Concrete Images Research Articles

A scoping review of robotic technology in hospitality and tourism

Purpose With the disruptive evolution of artificial intelligence (AI), the roles of robotics in hospitality and tourism are shifting rapidly from automatic to emotional works. Although academics have focused on examining robotic applications in hospitality and tourism, concrete images of the different roles of robots are unclear. Thus, this study aims to systematically aggregate and evaluate existing robotic technology-related studies published in Social Science Citation Index-listed hospitality and tourism journals to link the fragmented knowledge and provide an up-to-date overview of robotic technology in hospitality and tourism. Design/methodology/approach This study retrieved 134 robotic technology-related articles and used descriptive and content analyses to analyze the retrieved papers thoroughly. Findings The top keyword identified was service robot. Robotic technologies are categorized into AI-supplemented, AI-generated, robotic technology anthropomorphism (RTA)-facilitated and RTA-mediated. Research limitations/implications Future studies can consider exploring service robots further from the perspectives of suppliers in tourism. A more comprehensive categorization of robotic technologies is also recommended. Originality/value This study contributes to the robotic research realm by providing a holistic view of robotic applications in hospitality and tourism research. This study also attempts to pin down the potential research directions to guide researchers in expanding future studies.

Midwives’ account of virtual reality childbirth education

In Finland, prenatal clinics and birthing hospitals struggle with resources to provide expectant parents with childbirth education (CBE) and a tour of the hospital birthing unit. Not preparing for birth is common, and not knowing how and where to give birth causes parents unnecessary stress during pregnancy. One new possibility to complement CBE is using virtual reality (VR) to deliver content. The aim of this study is to describe midwives' views of the status of parents' preparation for birth and provide information on the VR CBE program's advantages and challenges from the midwife's perspective. The study consisted of two focus group sessions with seven participants. Participants were midwives with experience from a hospital birthing ward and had provided childbirth education. The data was analysed using a thematic content analysis approach. According to the midwives, the parents do not prepare for birth well enough. Preparing for birth affects the parents' knowledge about childbirth, their cooperation with the healthcare personnel, and how they trust the birthing process, themselves, and the healthcare personnel. Preparation can also have adverse effects if the birthing person is trying to give birth with a detailed plan or has false expectations. Parents should focus on knowledge, attitude, and mental and physical exercise in preparing for childbirth. By preparing from trusted sources, parents can make informed decisions. They have the skills to stay calm, relaxed, and open-minded during labour and birth. Physical and mental exercise will help them cope with labour and relax their mind and bodies during birth. Midwives see VR as offering great potential for concrete, realistic, and flexible childbirth education that can address current challenges. They were also worried that VR could replace face-to-face contact. However, since there is minimal such contact in the current CBE, VR could enhance childbirth education and offer a realistic and concrete image of birth and a hospital tour. Midwives want families to prepare better for birth and work on their knowledge, attitude, and exercise. By preparing, parents understand childbirth, trust themselves, the birthing process, and the midwives; cooperation works better. Midwives see VR as an excellent addition to childbirth education by offering realistic and concrete online childbirth education. VR should not replace face-to-face contact.

When Silence Creates Memory: Performed Communities in Nineteenth-century Spanish and French Commemorations

Recently, the Third Phase in memory studies has challenged traditional concepts of memory that conceived it to be a concrete image or narrative set within a fixed moment and, consequently, neglect its changeable dimension and malleability. Focusing on a constructed narrative of the past, scholars have renewed the theoretical and methodological basis of this analytical field. This study adopts this new conceptualisation to analyse the intertwining of memory and silence in official commemorations during the regencies of María Cristina (1833–40) and Espartero (1840–43) in Spain and the July Monarchy (1830–48) in France. Its main contribution is to show that a deliberate omission of a narrative cannot only be linked to oblivion and can even become a space for creating memory.

