Toward a General Theory of Client Acceptance and Continuance Decisions

This study investigates the theoretical perspectives used in the extant auditing literature on client acceptance and continuance decisions. First, the different client acceptance and continuance decisions are presented. Second, the paper identifies and discusses the single-client approach and introduces “the auditor-client relationship life cycle” as an integrative framework for this perspective commonly used in academia. Third, the audit firm portfolio management perspective is examined. Finally, the apparent incommensurability of the single-client and the clients-portfolio approaches is discussed and the foundation for a general theory of the client acceptance and continuance decisions is developed

Ontology-based collaborative framework for disaster recovery scenarios

This paper aims at designing of adaptive framework for supporting collaborative work of different actors in public safety and disaster recovery missions. In such scenarios, firemen and robots interact to each other to reach a common goal; firemen team is equipped with smart devices and robots team is supplied with communication technologies, and should carry on specific tasks. Here, reliable connection is mandatory to ensure the interaction between actors. But wireless access network and communication resources are vulnerable in the event of a sudden unexpected change in the environment. Also, the continuous change in the mission requirements such as inclusion/exclusion of new actor, changing the actor's priority and the limitations of smart devices need to be monitored. To perform dynamically in such case, the presented framework is based on a generic multi-level modeling approach that ensures adaptation handled by semantic modeling. Automated self-configuration is driven by rule-based reconfiguration policies through ontology

Gauge Invariant Framework for Shape Analysis of Surfaces

This paper describes a novel framework for computing geodesic paths in shape spaces of spherical surfaces under an elastic Riemannian metric. The novelty lies in defining this Riemannian metric directly on the quotient (shape) space, rather than inheriting it from pre-shape space, and using it to formulate a path energy that measures only the normal components of velocities along the path. In other words, this paper defines and solves for geodesics directly on the shape space and avoids complications resulting from the quotient operation. This comprehensive framework is invariant to arbitrary parameterizations of surfaces along paths, a phenomenon termed as gauge invariance. Additionally, this paper makes a link between different elastic metrics used in the computer science literature on one hand, and the mathematical literature on the other hand, and provides a geometrical interpretation of the terms involved. Examples using real and simulated 3D objects are provided to help illustrate the main ideas.Comment: 15 pages, 11 Figures, to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence in a better resolutio

INVESTIGATION OF THE MECHANICAL PROPERTIES OF POLY (ETHYLENE GLYCOL) DIACRYLATE BY NANOINDENTATION USING ATOMIC FORCE MICROSCOPY

Poly (ethylene glycol) (PEG) hydrogel based polymers are among the most widely used synthetic materials for biomedical applications. Because of their biocompatibility, and ease of fabrication, hydrogels are highly suitable for use as constructs to engineer tissues as well as for cell transplantation. A critical parameter of importance for PEG hydrogels is their mechanical properties which are highly dependent on the environmental conditions. Properties of PEG-based hydrogels can be engineered to resemble scaffolds composed of extracellular matrix molecules, which provide structural support, adhesive sites and mechanical as well as biomechanical signals to most cells. The mechanical properties of these synthetic scaffolds can affect the migration, proliferation and differentiation of the cells. Accordingly, it is important to investigate the mechanical properties of these hydrogels and observe their effect on cell behavior as PEG-based scaffolds for example. In this research, the objective is to measure the mechanical properties such as the elastic modulus (Ec) and the stiffness (S) of polyethylene glycol diacrylate (PEGDA) hydrogel matrices at the nanoscale. The effect of varying parameters in the fabrication of PEGDA hydrogels including monomer molecular weight, initiator concentration and rates of hydration were investigated via nanoindentation using an atomic force microscope (AFM). Two different silicon nitride based cantilevers were used to study the effect of varying loading rates on the mechanical properties of these materials. Indentation parameters such as loads applied and indent depths were varied for each hydrogel sample. Different models were used to fit the experimental data to obtain the parameters of interest for the material (Ec and S). In particular, the data was best described using the model of Oliver-Pharr to analyze and fit the nanoindentation curves. Scanning electron microscope was used to image and confirm the geometry of the tip before and after the indentation experiments. Under high load and displacement modes, the indentation analysis was relatively easy and the elastic modulus and stiffness values were obtained for all dry PEGDA hydrogel sample. The variation of the initiator concentration has been analyzed as well. The mechanical properties of the hydrogel increase as the amount of the initiator increase in the precursor. The degree of hydration dramatically affects the mechanical behavior of the PEGDA. The presence of water within the hydrogel network weakens the internal as well the external mechanical properties, leading to smaller values of elastic modulus and stiffness compared with the dry condition. The mechanical properties of the indenter (cantilever tips) have significant impact on the results. It is important to study carefully the indenter properties before and after the indentation experiments. Since little work has been done on investigating the mechanical properties of PEGDA hydrogels at the nanoscale via AFM, the analysis of the mechanical behavior of this type of hydrogel using this strategy is of great importance

