Query: Complex hole-filling algorithm for 3D models

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A signal processing approach to fair surface design <=SEP=> In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals – functions defined on polyhedral surfaces of arbitrary topology –, we reduce the problem of surface smoothing, or fairing, to low-pass filtering. We describe a very simple surface signal low-pass filter algorithm that applies to surfaces of arbitrary topology. As opposed to other existing optimization-based fairing methods, which are computationally more expensive, this is a linear time and space complexity algorithm. With this algorithm, fairing very large surfaces, such as those obtained from volumetric medical data, becomes affordable. By combining this algorithm with surface subdivision methods we obtain a very effective fair surface design technique. We then extend the analysis, and modify the algorithm accordingly, to accommodate different types of constraints. Some constraints can be imposed without any modification of the algorithm, while others require the solution of a small associated linear system of equations. In particular, vertex location constraints, vertex normal constraints, and surface normal discontinuities across curves embedded in the surface, can be imposed with this technique. CR

Filling Holes in Triangular Meshes in Engineering <=SEP=> In this paper, a novel hole-filling algorithm for triangular meshes is proposed. Firstly, the hole is triangulated into a set of new triangles using modified principle of minimum angle. Then the initial patching mesh is refined according to the density of vertices on boundary edges. Finally, the patching mesh is optimized via the bilateral filter to recover missed features. Experimental results demonstrate that the proposed algorithm fills complex holes robustly, and preserves geometric features to a certain extent as well. The resulted meshes are of good quality for engineering.

Polygon mesh repairing: An application perspective <=SEP=> Nowadays, digital 3D models are in widespread and ubiquitous use, and each specific application dealing with 3D geometry has its own quality requirements that restrict the class of acceptable and supported models. This article analyzes typical defects that make a 3D model unsuitable for key application contexts, and surveys existing algorithms that process, repair, and improve its structure, geometry, and topology to make it appropriate to case-by-case requirements.
 The analysis is focused on polygon meshes, which constitute by far the most common 3D object representation. In particular, this article provides a structured overview of mesh repairing techniques from the point of view of the application context. Different types of mesh defects are classified according to the upstream application that produced the mesh, whereas mesh quality requirements are grouped by representative sets of downstream applications where the mesh is to be used. The numerous mesh repair methods that have been proposed during the last two decades are analyzed and classified in terms of their capabilities, properties, and guarantees. Based on these classifications, guidelines can be derived to support the identification of repairing algorithms best-suited to bridge the compatibility gap between the quality provided by the upstream process and the quality required by the downstream applications in a given geometry processing scenario.

An Integrated Approach to Filling Holes in Meshes <=SEP=> Data obtained by scanning 3D models typically contains missing pieces and holes. This paper describes an integrated approach for filling holes in triangular meshes obtained during the process of surface reconstruction, which consists of three steps: triangulation, refinement and smoothing. The approach first generates a minimal triangulation to the hole, and then iteratively subdivides the patching meshes making it assort with the density of surrounding meshes. Finally, geometric optimization is enforced to the patching meshes for improving regularity. The approach can deal with arbitrary holes in oriented connected manifold meshes. Experimental results on several triangular meshes are presented.

Filling Holes in Complex Surfaces using Volumetric Diffusion <=SEP=> We address the problem of building watertight 3D models from surfaces that contain holes − for example, sets of range scans that observe most but not all of a surface. We specifically address situations in which the holes are too geometrically and topologically complex to fill using triangulation algorithms. Our solution begins by constructing a signed distance function, the zero set of which defines the surface. Initially, this function is defined only in the vicinity of observed surfaces. We then apply a diffusion process to extend this function through the volume until its zero set bridges whatever holes may be present. If additional information is available, such as known-empty regions of space inferred from the lines of sight to a 3D scanner, it can be incorporated into the diffusion process. Our algorithm is simple to implement, is guaranteed to produce manifold noninterpenetrating surfaces, and is efficient to run on large datasets because computation is limited to areas near holes.

Hairstyle recommendation system for women <=SEP=> A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.

