Entire Object Research Articles

Architecture and the development of downgraded urban areas

Architecture surrounds us daily. We notice low-rise buildings and high-rise buildings, big and small edifices but we hardly ever notice their design or beauty, not to mention their surroundings, the urban interior where they are situated. This also applies to contemporary architecture. The paper suggests focusing on objects that function as museums, especially modern art museums which become characteristic features in urban space and to which urban systems are subordinated, e.g. Frank Ghery’s Bilbao centre. This function has become important and distinct over the last two centuries. That is why it is placed in objects whose architecture is special: sometimes extravagant and often monumental in scale but always significant on an urban scale. It is because architecture is not only a costume or a shell enclosing any odd interior. It forms an integral part of the urban context which is an important pretext for making it happen. It is also a composition of volumes, their forms, functions, light, the idea contained within the entire object, and first of all, its subjective perception analysed in terms of directing public attention to particular objects with a view to making them distinct. The paper presents an analysis of some examples of European museums of modern art – including some Polish ones – as significant architecture which is a key component on the scale of a city or its fragment. The aim is to stress the importance of the activities of local authorities and architects and the decisions they make for the development of downgraded urban areas.

Relevansi Pemeringkatan Kata Kunci Dengan Menggunakan Dalil Zipf Pada Abstrak Skripsi Hukum Perdata Fakultas Hukum Universitas Sriwijaya Tahun 2018-2022

This study aims to see the relevance of the results of subject indexing through the zipf argument with thesis keywords in several thesis abstracts for the Civil Law Department, Faculty of Law, Sriwijaya University 2018-2022 and also to find out the topic of the thesis. This research uses a quantitative descriptive approach with a bibliometric measurement method using the zipf theorem. The object is the undergraduate student thesis of the Department of Civil Law, Faculty of Law, University of Sriwijaya from 2018 to 2022 as many as 60 thesis abstracts. The research sample is the entire research object. The results of the research apply the use of the zipf argument as a method for determining keywords in the thesis abstracts of the civil law study program, Faculty of Law, Sriwijaya University, 2018-2022. Keywords as a result of zipf ranking are then compared with the author's keywords to obtain the level of relevance of these keywords. From the calculation of the relevance, it can be said that indexing through Zipf's law shows a high level of marginal relevance when equated with the relevant level and irrelevant index results. The proof is through estimatio

Efek Serbuk Jahe Merah (Zingiber Officinale Var Rubrum) Terhadap Penurunan Intensitas Dismenore Pada Siswi SMAN 1 Muncang Kabupaten Lebak Banten

Introduction: Dysmenorrhea is a condition that occurs during menstruation or menstruation which can interfere with activities and requires treatment which is characterized by pain or pain in the abdominal or pelvic area "(Judha 2012)", Dysmenorrhea is pain during menstruation that interferes with women's daily activities. According to WHO (2012), there were 1,796,425 people (90%) experiencing dysmenorrhea with 10-15% experiencing severe dysmenorrhea. About 75% of women report some degree of discomfort related to menstruation, and about 15% report severe dysmenorrhea. Methods: The research method is a pre-experimental research design. In this study, the authors conducted quantitative research using the pre-experimental design method, type one group pretest-posttest (preliminary test, final test, single group). Sampling was carried out by purposive sampling and in this study the samples were partially taken from the entire object studied and considered to represent the entire population of class X, X1, and XII students at SMAN 1 Muncang in the year 2022/2023 who experienced dysmenorrhea as many as 56 people. Results: The results of the Wilcoxon test showed that the p-value of red ginger was 0,000 0,05 so there was an effect of giving red ginger. The experimental group was given capsules containing red ginger powder 4x1 for 2 days at a dose of 250 mg. The results of the independent t-test obtained a p-value of 0,000 (α 0,05). Discussion: Broadly speaking, there is an effect of red ginger powder (Zingiber officinale var. Rubrum) on reducing the intensity of dysmenorrhea pain in female students at SMAN 1 Muncang, Lebak Regency Banten before and after consuming red ginger powder (Zingiber officinale var. Rubrum) as evidenced by the results of statistical tests using One-Sample Statistics with a significance value of p 0,000.

