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Forensic dentistry is the application of dentistry in legal proceedings that arise from any facts relating to teeth. The ultimate goal of forensic odontology is to identify the individual when there are no other means of identification such as fingerprint, Deoxyribonucleic acid (DNA), iris, hand print and leg print. The purpose of selecting dental record is for the teeth to be able to withstand decomposition, heat degradation up to 1600 ^°C. Dental patterns are unique for every individual. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic dental images for person identification.

To achieve an accurate identification of individuals, the missing tooth in the radiograph has to be identified before matching of ante-mortem (AM) and post-mortem (PM) radiographs. To identify whether the missing tooth is a molar or premolar, each tooth in the given radiograph has to be classified using a k-nearest neighbor (k-NN) classifier; then, it is matched with the universal tooth numbering system. In order to make exact person identification, this research work is mainly concentrate on contour shape extraction and texture feature extraction for person identification. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic images for individual identification. Then, shape matching of AM and PM images is performed by similarity and distance metric for accurate person identification.

The experimental results are analyzed for shape and feature extraction of both radiographic and photographic dental images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model, and it is well suited for forensic odontologists to identify a person in mass disaster situations.

Forensic odontology is a branch of human identification that uses dental evidence to identify the victims. In mass disaster circumstances, contours and dental patterns are very useful to extract the shape in individual identification.

The experimental results are analyzed both the contour shape extraction and texture feature extraction of both radiographic and photographic images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model and it is well suited for forensic odontologists to identify a person in mass disaster situations. The findings provide theoretical and practical implications for individual identification of both radiographic and photographic images with a view to accurate identification of the person.

The purpose of this paper is to develop a transform method and a deep learning model to identify the inner surface shape based on the measurement temperature at the outer boundary of the pipe.

The training process is assisted by the finite element method (FEM) simulation which solves the direct problem for the data preparation. To avoid re-meshing the domain when the inner surface shape varies, a new transform method is proposed to transform the shape identification problem into the effective thermal conductivity identification problem. The deep learning model is established to set up the relationship between the measurement temperature and the effective thermal conductivity. Then the unknown geometry shape is acquired by the mapping between the inner shape and the effective thermal conductivity through the inverse transform method.

The new method is successfully applied to identify the internal boundary of a pipe with eccentric circle, ellipse and nephroid inner geometries. The results show that as the measurement points increased and the measurement error decreased, the results became more accurate. The position of the measurement point and mesh density of the FEM model have less effect on the results.

The deep learning model and the transform method are developed to identify the pipe inner surface shape. There is no need to re-mesh the domain during the computation progress. The results show that the proposed method is a fast and an accurate tool for identifying the pipe inner surface.

Identification of human body shapes has been a key issue to develop sizing standards for ready‐to‐wear and to develop made‐to‐measure applications. Current methods to identify the body shapes are mostly based on subjective/visual determination approaches. The purpose of this paper is to look for numerical evaluation parameters for an objective method in order to classify the body shapes and to build up an automated process link.

Female subjects were chosen for the experimental design. 3D body scanning technology was integrated in the process for measurement taking and body silhouette detection of the sample group. Based on this sample data set, body shape identification was realized by referees as visual analyses, and additionally, an objective method was tried out by using body dimensions as numerical evaluation parameters. Obtained results with the sample group were inserted in a database and a body shape calculation tool was developed.

Statistical analyses showed that there is mostly a good agreement between the pairs of the evaluations including the objective calculation methods and the subjective assessments of the referees. The calculation tool was designed as web‐based software in order to integrate with further developments and automation purposes.

A new automatic tool was developed to make the body shape classification objective and repeatable. By integrating this tool to the product development chain, a continuous process link can be provided for the companies through the way for better fitting clothing.

The magnetic field was applied for identification of a shape of an inaccessible boundary between substances of different magnetic properties. Taking advantage of the conception of substitute field sources a mathematical model of the problem was described by means of the potential theory and integral equations. Numerical processing of Fredholm integral equations of the first kind appearing in the above model leads to a system of ill‐posed and non‐linear algebraic equations. This results in numerical instability. The problem was solved by the use of the fixed‐point technique based on Brouwer’s theorem. A few numerical examples were illustrated for the selected shape of the investigated boundary.

For actors in a tourism destination, the atmospheric turn initially means looking at themselves in a more holistic and differentiated way. Through the analysis of strategic visitor flows, it is possible to identify subspaces with high frequency, which thus become identification spaces of a destination. Together with other identification fields, they shape the destination brand understood as spatial and atmospheric entity. This enables a different view of a destination, its structure and generates new opportunities for destination management, which wll be discussed in the form of an outlook. A destination manager, for example, could in the future collaborate with governmental bodies responsible for spatial planning and with private builders on spatial design projects and introduce the perspective of the destination as a branded space.

