas reprocesses a significant amount of UV radiation, shielding the dust. However, the dust also scatters UV photons back to the inner disc, increasing its temperature and H α emission. We conclude that self-consistent models, that account for the gas–dust interplay in the envelope, are essential for studying sgB[e] and similar objects.

Spatial feature embedding for robust visual object tracking

AbstractRecently, the offline‐trained Siamese pipeline has drawn wide attention due to its outstanding tracking performance. However, the existing Siamese trackers utilise offline training to extract ‘universal’ features, which is insufficient to effectively distinguish between the target and fluctuating interference in embedding the information of the two branches, leading to inaccurate classification and localisation. In addition, the Siamese trackers employ a pre‐defined scale for cropping the search candidate region based on the previous frame's result, which might easily introduce redundant background noise (clutter, similar objects etc.), affecting the tracker's robustness. To solve these problems, the authors propose two novel sub‐network spatial employed to spatial feature embedding for robust object tracking. Specifically, the proposed spatial remapping (SRM) network enhances the feature discrepancy between target and distractor categories by online remapping, and improves the discriminant ability of the tracker on the embedding space. The MAML is used to optimise the SRM network to ensure its adaptability to complex tracking scenarios. Moreover, a temporal information proposal‐guided (TPG) network that utilises a GRU model to dynamically predict the search scale based on temporal motion states to reduce potential background interference is introduced. The proposed two network is integrated into two popular trackers, namely SiamFC++ and TransT, which achieve superior performance on six challenging benchmarks, including OTB100, VOT2019, UAV123, GOT10K, TrackingNet and LaSOT, TrackingNet and LaSOT denoting them as SiamSRMC and SiamSRMT, respectively. Moreover, the proposed trackers obtain competitive tracking performance compared with the state‐of‐the‐art trackers in the attribute of background clutter and similar object, validating the effectiveness of our method.

Sulphur ion implantation into O2, CO, and CO2 ices: Implications for the formation of sulphur-bearing molecules in the Kuiper Belt

Previous experimental work has systematically investigated the radiolytic sulphur chemistry arising as a result of the implantation of reactive sulphur ions into various oxygen-bearing molecular ices (e.g., H2O, CO2) so as to better understand the surface chemistry of the Galilean moons of Jupiter, where sulphur ions are sourced from the giant Jovian magnetosphere. However, significantly less attention has been paid to analogous sulphur chemistry occurring under conditions relevant to the Kuiper Belt, where sulphur ions supplied by the solar wind may implant into the surfaces of icy bodies that are rich in volatile oxygen-bearing molecular ices such as O2, CO, or CO2. This paper presents the results of a study on the implantation of 290–400 keV S+ ions into pure O2, CO, and CO2 ices under temperature, pressure, radiation dose, and ice deposition conditions relevant to the Kuiper Belt, with a particular focus on the potential synthesis of simple inorganic sulphur-bearing molecules (e.g., SO2, OCS, CS2). Experiments involving CO2 ices were also performed at higher temperatures more typically associated with the Galilean moon system so as to determine whether there exist any differences in the chemistry resulting from the implantation of reactive sulphur ions in these two regions of the Solar System. Our results constitute the first systematic investigation of solid-phase sulphur chemistry in the Kuiper Belt mediated by the sulphur ion component of the solar wind, and thus represent an important step forward in our understanding of the astrochemical processes occurring in the outermost depths of the Solar System.

End-to-end adaptive object detection with learnable Retinex for low-light city environment

ABSTRACT In the smart city context, efficient urban surveillance under low-light conditions is crucial. Accurate object detection in dimly lit areas is vital for safety and nighttime driving. However, subpar, poorly lit images due to environmental or equipment limitations pose a challenge, affecting precision in tasks like object detection and segmentation. Existing solutions often involve time-consuming, inefficient image preprocessing and lack strong theoretical support for low-light city image enhancement. To address these issue, we propose an end-to-end pipeline named LAR-YOLO that leverages convolutional network to extract a set of image transformation parameters, and implements the Retinex theory to proficiently elevate the quality of the image. Unlike conventional approaches, this innovative method eliminates the need for hand-crafted parameters and can adaptively enhance each low-light image. Additionally, due to a restricted quantity of training data, the detection model may not achieve an adequate level of expertise to enhance detection accuracy. To tackle this challenge, we introduce a cross-domain learning approach that supplements the low-light model with knowledge from normal light scenarios. Our proof-of-principle experiments and ablation studies utilising ExDark and VOC datasets demonstrate that our proposed method outperforms similar low-light object detection algorithms by approximately 13% in terms of accuracy.