A support vector machine approach for identification of pleural effusion

In this research, we investigated the method which was based on a support vector machine (SVM) to identify pleural effusion on the thoracic image. SVM is a method of machine learning that works well when applied to data outside the training set. We formulated the detection of pleural effusion and applied SVM to develop the identification algorithm. We applied SVM to detect thoracic images whether they identified as pleural effusion or normal. The identification of pleural effusion on the thoracic image was conducted through some processes such as the determination of the region of interest (ROI), segmentation, morphology operation, measurement of the sharpness value and slope value, training as well as testing. Determining ROI was intended to focus the measurement on the left side of the chest. Segmentation was carried out to separate lungs object from the background. Morphology operation was carried out for cavities on the object as the segmentation result to obtain the entire object so that the measurement of the slope's lower part image could be done perfectly. The training was carried out on 100 thoracic images, 50 of them were identified with pleural effusion and the other 50 were normal. The objective was to find the hyperplane with the parameter input such as the sharpness value and slope value of the lungs on the thoracic image. We tested the method proposed based on doctors' diagnosis using 50 thoracic images, 25 of which were identified with pleural effusion and the other 25 were normal. From the result of the test, the accuracy of the method we proposed was 96%.

Training and Applying Artificial Neural Networks in Traffic Light Control: Improving the Management and Safety of Road Traffic in Tyumen (Russia)

The article presents the initial experience (spring-summer 2023) of using artificial neural networks (ANN) to improve traffic management in the large Russian city of Tyumen. Using the example of one of the intersections of the city's road network, it is shown how much transport delays are reduced when the duration of the traffic light cycle phases is quickly adjusted to the actual traffic intensity when compared with the usual previously used traffic light predictive mode. For the specific intersection of Odesskaya and Kotovskogo streets in Tyumen, considered in this article, the traffic light control mode using an ANN can significantly (by 20.6 ... 22.4%) reduce the average delay time of vehicles. It is also important that the reduction in traffic delays, which is possible with the regulation of traffic using ANN, helps to reduce stress for road users and improve road safety. The article presents historical data illustrating the dynamics of changes in the field of traffic management and road safety in Tyumen. This information confirms the thesis about the dialectic of systemic development and the need for a gradual increase in the intellectual component of traffic management in large cities. The Applications (Appendix A and Appendix B) present the code of the auxiliary procedures and functions module and the code of the main data collection module used to optimize the traffic light control mode at the experimental intersection of the Tyumen road network. The main conclusion of the study is that the use of an ANN allows for taking into account a much larger number of factors and optimizing the control of the entire object, consisting of several intersections, which is not achievable using predictive modes and local adaptive control.

Local fractal dimension of collagen detects increased spatial complexity in fibrosis

Increase of collagen content and reorganization characterizes fibrosis but quantifying the latter remains challenging. Spatially complex structures are often analyzed via the fractal dimension; however, established methods for calculating this quantity either provide a single dimension for an entire object or a spatially distributed dimension that only considers binary images. These neglect valuable information related to collagen density in images of fibrotic tissue. We sought to develop a fractal analysis that can be applied to 3-dimensional (3D) images of fibrotic tissue. A fractal dimension map for each image was calculated by determining a single fractal dimension for a small area surrounding each image pixel, using fiber thickness as the third dimension. We found that this local fractal dimension increased with age and with progression of fibrosis regardless of collagen content. Our new method of distributed 3D fractal analysis can thus distinguish between changes in collagen content and organization induced by fibrosis.