A fine-segmentation algorithm for XCT images of multiphase composite building materials based on deep learning

In response to the challenges presented by the variable internal structures and complex components of multiphase composite building materials, this study introduces a novel image segmentation network named UT-FusionNet. Building on the U-Net model, UT-FusionNet integrates Transformer module and tensor concatenation and fusion mechanism to overcome the limitations of convolutional networks in relation to the receptive field. UT-FusionNet is employed to segment CT images of conventional concrete, grout consolidation bodies, and fiber-reinforced concrete. The results demonstrate that UT-FusionNet achieves superior segmentation accuracy and robustness, with Accuracy (ACC), Intersection over Union (IoU) and Dice scores exceeding 90 % across all subtasks. The mean accuracy metrics are 99.44 %, 96.70 %, and 98.31 %, respectively. This innovative end-to-end network offers robust support for detailed structural analysis, damage detection, and digital modeling of multiphase composite building materials through deep learning.

Evaluation method for uniformity of steel slag concrete aggregate based on improved YOLOv8

The use of steel slag as a replacement for natural aggregates in concrete effectively alleviates the issue of natural resource scarcity. The uniformity of aggregates in steel slag concrete directly affects its performance and durability. Therefore, precise segmentation of aggregates in sedimentation images is crucial for evaluating their uniformity. This paper not only proposes an improved instance segmentation model for accurate segmentation of steel slag concrete aggregates but also introduces a new method for assessing the uniformity of aggregates when steel slag replaces natural aggregates. This method quantitatively analyses the area, replacement rate, and spatial distribution of the aggregates and conducts a comprehensive quantitative analysis using the comprehensive uniformity evaluation coefficient, K. This approach allows for the evaluation of the uniformity of aggregates in sedimentation images of steel slag concrete. Experimental results show that the proposed improved model achieved an mAP50 of 98.3 %. Furthermore, the aggregate uniformity assessment method based on the improved model can objectively and accurately evaluate the uniformity of steel slag concrete aggregates.

Quantifying the effect of scanning parameters on digital volume correlation analysis for in situ X-ray imaging of concrete

The potential effects of X-ray Computed Tomography (XCT) scan settings on the kinematic field computed via Digital Volume Correlation (DVC) are discussed in this paper. A parametric sensitivity study was conducted, including a series of acquired XCT scans, probing the same volume of interest (VOI) of a concrete specimen, and applying various combinations of XCT scan settings. Different values were adopted for the number of projections acquired, the exposure time and the pixel size. The DVC results revealed a linear correlation between the mean normalised correlation residuals, designating the quality of the correlation and the reliability of the computed kinematic field and the scanning parameters. Gaussian and median filters were applied to the raw data to explore the effect of image noise smoothness in the DVC analysis. The significant reduction noted for the mean normalised correlation residuals was not accompanied by similar levels of change for the corresponding error uncertainties, as defined by the displacement magnitude. The concept was tested against a realistic loading scenario, where the DVC kinematic fields, computed for various XCT “recipes”, were compared. Slim differences in the distribution of the mean minimum principal strain were observed.

An improved EnlightenGAN shadow removal framework for images of cracked concrete

The concrete crack images in engineering are usually obscured by unexpected shadow and their removal is usually required which is always a challenging task due to its complexity and diversity. An improved Enlighten Generative Adversarial Networks (IEnlightenGAN) is developed in a proposed framework to improve shadow removal results. The Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) is firstly developed to improve the concrete image resolution. Residual-in-residual-dense blocks module and Atrous Spatial Pyramid Pooling module are then integrated into the IEnlightenGAN to improve its shadow removal capability. A quality assessment methodology on the shadow removal results is also proposed. The crack contour is extracted based on U-Net, and denoising is conducted based on morphological closing. Image segmentation metrics are used for the evaluation. The proposed IEnlightenGAN and the evaluation method are trained and verified using a dataset containing both normal and shadowed concrete crack images with comparison of results from the state-of-the-art models. The proposed shadow removal framework is found able to improve the quality of shadowed crack images, and the proposed quality assessment method is also found better than existing approach.

Physical characteristics identification of the concrete interfacial transition zone via 3D image scanning

The interfacial transition zone (ITZ) is the weakest link in concrete materials. The physical characteristics of the ITZ, such as its microhardness and thickness, significantly influence the mechanical properties and durability of concrete. Typically, microhardness testing is employed to identify the physical features of the ITZ through line measurements with a certain degree of randomness. In order to accurately describe ITZ physical characteristics, this study proposes a novel physical characteristics identification method via three-dimensional (3D) image scanning. The fundamental principle of this method lies in the varying hardness of each concrete phase, resulting in different surface elevations after polishing. As the polishing abrasiveness changes from coarse (240 mesh) to fine (800 mesh), the ITZ can be clearly observed in the 3D point cloud images of the experimental concrete. The ITZ geometrical information (thickness and different surface heights) can be easily identified using 3D scanned images. It was also found that there was a considerable linear regression relationship between the height difference and the relative microhardness variation; thus, the ITZ microhardness could be indirectly recognized through surface measurements. The physical characteristics of ITZ can be statistically analysed, effectively mitigating the bias inherent in traditional test results and significantly improving identification efficiency and accuracy.