Determining the estimated cost of project works using aggregated indicators of labor intensity.

The article is devoted to the determination of the estimated cost of project works using aggregated indicators of labor intensity. The instruction on determining the estimated cost of design works of construction objects provides for the application of percentage indicators of the cost of design works to the calculation base. The cost of the calculation base is determined with the help of similar objects, or by aggregated indicators of the cost of construction per unit of measurement. The use of analog objects has a number of disadvantages, namely: limited experience and qualifications of designers, an indefinite number of comparative objects that are available in the archive of the project enterprise, the total cost of the analog object includes the cost of material resources that affect on the estimated cost of the analogue object, and accordingly on the cost of the estimated project documentation.
 To get rid of the presence of the specified shortcomings, it is necessary to change the basis of justification for determining the cost of project works. It was found that the basis can only be regulatory labor intensity, as it is stable and uniform for all regions of Ukraine.
 The authors propose to change the basis of the justification for determining the cost by arriving at the determination of the labor intensity of the analogue object using the following methodology. The methodology shows the extent to which specific values of technical and economic indicators change for homogeneous groups of housing reconstruction objects and regression equations of specific values of labor intensity for certain types of works of a similar object.
 The methodology was developed on the basis of normative labor intensity values of more than 30 objects of housing reconstruction. It was determined that the normative labor intensity depends on the technical and economic indicators of the objects under investigation, namely: perimeter, height, superstructure volume and base area. As a result of the study, the limits of technical and economic indicators corresponding to 3 regression models of specific values of normative labor intensity were obtained.
 To verify the specified methodology, an example of determining the specific labor costs of a similar object depending on the specific values of the technical and economic indicators of the new object based on the regression equations of homogeneous groups is given. According to the equations of homogeneous groups and the corresponding technical and economic indicators, a search is made for the correspondence of the technical and economic indicators of the object under study.
 The specified method allows you to find a quantitative estimate of the cost of project works by individual types.

COMPULSORY MADRASAH EARLY EDUCATION PROGRAM IN PASURUAN REGENCY: BETWEEN PUBLIC POLICY AND POLITICAL ORDERS

Research on the madrasah diniyah compulsory education program in Pasuruan Regency was conducted to reveal the relationship between public policy and the power of a particular religious group. A qualitative approach was applied to this research, by presenting facts that had been collected through several reports and in-depth interviews. Furthermore, the fact findings are analyzed based on the principles, concepts and theories of public policy. Research also relies on the results of existing research, especially research with similar material objects and similar perspectives. The focus of this research is to analyze the background of policy making, the dynamics of policy making, and program implementation. The results of this study indicate that the purpose of establishing the madrasah diniyah compulsory education program is to instill religious values to build morality and overcome crime. This research also confirms that regulations related to compulsory education at madrasah diniyah in Pasuruan Regency were formed because of the closeness between policy makers and Nahdlatul Ulama (NU) as a social organization. NU is the dominant force in social, cultural, and governmental politics. This research also clearly finds indications of policy exclusivity. The stipulated program only contains the interests of one group, thus reducing the power of other groups as elements that also live in the public sphere. Another implicative aspect is the increasing implementation of madrasah diniyah in Pasuruan district which is followed by an increase in the quality of teachers.

Creating a Collaborative Teaching Culture: The Case Study of the Value of a Professional Learning Community

In sport management higher education, doctoral students and early-career faculty are often tasked with teaching university courses, yet the training and support rarely match the demands of the job. This paper explores the utility of a professional learning community (PLC) within a sport management department at an American university using a case study methodology. A PLC creates space for colleague collaboration among faculty with similar objectives and goals. The PLC examined in this manuscript includes a mix of experienced (i.e., associate and full professors) and inexperienced professors (i.e., assistant professors and doctoral students) sharing their classroom/instructional experience at the undergraduate and master’s levels. PLCs represent a viable solution to help ease the classroom transition for early-career instructors and provide a platform for faculty to seek advice related to challenges. Findings suggest that the proper implementation of PLCs can lead to perceived increases in teaching effectiveness, self-efficacy, and collaboration amongst experienced and inexperienced faculty. Other benefits of PLCs include allowing faculty members to remain attuned to current classroom trends and maintaining a common philosophical mission/vision for the degree program.