SACuP: Sonar Image Augmentation with Cut and Paste Based DataBank for Semantic Segmentation

In this paper, we introduce Sonar image Augmentation with Cut and Paste based DataBank for semantic segmentation (SACuP), a novel data augmentation framework specifically designed for sonar imagery. Unlike traditional methods that often overlook the distinctive traits of sonar images, SACuP effectively harnesses these unique characteristics, including shadows and noise. SACuP operates on an object-unit level, differentiating it from conventional augmentation methods applied to entire images or object groups. Improving semantic segmentation performance while carefully preserving the unique properties of acoustic images is differentiated from others. Importantly, this augmentation process requires no additional manual work, as it leverages existing images and masks seamlessly. Our extensive evaluations contrasting SACuP against established augmentation methods unveil its superior performance, registering an impressive 1.10% gain in mean intersection over union (mIoU) over the baseline. Furthermore, our ablation study elucidates the nuanced contributions of individual and combined augmentation methods, such as cut and paste, brightness adjustment, and shadow generation, to model enhancement. We anticipate SACuP’s versatility in augmenting scarce sonar data across a spectrum of tasks, particularly within the domain of semantic segmentation. Its potential extends to bolstering the effectiveness of underwater exploration by providing high-quality sonar data for training machine learning models.

Module of Axis-based Nexus Attention for weakly supervised object localization

Weakly supervised object localization tasks remain challenging to identify and segment an entire object rather than only discriminative parts of the object. To tackle this problem, corruption-based approaches have been devised, which involve the training of non-discriminative regions by corrupting (e.g., erasing) the input images or intermediate feature maps. However, this approach requires an additional hyperparameter, the corrupting threshold, to determine the degree of corruption and can unfavorably disrupt training. It also tends to localize object regions coarsely. In this paper, we propose a novel approach, Module of Axis-based Nexus Attention (MoANA), which helps to adaptively activate less discriminative regions along with the class-discriminative regions without an additional hyperparameter, and elaborately localizes an entire object. Specifically, MoANA consists of three mechanisms (1) triple-view attentions representation, (2) attentions expansion, and (3) features calibration mechanism. Unlike other attention-based methods that train a coarse attention map with the same values across elements in feature maps, MoANA trains fine-grained values in an attention map by assigning different attention values to each element. We validated MoANA by comparing it with various methods. We also analyzed the effect of each component in MoANA and visualized attention maps to provide insights into the calibration.

Coronavirus-like all-angle all-polarization broadband scatterer

Creeping waves traveling around a volumetric electromagnetic scatterer provide a significant contribution to its radar cross-section. While quite a few efforts were devoted to suppressing creeping waves as a part of radar countermeasures, here we utilize specially engineered creeping waves to our advantage to create broadband, all-angle, and polarization scatterers. Metalized spherical surfaces, patterned with corona virus-like spikes are designed to provide a broadband constructive interference between the specular reflection and creeping waves, elevating the scattering cross-section. The demonstrated miniature corona scatterers utilize both resonant cascading phenomena and traveling wave interference to tailor electromagnetic interactions, outperforming a resonant dipole in terms of amplitude and bandwidth quite significantly. Our experimental samples are fabricated with an additive manufacturing technique, where a 3D-printed plastic skeleton is subsequently metalized. Micron-thick layers allow governing electromagnetic interactions as if the entire object was made of solid metal. Lightweight, all-angle, all-polarization, and broadband compact scatterers such as these, reported here, have numerous applications, including radar deception, electromagnetic beckoning, and many others.

Nanoform of Phospholipid Composition: Investigation of the Morphological Features by Atomic Force Microscopy.