Role-Based Access Control Model Supporting Regional Division in Smart Grid System <=SEP=> Smart Grid is a modern electric power infrastructure that incorporates elements of traditional power systems and information and communication technology (ICT) with the aim to improve the reliability, efficiency and safety as crucial requirements of critical infrastructure systems. Relying on the ICT, Smart Grid exposes electric power systems to new security issues and vulnerabilities over the network. Therefore, the security is becoming an increasing concern at both physical and information levels. Access controls are one of the most important aspects of information security. Role-based access control (RBAC) model is widely used in complex systems that are characterized by many participants. However, the existing models are not suitable for critical infrastructure systems with specialized features, such as a high number of devices dispersed over the vast geographical regions in “an Internetlike distributed environment”. In this paper, access control mechanism optimized for Smart Grid systems is developed considering the regional division and concept of areas of responsibility (AOR). To this end, the standardized RBAC model will be extended to provide an efficient and consistent policy with greater level of granularity from the aspect of resource protection. The proposed RBACAOR model is formally described and then applied to the smart grid environment.

Polyphenolic content and antioxidant properties of Moringa oleifera leaf extracts and enzymatic activity of liver from goats supplemented with Moringa oleifera leaves/sunflower seed cake. <=SEP=> The study investigated antioxidant potency of Moringa oleifera leaves in different in vitro systems using standard phytochemical methods. The antioxidative effect on the activities of superoxide dismutase (SOD), catalase (CAT), lipid peroxidation (LPO) and reduced glutathione (GSH) were investigated in goats supplemented with M. oleifera (MOL) or sunflower seed cake (SC). The acetone extract had higher concentrations of total flavonoids (295.01 ± 1.89 QE/g) followed by flavonols (132.74 ± 0.83 QE/g), phenolics (120.33 ± 0.76 TE/g) and then proanthocyanidins (32.59 ± 0.50 CE/g) than the aqueous extract. The reducing power of both solvent extracts showed strong antioxidant activity in a concentration dependent manner. The acetone extract depicted higher percentage inhibition against DPPH, ABTS and nitric oxide radicals which were comparable with reference standard antioxidants (vitamin C and BHT). MOL increased the antioxidant activity of GSH (186%), SOD (97.8%) and catalase (0.177%). Lipid peroxidation was significantly reduced by MOL. The present study suggests that M. oleifera could be a potential source of compounds with strong antioxidant potential.

A 10-GS/s 4-Bit Single-Core Digital-to-Analog Converter for Cognitive Ultrawidebands <=SEP=> This brief delineates the design and realization of a 10-GS/s 4-bit digital-to-analog converter (DAC) for the cognitive ultrawideband (CUWB), an emerging solution for low interference and efficient spectrum utilization in communication networks. The DAC serves as the data converter for the adaptive waveform transmitter therein, largely to reduce its power dissipation and hardware complexity. For reasons of low power dissipation and low-cost CUWB application, the resolution of the DAC is 4 bits, its realization is in standard 65-nm CMOS, and the architecture is a single core. The binary current-steering DAC includes critical building blocks such as current sources and a novel deglitcher circuit. The current sources are designed for small area with high linearity based on our derived relationship between current-source output resistance and linearity parameters [integral nonlinearity (INL) and spurious-free dynamic range (SFDR)]. The deglitcher design includes high-speed source followers as high-speed low voltage swing buffers to improve the linearity by decreasing the output glitch energy. The DAC embodies an in situ hardware efficient (small integrated-circuit area and reduced input/output pinout) tester that generates 4 × 10-Gb/s test-data pattern to facilitate functional verification. The designed DAC achieves ≤ 0.16-least significant bit INL/differential nonlinearity and > 23-dBc SFDR over the Nyquist bandwidth up to 4.53 GHz, and features the most competitive figures-of-merit of all similar DACs reported to date.

Inferring causal impact using Bayesian structural time-series models <=SEP=> An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must determine the incremental contributions that different advertising campaigns have made to web searches, product installs, or sales. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on synthetic data. We then demonstrate its practical utility by evaluating the effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of our approach in improving the accuracy of causal attribution, power analyses, and principled budget allocation.