Remote sensing image super-resolution via cross-scale hierarchical transformer

ABSTRACT Global and local modeling is essential for image super-resolution tasks. However, current efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth observation scenarios, resulting in suboptimal single-scale representations in global and local modeling. The key motivation of this work is inspired by two observations: 1) There exists hierarchical features at the local and global regions in remote sensing images, and 2) they exhibit scale variation of similar ground objects (e.g. cross-scale similarity). In light of these, this paper presents an effective method to grasp the global and local image hierarchies by systematically exploring the cross-scale correlation. Specifically, we developed a Cross-scale Self-Attention (CSA) to model the global features, which introduces an auxiliary token space to calculate cross-scale self-attention matrices, thus exploring global dependency from diverse token scales. To extract the cross-scale localities, a Cross-scale Channel Attention (CCA) is devised, where multi-scale features are explored and progressively incorporated into an enriched feature. Moreover, by hierarchically deploying CSA and CCA into transformer groups, the proposed Cross-scale Hierarchical Transformer (CHT) can effectively explore cross-scale representations in remote sensing images, leading to a favorable reconstruction performance. Comprehensive experiments and analysis on four remote sensing datasets have demonstrated the superiority of CHT in both simulated and real-world remote sensing scenes. In particular, our CHT outperforms the state-of-the-art approach (TransENet) in terms of PSNR by 0.11 dB on average, but only accounts for 54.8% of its parameters.

Cometary Activity Discovered on Vacationing Centaur 2019 OE31

We have detected cometary activity on minor planet 2019 OE31 through both the Active Asteroids Citizen Science program and an independent archival search. Before 2013, 2019 OE31 was on a Centaur orbit, between the orbits of Jupiter and Neptune. Centaurs are objects in transition from the outer solar system to the inner solar system. They play a vital role in the understanding of the Kuiper Belt and comets. In 2013 October, following a close encounter with Jupiter, 2019 OE31 moved to an orbit entirely interior to that of Jupiter. This reduced orbital distance and, hence, increased temperature is likely the cause of the observed activity. Through a suite of orbital dynamics simulations, we find that 2019 OE31 will experience many more similar encounters and is statistically likely to return to a Centaur orbit, potentially within the next 80 yr, from its current “vacation.”

Moving scene object tracking method based on deep convolutional neural network

The effect of target tracking is not ideal when facing various complex tracking scenarios such as non-rigid deformation of target, frequent occlusion, clutter of target background and interference of similar objects. In this paper, the feature based on deep convolutional neural network is used for target tracking in moving scenes, and a sliding window target segmentation method is proposed to study the impact of data normalization and data set expansion on the final result. In order to select more distinguishing features, principal component analysis is used to process the features of Deep Convolution Neural Network (DCNN), and the features of different network layers of DCNN are compared. The feature coding algorithm is studied, and the extracted DCNN features are encoded by Fisher Vectors algorithm, and compared with the locality-constrained linear encoding technique. Experiments show that the feature based on deep convolutional neural network in this paper can obtain higher accuracy than the traditional feature fusion method. According to the result analysis, the tracking accuracy of deep convolutional neural network algorithm is improved under the condition of illumination variation. In the case of local occlusion, the tracking accuracy is also improved.

A Dual Perspective on Geostationary Satellite Monitoring Using DSLR RGB and sCMOS Sloan Filters

This paper outlines a multi-system approach for ground-based optical observations and the characterization of satellites in geostationary orbit. This multi-system approach is based on an in-depth analysis of the key factors to consider for light curve analysis of Earth’s orbiting satellites. Light curves have been observed in different spectral bands using two different systems. The first system is specialized for astronomical observations and consists of a telescope equipped with an sCMOS camera and Sloan photometric filters. In contrast, the second system is a more cost-effective solution designed for professional non-astronomical applications, incorporating DSLR cameras equipped with RGB channels associated with a Bayer mask and photographic lenses. This comparative analysis aims to highlight the differences and advantages provided by each system, stressing their respective performance characteristics. The observed light curves will be presented as a function of the phase angle, which depends on the relative positions of the observer, the object, and the Sun. This angle plays an important role in optimizing the visibility of Earth’s orbiting satellites. Finally, multiband observations of different satellites will be compared to seek an associated spectral signature, which may allow the identification of structurally similar objects through optical observations.