Morphological features of the nanoform of a phospholipid composition (NFPh), which can be used as an individual pharmaceutic agent or as a platform for designing drug delivery systems, have been studied using atomic force microscopy (AFM). NFPh has been developed, and its characteristics have been investigated using conventional drug analysis methods, including the determination of the mean diameter of nanosized vesicles in the emulsion via dynamic light scattering (DLS). Using DLS, the mean diameter of the vesicles was found to be ~20 nm. AFM imaging of the surface has revealed four types of objects related to NFPh: (1) compact objects; (2) layer fragments; (3) lamellar structures; and (4) combined objects containing the compact and extended parts. For type (4) objects, it has been found that the geometric ratio of the volume of the convex part to the total area of the entire object is constant. It has been proposed that these objects formed owing to fusion of vesicles of the same size (with the same surface-to-volume ratio). It has been shown that this is possible for vesicles with diameters of 20 nm. This diameter is in good coincidence with the value obtained using DLS.

Boosting the Response of Object Detection and Steering Angle Prediction for Self-Driving Control

Our previous work introduced the LW-YOLOv4-tiny and the LW-ResNet18 models by replacing traditional convolution with the Ghost Conv to achieve rapid object detection and steering angle prediction, respectively. However, the entire object detection and steering angle prediction process has encountered a speed limit problem. Therefore, this study aims to significantly speed up the object detection and the steering angle prediction simultaneously. This paper proposes the GhostBottleneck approach to speed the frame rate of feature extraction and add the SElayer method to maintain the existing precision of object detection, which constructs an enhanced object detection model abbreviated as LWGSE-YOLOv4-tiny. In addition, this paper also conducted depthwise separable convolution to simplify the Ghost Conv as depthwise separable and ghost convolution, which constructs an improved steering angle prediction model abbreviated as LWDSG-ResNet18 that can considerably speed up the prediction and slightly increase image recognition accuracy. Compared with our previous work, the proposed approach shows that the GhostBottleneck module can significantly boost the frame rate of feature extraction by 9.98%, and SElayer can upgrade the precision of object detection slightly by 0.41%. Moreover, depthwise separable and ghost convolution can considerably boost prediction speed by 20.55% and increase image recognition accuracy by 2.05%.

Global superpixel-merging via set maximum coverage

Due to better boundary adherence and low computational cost, the superpixel segmentation algorithm SLIC (simple linear iterative clustering) has been widely applied in vision-based applications. However, limit to unavoidable over-segmentation problem, one has to consider region-merging to reconstruct entire objects from the segmented superpixels (or called regions). The existing region-merging methods are generated from data clustering, and avoidably suffer from error-merging, slow convergence speed, or easily dropping in LOCAL optimal problems, especially for high-resolution RS (remote sensing) images. In this paper, instead of data clustering, we propose a fast GLOBAL method based on Set Maximum Coverage, termed as MaxCov-merging. Theoretically, the existence of the maximum coverage is proved by using Bayes optimal decision principle. To speed up MaxCov-merging, some heuristic strategies are also provided. Finally, extensive verification and comparison are carried on the public and our collected high-resolution images. Compared with the state-of-the-art methods, the comparison shows the superiority of our MaxCov in terms of the performance of globality, ease of use and fast region-merging speed.

Analysis of Hapanasan Water Tourism Management Strategies Facing the Post-COVID-19 Pandemic Period Rambah Tengah Hulu Village, Riau Province

The Covid-19 pandemic has had a negative impact on various tourism sectors. In the implementation of tourism destination management by Rokan Hulu Regency, strategic steps such as improving facilities, building new facilities for the convenience of tourists, supervision for compliance with health standards to reduce the spread of Covid-19, are less carried out by the management. This study aims to analyze how the management of water Hapanasan tourism destinations faces the conditions of the Covid-19 pandemic, to identify and analyze the current management of water Hapanasan, and to find out the strategy of sustainable management of water Hapanasan tourism destinations. Population is the entire hot water tourism object Rambah Tengah Hulu Village, Riau Province The sample used in this study is purposive sampling. To achieve this goal, the author uses descriptive qualitative methods, with observation, in-depth interviews with key persons, and literacy as data collection methods. The results of this study show that the management carried out did not run smoothly so that many facilities were abandoned during the pandemic, but when it was loose, managers began to repair damaged facilities and return to maintain them, and there was no sustainable management strategy after the Covid-19 pandemic. The results of this study will have implications for improving the quality of tourism infrastructure, management strategies that can be done in tourist destinations, managing human resource capabilities. All of that can make water Hapanasan a sustainable tourism destination.