Interpretation of Musical Topos in W. A. Mozart’s Sonata Cycles

The article represents an important problem of modern musicology and aims to reveal the specifics of the functioning of musical topos in the piano sonatas of W. A. Mozart. The concept of topos is interpreted as a field of abstract ideas and concrete images embodied by certain musical means. It is argued that the model of the functioning of musical topos in the sonata cycles of W. A. Mozart consists of the following main topos: gallant, heroic, pastoral, pathetic, comic, which include lesser, similar modifications. The main role in the sonatas is played by the topos of gallantry, which focuses on communicative forms of secular interchange. Therefore, revealing the semantic depth of the musical topos of Mozart’s sonatas will allow us to discover the musical cosmos of the composer’s artistic imagery. Keywords: sonata cycle, topos, semantics, style, principles of thinking, classicism.

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm.

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

Assessing the Influences of Noise Suppression Filters on Ultrasonic Concrete Images Generated by an Innovative CMU-SAFT Algorithm

AbstractUltrasonic imaging is a useful nondestructive testing technology for visualizing internal structural defects in structures. Despite its utility, since synthetic aperture focusing technique (SAFT) algorithm demands using advanced equipment to superimpose the measurements, this paper introduces a novel approach named CMU-SAFT to broaden applicability of SAFT on conventional ultrasonic data obviating the necessity for matrix antenna array-equipped devices and preventing hyperbolic patterns. To validate the feasibility of the proposed algorithm, experimental tests were conducted on a laboratory-produced concrete specimen including delamination defects at varying depths. Since other reflectives causing distortion in the ultrasonic image can interfere with the signal, the study also evaluated the influences of different noise suppression filters along with their combinations (band pass, wavelet transform, Wiener, and Savitzky–Golay). CMU-SAFT images were constructed using eleven filter combinations, and their performances were quantitatively assessed using signal-to-noise ratio, signal-to-noise and distortion ratio, total harmonic distortion, root mean square, mean squared ratio, mean absolute error and cross-correlation. The most effective filters and performance indices aligning with CMU-SAFT images considering defect depth and scanning width were suggested. The findings of the study revealed the leading potential of CMU-SAFT algorithm to overcome the need for specialized equipment by utilizing recommended filters and indicators under specific conditions.

Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass

AbstractScale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data augmentation. However, when presented with a scale far outside the range covered by the training set, neural networks may fail to generalize. Here, we introduce the Riesz network, a novel scale- invariant neural network. Instead of standard 2d or 3d convolutions for combining spatial information, the Riesz network is based on the Riesz transform which is a scale-equivariant operation. As a consequence, this network naturally generalizes to unseen or even arbitrary scales in a single forward pass. As an application example, we consider detecting and segmenting cracks in tomographic images of concrete. In this context, ‘scale’ refers to the crack thickness which may vary strongly even within the same sample. To prove its scale invariance, the Riesz network is trained on one fixed crack width. We then validate its performance in segmenting simulated and real tomographic images featuring a wide range of crack widths. An additional experiment is carried out on the MNIST Large Scale data set.

Investigations on lightweight concrete by in situ compression tests using high-resolution computed tomography (μ-CT).

The fracture behaviour of concrete is studied in various micro- and macro-damage models. This is important for estimating serviceability and stability of concrete structures. However, a detailed understanding of the material behaviour under load is often not available. In order to better interpret the fracture behaviour and pattern, images of lightweight concrete were taken using a high-resolution computed tomography (μ-CT) scanner. The samples were loaded between the taken images and the load was kept constant during the measurement. This study describes the method used and how the data set was analysed to investigate displacements and cracks. It has been shown that displacements and damage to the concrete structure can be detected prior to failure, allowing conclusions to be drawn about the structural behaviour. In principle, the μ-CT measurement can be used to examine different kinds of concrete as well as other systems with inorganic binders and to compare the fracture behaviour of different systems.