Understanding the influence of dead-time on GaN based synchronous boost converter <=SEP=> Gallium Nitride (GaN) based power switching devices are known to be superior to conventional Si devices due to properties such as low loss and fast switching speed. However, as a new technology, some special characteristics of GaN-based power devices still remain unknown to the public. This paper tries to investigate the effect of dead-time on a GaN-based synchronous boost converter, as compared to its Si counterpart. It is found out that GaN-based converter is more sensitive to dead-time related loss as a result of fast switching transition and high reverse voltage drop. Improper selection of dead-time value can offset its advantage over Si converter. Analyses also show that GaN HEMTs have different switching characteristics compared to Si MOSFET due to low Cgd to Cds ratio and lack of reverse recovery. These critical findings will help power electronic engineers take better advantage of GaN technology in synchronous rectification and inverter applications.

Clonogenic assay of cells in vitro <=SEP=> Clonogenic assay or colony formation assay is an in vitro cell survival assay based on the ability of a single cell to grow into a colony. The colony is defined to consist of at least 50 cells. The assay essentially tests every cell in the population for its ability to undergo “unlimited” division. Clonogenic assay is the method of choice to determine cell reproductive death after treatment with ionizing radiation, but can also be used to determine the effectiveness of other cytotoxic agents. Only a fraction of seeded cells retains the capacity to produce colonies. Before or after treatment, cells are seeded out in appropriate dilutions to form colonies in 1–3 weeks. Colonies are fixed with glutaraldehyde (6.0% v/v), stained with crystal violet (0.5% w/v) and counted using a stereomicroscope. A method for the analysis of radiation dose–survival curves is included.

Routing and Wavelength Assignment in Optical WDM Networks <=SEP=> This article discusses the routing and wavelength assignment (RWA) problem in optical networks employing wavelenength division multiplexing (WDM) technology. Two variants of the problem are studied: static RWA, whereby the traffic requirements are known in advance, and dynamic RWA in which connection requests arrive in some random fashion. Both point-topoint and multicast traffic demands are considered.

Animations 25 Years Later: New Roles and Opportunities <=SEP=> Animations are commonplace in today's user interfaces. From bouncing icons that catch attention, to transitions helping with orientation, to tutorials, animations can serve numerous purposes. We revisit Baecker and Small's pioneering work Animation at the Interface, 25 years later. We reviewed academic publications and commercial systems, and interviewed 20 professionals of various backgrounds. Our insights led to an expanded set of roles played by animation in interfaces today for keeping in context, teaching, improving user experience, data encoding and visual discourse. We illustrate each role with examples from practice and research, discuss evaluation methods and point to opportunities for future research. This expanded description of roles aims at inspiring the HCI research community to find novel uses of animation, guide them towards evaluation and spark further research.

The knowledge pyramid: a critique of the DIKW hierarchy <=SEP=> The paper evaluates the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy. This hierarchy is part of the canon of information science and management. The paper considers whether the hierarchy, also known as the ‘Knowledge Hierarchy’, is a useful and intellectually desirable construct to introduce, whether the views expressed about DIKW are true and have evidence in favour of them, and whether there are good reasons offered or sound assumptions made about DIKW. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error that DIKW makes. The paper identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a semantically and pragmatically sound way, that information is what is known in other literature as ‘weak knowledge’, that knowledge also is ‘weak knowledge’ and that wisdom is the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge.

Testing of Digital Systems <=SEP=> Device testing represents the single largest manufacturing expense in the semiconductor industry, costing over $40 million a year. The most comprehensive and wide ranging book of its kind, Testing of Digital Systems covers everything you need to know about this vitally important subject. Starting right from the basics, the authors take the reader through automatic test pattern generation, design for testability and built-in self-test of digital circuits before moving on to more advanced topics such as IDDQ testing, functional testing, delay fault testing, memory testing, and fault diagnosis. The book includes detailed treatment of the latest techniques including test generation for various fault modes, discussion of testing techniques at different levels of integrated circuit hierarchy and a chapter on system-on-a-chip test synthesis. Written for students and engineers, it is both an excellent senior/graduate level textbook and a valuable reference.

Understanding the Role of Social presence in Crowdfunding: Evidence from Leading U.S. And German Platforms <=SEP=> As a novel opportunity to acquire capital from the masses, crowdfunding has attracted great attention in academia and practice. So far, little is known about the factors that promote the success of crowdfunding projects, however. In this paper, we examine in how far the social presence perceived on a project’s website influences the success of the respective crowdfunding project. Based on a data-driven analysis of 2.000 project websites from the largest crowdfunding platforms in the U.S. and Germany, we show that the perceived social presence has a significant influence on the success of crowdfunding projects. The obtained results indicate that using socially rich pictures and a socially rich description in the project presentation positively affects the success of a crowdfunding project. A socially rich profile page of the founder(s) in contrast appears to have a rather limited effect. The success of crowdfunding projects seems to be dependent on the participation behavior of the founder, however. Our results indicate that having backed other projects positively influences the success of one’s own initiative. The number of answered comments might have a negative effect on the success of the initiative, though.