Influence of Tilting Angle on Temperature Measurements of Different Object Sizes Using Fiber-Optic Pyrometers.

This article presents a new model of optical power gathered by a fiber-optic pyrometer when there is a tilting angle between the fiber longitudinal axis and the vector perpendicular to the tangent plane of the emitted surface. This optical power depends on the fiber specifications, such as the diameter and the numerical aperture (NA), as well as the object parameters, including its diameter, emissivity, and tilting angle. Some simulations are carried out using other pyrometers from the literature without tilting to validate the model. Additional simulations with different optical fibers, object sizes, and distances at different tilting angles allow us to describe the behavior of the pyrometer when the object is smaller than the optical fiber field of view (the light cone defined by its NA). The results show that for a finite surface object, the power collected by the optical fiber is affected by changes in the tilting angle, greater tilting lesser gathered power, and reaching the maximum power when the field of view of the fiber covers up the entire object, as expected. On the other hand, additional equations are presented to describe the maximum tilting angle, and distance that allow the maximum power gathered for a determined object diameter and fiber, avoiding temperature measurement errors.

Automated Creation of an IFC‐4 Compliant Damage Model from a Digital Inspection Supported by AI.

AbstractThis paper describes a technological solution for digital condition inspection, which includes the entire process, starting with on‐site image acquisition upto the IFC‐4‐compliant result. In detail, the process includes the following steps: – Digital image recording of the entire object on site – Automated creation of a 3D model from the image data in the form of a textured scatter plot – Generation of a BIM geometry model from the scatter plot – Fully automated damage detection in the original images using AI (accurate to the pixel) – Correct location of the damage on the 3D model as a polyline and linking the damage with the associated damage information in a database (e.g., crack length, crack width, damage area) – Fully automated creation of an IFC‐4 compliant damage model.

The Effect of Variation Training on Passing and Stopping Skills in Football on Students’ Extracurricular at SMPN 1 Siak Hulu, Kampar Regency

Based on observations conducted at Students’ Extracurricular at SMPN 1 Siak Hulu, Kampar Regency, the author notices that the basic techniques of Passing and Stopping are still lacking. This is proven by the inaccurate passing of the ball to the other players, and the students' inability to stay in control of the ball when receiving passes, which prevents football game from reaching maximum results, especially in real matches. Football passing and stopping skills can be improved through variation training methods. This study aimed to determine if different exercises have an effect on passing and stopping in football on students’ extracurricular at SMPN 1 Siak Hulu, Kampar District. This is quantitative research applying a Pre-experiment design with a Pretest-Posttest design and a statistical formula or T-score. Population is the entire object of research, so for this study, the population consisted of all 24 students who participated in extracurricular football activities at SMPN 1 Siak Hulu, Kampar Regency. The t-count value for this study was 13.18, while the t-table with a significant 5% and db = N-1 (24-1 = 23) was 2.07. Therefore, t count > t table. Thus, there is a substantial difference between the results of the Pretest and Posttest. Based on the findings of this study, it can be concluded that the proposed hypothesis regarding the relationship between Variation Training on Passing and Stopping Skills in Football on Students’ Extracurricular at SMPN 1 Siak Hulu, Kampar Regency is accepted. Students who participate in extracurricular football activities at SMPN 1 Siak Hulu, Kampar Regency, and who wish to improve their football skills are advised to always practice diligently. This is due to the fact that a form of sport skill can only be acquired through regular, continuous practice.