Parametric image-based concrete defect assessment method

Structural health monitoring aims to ensure the integrity of infrastructure. Assessing structural integrity through image classification techniques based on human perception is often challenging. Incorporating a morphological image-based crack detection algorithm can mitigate these limitations. This study proposes a parametric approach for concrete crack analysis using digital image processing techniques. The objective is to detect damage to the concrete surface of infrastructures, potentially exacerbating structural deterioration. The research justifies the utilization of digital parametric image processing techniques, including a weighted median filter, grayscale, Otsu filter, and other methods. These techniques were applied to evaluate different concrete images for crack detection. The findings were validated through Image Quality Assessment (IQA), determining the condition of the studied images and statistical properties using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perceptual Image Quality Evaluator (PIQE). Additionally, uncontrollable factors such as variations in lighting effects under different conditions, testing setups, and different textures of the concrete were considered in this study. The comprehensive experimental results demonstrated that the Otsu filter outperforms other filtering techniques. Overall, the study achieved remarkable accuracy, approximately 95%, in detecting cracks on building infrastructure. The proposed method holds potential for integration with advanced techniques in practical applications for the maintenance and safety of infrastructure. This research not only contributes to technological advancements in concrete defect assessment but also carries significant implications for the future of automated and reliable structural health monitoring.

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine.

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

Universal coordinates and methods of reformatting an ambiguous humanitarian paradigm

The main problem: In the conditions of market uncertainty, the coronavirus pandemic, the absence of an officially articulated national idea, national interests and ideology in Kazakhstan, the problem of the lack of logically justified and coordinated coordinates of systemic thinking, an unambiguous paradigm of humanitarian thinking (analytical, pedagogical, managerial, economic, etc.) is becoming more and more clearly realized. as the most important conditions for personal, civil and professional self-determination. Purpose: to substantiate the general logical coordinates and methods of reformatting the ambiguous humanitarian paradigm used in the activity. Methods: the ontological principle of genetically meaningful logic, the method of transformation of concrete images of objects based on their abstract essence, the method of ascent from the abstract to the concrete, the speculative language of schematic images of thought and the language of methodological theory of activity. The results and their significance: logically justified universal abstract-concrete coordinates of universal motion are given. In relation to life and the world of activity, they can be concretized by coordinates: "standard of living – efficiency of activity", or "efficiency of activity – time", or "results – costs", or "quality – quantity", etc. The use of motion vectors in the logic of descent from the concrete to the abstract (NCA), ascent from the abstract to the concrete (AAC), as well as in their combination – logic CAC gives certainty to thinking. As an example, the scheme of the qualitative difference between educational (pre-professional) activities from professional ones using these methods and paired dialectical categories is given. The scheme and description of the functional model of the system object, considered as a rubicon of the transformation of educational activity into professional activity, are given. To reformat the indefinite, ambiguous paradigms of the humanities used in the activity into functional-logical, visible, constructive and unambiguous, it is recommended to use abstract-concrete coordinates, the methods given, the language of schematic images and the paradigm of the methodological theory of activity. As an example, the concept of a typical unit of activity development is given. A visible unambiguous paradigm is a necessary, but not sufficient prerequisite for adequate personal, civil and professional self-determination of a person in situations of high uncertainty. A sufficient prerequisite is the acquisition by subjects of appropriate reflexive-thinking abilities for self-determination, self-organization, self-regulation, etc.