Zero-Shot Learning Posed as a Missing Data Problem <=SEP=> This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way - our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. Following the transductive setting, we leverage unlabeled data to refine the initial estimation. In experiments, our method achieves the highest classification accuracies on two popular datasets, namely, 96.00% on AwA and 60.24% on CUB.

Relationship between Variants of One-Class Nearest Neighbors and Creating Their Accurate Ensembles <=SEP=> In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study one-class nearest neighbor (OCNN) classifiers and their different variants. We present a theoretical analysis to show the relationships among different variants of OCNN that may use different neighbors or thresholds to identify unseen examples of the non-target class. We also present a method based on inter-quartile range for optimizing parameters used in OCNN in the absence of non-target data during training. Then, we propose two ensemble approaches based on random subspace and random projection methods to create accurate OCNN ensembles. We tested the proposed methods on 15 benchmark and real world domain-specific datasets and show that random-projection ensembles of OCNN perform best.

Analysis of Deep Convolutional Neural Network Architectures <=SEP=> In computer vision many tasks are solved using machine learning. In the past few years, state of the art results in computer vision have been achieved using deep learning. Deeper machine learning architectures are better capable in handling complex recognition tasks, compared to previous more shallow models. Many architectures for computer vision make use of convolutional neural networks which were modeled after the visual cortex. Currently deep convolutional neural networks are the state of the art in computer vision. Through a literature survey an overview is given of the components in deep convolutional neural networks. The role and design decisions for each of the components is presented, and the difficulties involved in training deep neural networks are given. To improve deep learning architectures an analysis is given of the activation values in four different architectures using various activation functions. Current state of the art classifiers use dropout, max-pooling as well as the maxout activation function. New components may further improve the architecture by providing a better solution for the diminishing gradient problem.

Bus detection device for the blind using RFID application <=SEP=> This paper outlines a bus detection mechanism for the blind in travelling from one place to another. In order to get transportation independently, the blind use auditory touched clues like walking stick or white cane. The limitation of the walking stick is that a blind person must come into close proximity with their surroundings to determine the location of an obstacle. For that basis, various devices have been developed such as the Sonicguide, the Mowat sensor, the Laser cane and the Navbelt [4]. However, these device can only assist the blind at a pedestrian crossing. Therefore, the project is aims to develop a bus detection prototype using Radio Frequency Identification (RFID) for the blind. The paper covers brief idea about the blind and RFID system, review relevant papers and summary of current research. The review of RFID system compare between families of auto-ID technology, the basic principle of RFID, the type of RFID tagging and the antenna characteristic. The summary of current research discussed about the development of prototype, the database system, the output mechanism and integration between hardware and software. Database management will provided. The information such as bus route, final destination and bus number are also provided. This paper also describes the future work intended to be done.

Association rules: Normalizing the lift <=SEP=> Association rules is a popular data mining technique for discovering relations between variables in large amounts of data. Support, confidence and lift are three of the most common measures for evaluating the usefulness of these rules. A concern with the lift measure is that it can only compare items within a transaction set. The main contribution of this paper is to develop a formula for normalizing the lift, as this will allow valid comparisons between distinct transaction sets. Traffic accident data was used to validate the revised formula for lift and the result of this analysis was very strong.

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model <=SEP=> Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

Effectiveness of domestic wastewater treatment using microbial fuel cells at ambient and mesophilic temperatures. <=SEP=> Domestic wastewater treatment was examined under two different temperature (23+/-3 degrees C and 30+/-1 degrees C) and flow modes (fed-batch and continuous) using single-chamber air-cathode microbial fuel cells (MFCs). Temperature was an important parameter for treatment efficiency and power generation. The highest power density of 422 mW/m(2) (12.8 W/m(3)) was achieved under continuous flow and mesophilic conditions, at an organic loading rate of 54 g COD/L-d, achieving 25.8% COD removal. Energy recovery was found to depend significantly on the operational conditions (flow mode, temperature, organic loading rate, and HRT) as well as the reactor architecture. The results demonstrate that the main advantages of using temperature-phased, in-series MFC configurations for domestic wastewater treatment are power savings, low solids production, and higher treatment efficiency.