Aging and temporal integration in the visual perception of object shape

It has been known for more than 160 years that highly occluded objects that would normally be visually unrecognizable can be successfully identified when they move. This anorthoscopic perception relies on the visual system’s ability to integrate information over time to complete the perception of an entire object’s shape. In this experiment, 16 younger and older adults (mean ages were 20.5 and 74.6 years, respectively) were familiarized with the (unoccluded) shapes of five naturally-shaped objects (bell peppers, Capsicum annuum) until they could be easily identified (i.e., with accuracies of at least 90 percent correct). All observers then viewed the stimulus objects anorthoscopically as they moved behind narrow slits; only small object fragments could be seen at any given time, because the objects were almost totally occluded from view. Even though the object identification performance for all observers was equivalent when whole object shapes were visible, a large age-related deficit in object identification emerged during anorthoscopic viewing such that the younger adults’ identification performance was 45.4 percent higher than that of the older adults. This first ever study of aging and anorthoscopic perception demonstrates that there is an age-related deficit in performing the temporal integration needed for successful object recognition.

Multisensory causal inference is feature-specific, not object-based.

Multisensory integration depends on causal inference about the sensory signals. We tested whether implicit causal-inference judgements pertain to entire objects or focus on task-relevant object features. Participants in our study judged virtual visual, haptic and visual-haptic surfaces with respect to two features-slant and roughness-against an internal standard in a two-alternative forced-choice task. Modelling of participants' responses revealed that the degree to which their perceptual judgements were based on integrated visual-haptic information varied unsystematically across features. For example, a perceived mismatch between visual and haptic roughness would not deter the observer from integrating visual and haptic slant. These results indicate that participants based their perceptual judgements on a feature-specific selection of information, suggesting that multisensory causal inference proceeds not at the object level but at the level of single object features. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Spin-mediated shear oscillators in a van der Waals antiferromagnet.

Understanding how microscopic spin configuration gives rise to exotic properties at the macroscopic length scale has long been pursued in magnetic materials1-5. One seminal example is the Einstein-de Haas effect in ferromagnets1,6,7, in which angular momentum of spins can be converted into mechanical rotation of an entire object. However, for antiferromagnets without net magnetic moment, how spin ordering couples to macroscopic movement remains elusive. Here we observed a seesaw-like rotation of reciprocal lattice peaks of an antiferromagnetic nanolayer film, whose gigahertz structural resonance exhibits more than an order-of-magnitude amplification after cooling below the Néel temperature. Using a suite of ultrafast diffraction and microscopy techniques, we directly visualize this spin-driven rotation in reciprocal space at the nanoscale. This motion corresponds to interlayer shear in real space, in which individual micro-patches of the film behave as coherent oscillators that are phase-locked and shear along the same in-plane axis. Using time-resolved optical polarimetry, we further show that the enhanced mechanical response strongly correlates with ultrafast demagnetization, which releases elastic energy stored in local strain gradients to drive the oscillators. Our work not only offers the first microscopic view of spin-mediated mechanical motion of an antiferromagnet but it also identifies a new route towards realizing high-frequency resonators8,9 up to the millimetre band, sothe capability of controlling magnetic states on the ultrafast timescale10-13 can be readily transferred to engineering the mechanical properties of nanodevices.

Integration of high-precision UAV laser scanning and terrestrial scanning measurements for determining the shape of a water tower

This study was aimed at integrating surveys from terrestrial laser scanning (TLS) and high-precision scanning performed by a drone (ULS) for a tower structure. This will allow a complete point cloud to be obtained for the entire object. Integrated measurements will eliminate gaps in the point cloud, characteristic of TLS or ULS separate measurements. Existing studies report ULS accuracy of a few cm, but the precision may be about 10 mm. Assuming that the ULS point cloud will be matched to the TLS cloud, which has higher accuracy, the high accuracy of the whole integrated model can be expected. The integration of the surveys was carried out for the water tower and a coherent model of the structure was obtained, with a mean merge error of 13 mm. Detailed comparative analyses were carried out in selected cross-sections, obtaining mostly a several-millimetre compliance of the results in the analysed categories.