Brand theory and practice have remained quite two-dimensional to this day and focus on logos, corporate design, website design, etc. As with atmospheres, it was the sales room where the brand idea was spatialised early on. This chapter discusses how to spatialise brand theory and to connect it with the place atmosphere model. Moreover, the chapter works out how the bridge between the strategy of an organisation (company, hotel, destination, etc.), its brand personality and the strategy of spatial design can be built. The brand personality shows itself in the long-term handling of the eight W questions of the brand space strategy (Who, Where, Wherein, What, Whom, Way to, What for and Why).

This paper aims to provide a review of different types of non-wearable human identification sensors which can be applied for smart home environment.

The authors performed a systematic review to assess and compare different types of non-wearable and non-intrusive human identification sensors used in smart home environment. The literature research adds up to 5,567 records from 2000 to 2016, out of which 40 articles were screened and selected for this review.

In this review, the authors classified non-wearable human identification technologies into four main groups, namely, object-based, footstep-based, body shape-based and gait-based identification technologies. Assessing these four group of identification technologies showed that the maturity of non-wearable identification is not high and most of these technologies are verified in a lab environment. Additionally, footstep-based identification is the most popular identification approach listed in the literature.

This study contributes to the literature on human identification technologies in several ways. This paper identifies the state-of-the-art regarding non-wearable technologies which can be used in smart home environment. Moreover, the results of this paper can provide a better understanding of advantages and disadvantages of the non-wearable identification technologies.

The fundamental mission of every green city is to harmonize urban living with environmental preservation. However, a critical challenge arises when the residents of such cities do not share the same level of environmental consciousness, potentially eroding the integrity of the green city’s brand identity. Hence, this study aims to explore the factors influencing residents’ identification with green city branding and assesses how this identification subsequently affects their green citizenship behaviors.

A survey of 1,217 residents from 15 green cities across six countries was conducted and analyzed using SPSS 28.0 for descriptive statistics and PLS-SEM for measurement and structural model analysis.

The results indicate that green city-self connection, green city distinctiveness and environmental benefits significantly impact GCRI, which, in turn, significantly influences green city protection, loyalty and advocacy behaviors. However, green city social and economic benefits did not affect resident identification.

The results of this study provide valuable managerial insights for city brand managers, green city developers and governmental representatives. The study underscores the importance of considering residents as crucial internal stakeholders in the creation of a green city identity that effectively promotes sustainable urban living and an eco-friendly culture.

This study provides theoretical insights into the relationship between green identification and residents’ willingness and commitment to act as ambassadors and promote their city’s green values.

The purpose of this paper is to show how shape analysis and quantitative characterization of fiber cross sections, with the aid of image analysis techniques, provide a quick, powerful approach to automated profiled fiber identification.

In this paper, an effective method of cross‐sectional shape characterization for profiled fiber identification is reported with extraction of the distance fluctuation curve of fiber cross‐sectional boundary to the centroid. By calculating their cross‐correlations using signal processing techniques, the authors tackle the problem of calibrating the starting points of fiber objects orientated arbitrarily in image successfully, which are difficult to deal with by means of image processing, to finish the normalization of distance fluctuation curves. For two fiber cross‐sections, the similarity degree of their boundary fluctuation curves normalized can effectively reflect the similarity degree of themselves.

Based on this, the method presented extracts the curves of all fiber cross‐sections in one sample, compares the similarity degrees between each other, and creates clusters to identify profiled fiber.

Experimental results validate that this curve can effectively characterize profiled fiber cross‐sectional contour for profiled fiber identification and the normalization method is feasible.

Focuses on the inverse problems arising from the simulation of forming processes. Considers two sets of problems: parameter identification and shape optimization. Both are solved using an optimization method for the minimization of a suitable objective function. The convergence and convergence rate of the method depend on the accuracy of the derivatives of this function. The sensitivity analysis is based on a discrete approach, e.g. the differentiation of the discrete problem equations. Describes the method for non‐linear, non‐steady‐state‐forming problems involving contact evolution. First, it is applied to the parameter identification and to the torsion test. It shows good convergence properties and proves to be very efficient for the identification of the material behaviour. Then, it is applied to the tool shape optimization in forging for a two‐step process. A few iterations of the inverse method make it possible to suggest a suitable shape for the preforming tools.