An ontological approach to the disclosure of a person's personal potential

The main problem: In the latest Messages of President Kassym-Jomart Tokayev to the people of Kazakhstan, it is indicated that "the systemic problems of our economy are well known. These are commodity dependence, low labor productivity, insufficient level of innovation, and uneven income distribution. Of course, all these are difficult problems, but there are specific ways to solve them. These are macroeconomic stability, economic diversification, digitalization, the development of small and medium-sized businesses, human capital, and ensuring the rule of law. However, there is still no tangible progress on these issues. Obviously, new approaches are needed" [1]. New approaches include methods and conditions for the disclosure of a person's personal and professional potential as the main value and supreme asset of any organization striving to maintain competitiveness in the market for as long as possible, which means jobs and the prospect of a decent life. As an alternative, Sayasat Nurbek believes, offering an Atlas of new professions, the country needs it first of all, because the higher education system has become detached from the real needs of the economy [2]. "For 30 years, we have not been able to create a simple applied tool for professional diagnostics and, in general, an understanding of the labor market, which professions will be in demand, and which ones will now leave the labor market or what they can transform into. Meanwhile, the stagnation in the public procurement system of Kazakhstan is failing, as the world has undergone a transformation of the labor market – digitalization is changing it. Thousands of people will be out of work because its structure and qualifications are evolving. The advent of artificial intelligence threatens the human monopoly on mental labor, which will be replaced by machines. People may cease to be in demand due to the creation of new "technological personalities (robots)" based on algorithms in mature markets, these are the forecasts of many experts" [2]. The authors of the article substantiate innovative approaches to the growth of a personality with a strong civic position, with high intellectual potential, with communicative and reflective abilities. Using an ontological approach to disclose a person's personal potential. Purpose: substantiation of the method (conditions) for the disclosure of a person's personal potential. Methods: the method of ascent from the abstract to the concrete, the method of converting concrete images of objects based on their abstract essence, the speculative language of schematic images of thought. Results, their significance: in relation to life and the world of activity, a person's personal potential is manifested in actions in the positions of "individual", "student" and "subject". The use of motion vectors in the logic of descent from the concrete to the abstract, ascent from the abstract to the concrete, as well as in their combination, gives certainty to thinking. As an example, the use of methods (AAC, LSI) is shown. Description of the cycle of personal potential disclosure, considered as a transformation in educational activity into a subject, where the personal potential of a person with sufficient ways and abilities is revealed to use them both in professional activities and in life. A sufficient prerequisite is the acquisition by subjects of appropriate reflexive thinking abilities for self-determination, self-organization, self-regulation, and so on.

Multi-scale ultrasonic imaging of sub-surface concrete defects

This paper introduces a new method for creating the ultrasonic image of concrete that utilizes both linear and nonlinear wave properties in a single measurement. Linear ultrasonic imaging relies on wave velocity. The resolution of linear ultrasonics for various inclusion sizes and inclusion-to-base material ratios is numerically and experimentally studied. The nonlinear ultrasonic testing (NLUT) image is based on the acoustic nonlinearity coefficient, calculated using inputs of the first and second harmonic amplitudes, wave number, and the distance between the transmitter and receiver. The ultrasonic waveform is decomposed into its harmonics using wavelet transform. Their wave velocities, wave numbers, and amplitudes are extracted to calculate the linear wave velocity and the acoustic nonlinearity coefficient. Four calibration samples with different inclusions are tested to evaluate the effectiveness of combining two imaging methods: pure concrete, concrete with small foam inserts simulating air inclusions, and two concrete samples with larger foam inclusions. The angle dependence of acoustic nonlinearity coefficient is shown. The results show that constructing both linear and nonlinear ultrasonic imaging is necessary when the defect’s properties are unknown.

High variability in learning materials benefits children’s pattern practice

Concrete materials (e.g., pictures, objects) are believed to be helpful with learning, but not in all circumstances. Variability in these materials (i.e., using different materials vs. the same materials) could be an important factor. We compared how variability in concrete images influenced children’s learning about repeating patterns (e.g., ABBABBABB). A total of 87 children aged 4 to 6 years from the United States (75% White; 44% female) completed an experiment via Zoom in which they received brief pattern training. Children were randomly assigned into Low, Medium, and High Variability training conditions, which differed in terms of whether the same materials were used over and over or they varied in their perceptual features. Children in the Low Variability condition performed better at the beginning of training, but this trend ultimately reversed. Children in the High Variability condition performed best by the end of training and on the posttest. Using variable materials may allow children to extract common structures across instances.

A new North Star

Leadership gurus often put creating a “shared vision” at the top of their pyramid of what successful leaders do. These visions are best when they are an anchor or a North Star – unwavering goals that the district works toward. Author Jon Saphier proposes this North Star for districts: Make every school an engine for continuous improvement of high-expertise teaching for equity. Saphier writes that the statement conjures concrete images of what to do to improve the experience of children and their learning. It comes with deep anchors in the research base on instructional improvement, deep roots in the literature of healthy organizational culture, and direct ties to the lived experiences of students in school.