Optimal Level of Participatory Approach in an NGO Development Project

Many authors, Bradley (2006), Banerjee (2007), Mohan (2008) and Sen (1999) among others, argue that the participatory approach in the development projects of a non-governmental organization, NGO, is more effective and sustainable than the externally imposed expert-driven approach. According to this research stream, the participatory approach promotes self-respect, dignity, inclusiveness, and empowerment of people involved in the project and, simultaneously, it improves the external local environment for the NGO. The key point of this paper is that adopting only the participatory approach may not be optimal, as this approach involves costs to learn about local culture, values and attitudes, and to design and implement feasible participatory development practices. Accordingly, an economically sensible and sustainable strategy for the NGO will be to use a mixture of both approaches. In this paper, the optimal level of participatory approach is theoretically derived and numerically illustrated

Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review

In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall

Rôle d'une base de connaissance dans SemIoTics, un système autonome contrôlant un appartement connecté

National audienceL'Internet des Objets représente une réalité de plus en plus concrète au fur et à mesure que se déploient de larges réseaux d'objets connectés. Ceux-ci ouvrent de larges perspectives d'applications, mais rencontrent des difficultés en terme d'interopérabilité, de configuration ou de passage à l'échelle. Ces probléma-tiques peuvent être traitées par le recours aux principes du web de données liées, d'où l'émergence d'ontologies dédiées aux applications de l'IoT, comme IoT-O, une ontologie pour l'IoT.Par ailleurs, une description en-richie des systèmes permet d'envisager leur configuration autonome : on parle alors d'autonomic computing. Ce papier présente SemIoTics, un système autonome reposant sur des bases de connaissance pour la gestion d'un appartement connecté. Nous présentons tout d'abord une vision générique d'une architecture de réseaux d'objets connectés qui permet de guider une analyse des travaux à l'interface du web sémantique et de l'IoT. Nous décrivons ensuite les deux bases de connaissances spécialisant IoT-O sur lesquelles s'appuie SemIoTics, et leur relation avec le dispositif expérimental. Enfin, la structure de ce système autonome de domotique est présenté en détails, et mis en relation avec l'architecture identifiée dans l'état de l'art

Self-Adaptive Communication for Collaborative Mobile Entities in ERCMS

International audienceAdaptation of communication is required for maintaining the connectivity and the quality of communication in group-wide collaborative activities. This becomes challenging to handle when considering mobile entities in a wireless environment, requiring responsiveness and availability of the communication system. We address these challenges in the context of the ROSACE project where mobile ground and flying robots have to collaborate with each other and with remote human and artificial actors to save and rescue in case of disasters such as forest fires. This paper aims to expose a communication component architecture allowing to manage a cooperative adaptation which is aware of the activity and resource context into pervasive environment. This allows to provide the appropriate adaptation of the activity in response to evolutions of the activity requirements and the changes in relation with the communication resource constraints. In this paper, we present a simulation of a ROSACE use case. The results show how ROSACE entities collaborate to maintain the connectivity and to enhance the quality of communications

Molecular cloning and in silico analysis of three somatic embryogenesis receptor kinase mRNA from date palm

We report here the isolation and characterizations of three somatic embryogenesis receptor kinase (PhSERK) genes from palm date by a rapid amplification of cDNA ends (RACE) approach. PhSERKs belong to a small family of receptor kinase genes, share a conserved structure and extensive sequence homology with previously reported plant SERK genes. Sequence analysis of these genes revealed the sequence size of 11051 pb (PhSERK1), 7981 pb (PhSERK2) and 10510 pb (PhSERK3). The open reading frames of PhSERK1, PhSERK2 and PhSERK3 are 1914 pb, 1797 pb and 1719 pb respectively. PhSERKs belongs to the LRR-type cell surface RLKs, which possess a number of characteristic domains. These include an extracellular domain (EX) containing a variable number of LRR units, signal pepetide (SP) immediately followed by a single transmembrane domain (TM) and an intracellular kinase domain. The phylogenetic tree shows that the protein PhSERK1, PhSERK2 and PhSERK3 clustered within monocots SERKs proteins groups. We also predicted the secondary and tertiary with ligand binding sites structure of the protein PhSERKs