[Botulinum toxin injection techniques in the lower third and middle of the face, the neck and the décolleté: the "Nefertiti lift"]. <=SEP=> Although correction of the dynamic wrinkles of the upper part of the face is the major indication for botulinum toxin, there are also many possibilities for the middle and lower thirds of the face and neck. However, these injections are more delicate and require an experienced operator who has excellent knowledge of the muscles of these regions, their functions, the antagonist actions exercised on other muscles, particularly in terms of the complex equilibrium of the mouth. An excessive dose, an inappropriate injection point, or a centering mistake can all easily be responsible for undesirable side effects. However, the results obtained, often with lower doses than in the superior part of the face, can be highly satisfactory, notably in erasing bunny lines, improvement of marionette lines, peau d'orange chin, attenuation of peribuccal lines, melomental folds, correction of a gummy smile, and facial asymmetries. In the neck it is possible to reduce platysmal bands, horizontal lines, and diagonal lines of the neck and décolleté. The face contours can also be improved by the Nefertiti lift. In the mid and lower regions of the face, botulinum toxin is often a complement to other esthetic techniques, particularly filling procedures.

changepoint: An R Package for Changepoint Analysis <=SEP=> One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular emphasis is placed on the PELT algorithm and how results differ from the binary segmentation approach.

Guarding Against the Erosion of Competitive Advantage: A Knowledge Leakage Mitigation Model <=SEP=> A critical objective of knowledge-intensive organizations is to prevent erosion of their competitive knowledge base through leakage. Our review of the literature highlights the need for a more refined conceptualization of perceived leakage risk. We propose a Knowledge Leakage Mitigation (KLM) model to explain the incongruity between perceived high-risk of leakage and lack of protective actions. We argue that an organization’s perceived risk of leakage increases if competitors can benefit from leakage incidents. Further, perceived leakage risk decreases if the organization is shielded from impact due to their diversity of knowledge assets and their ability to reconfigure knowledge resources to refresh their competitive knowledge base. We describe our approach to the design of a large-scale survey instrument that has been tested and refined in two stakeholder communities: 1) knowledge managers responsible for organizational strategy, and 2) Information security management consultants.

Eight-Band Antenna With A Small Ground Clearance for LTE Metal-Frame Mobile Phone Applications <=SEP=> This letter presents a compact eight-band monopole antenna whose size is 70 mm × 7 mm × 6 mm for LTE metal-frame mobile phones. The mainly radiating part of the proposed antenna is the metal frame. There are three gaps with the same size of 1 mm in the frame, and these gaps divide the antenna into two parts. Besides, a match circuit is used to enlarge the bandwidth and make the lower band matched. The advantage of the proposed antenna is that eight bands can be covered with a metal frame under the condition of only a 7 mm ground clearance. A prototype is manufactured and tested. The measured –6 dB impedance bandwidths are 375 MHz (0.675–1.05 GHz) and 1.2 GHz (1.6–2.8 GHz). The LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300, and LTE2500 bands are covered. The measured efficiencies are 52.7%–78.7% at the lower band and 45.6%–81% at the higher band. The measured gains are 0–2.1 dBi at the lower band and 1.6–3.6 dBi at the higher band.

Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets <=SEP=> The nonlinear characteristics of a photovoltaic (PV) array, maximum power point tracking of the PV inverter, presence of blocking diodes, and lower irradiation prevent the conventional protection devices to trip under certain faults and lead to reduced system efficiency and fire hazards. Moreover, the PV characteristics under certain partial shading conditions are similar to that under fault conditions. Hence, it is imperative to detect faults and differentiate faults from the partial shading condition to avoid false tripping of the system. This paper proposes a simple fault detection method using the available data of array voltage and current by means of wavelet packets. The proposed scheme is simulated using MATLAB/Simulink and is experimentally tested on a 1.6-kW $\text{4}\,\times \,\text{4}$ PV array to validate its performance.

mTOR Signaling in Growth Control and Disease <=SEP=> The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.