PROFESSIONAL COMPETENCE FOR USING ACOUNTING ESTIMATES IN CORPORATE FINANCIAL STATEMENTS

The IAS adopted in Bulgaria following its accession to the European Union introduced some new terms in the field of accounting, such as “accounting policy”, “accounting estimate”, and “accounting error”. Generally, the indigenous scepticism of professional accountants is an obstacle to their accepting the new knowledge and points of view and subsequently applying them in practice. The aim of this paper is to investigate the possibilities of unifying the professional competence of accountants and auditors in terms of using accounting estimates in financial statements of non-financial enterprises in compliance with the accounting legislation in force. In order to achieve this aim, the author has tried to outline the different meanings attributed to this term by accountants and auditors in related standards

Stochastic Filtering on Shape Manifolds

This thesis addresses the problem of learning the dynamics of deforming objects in image time series. In many biomedical imaging and computer vision applications it is important to satisfy certain geometric constraints which traditional time series methods are not capable of handling. We focus on building topology-preserving spatio-temporal stochastic models for shape deformation, which we combine with the observed images to obtain robust object tracking. The shape of the object is modeled as obtained through the action of a group of diffeomorphisms on the initial object boundary. We formulate a state space model for the diffeomorphic deformation of the object, and implement a particle filter on this shape space to estimate the state of the shape in each video frame. We use a practical method for sampling diffeomorphic shapes in which we generate deformations via flows of finitely generated vector fields. Based on the observations and the proposed samples we obtain an approximate estimate for the posterior distribution of the shape. We present the performance of this framework on various image sequences under different scenarios. We extend the random perturbation models to diffusion models on the manifold of planar (discretized) shapes whose drift component represents a trend in the shape deformation. To obtain trends intrinsic to the shape, we define the drift as a gradient of appropriate functions defined over the boundary of the shape. Given a sequence of observations from the path of the suggested stochastic differential equations, we propose a likelihood-ratio-based technique to estimate the missing parameters in the drift terms. We show how to reduce the computational burden and improve the robustness of the estimators by constraining the motion of the shapes to a lower-dimensional submanifold equipped with a sub-Riemannian metric. We further discuss how to apply this methodology to obtain estimates when we have only a limited number of observations

Effect of Semantic Priming for Wide Attention Focus on Visuomotor Decision-making Task in Young and Older Non-professional Drivers

The study was aimed at estimating the semantic priming effect for wide attention focus on subjects response time (RT) in a decision-making visuomotor hand movement task (VMHT) performed in traffic scenarios for non-professional driver’s license tests. The priming was based on modified scrambled sentence task. Traffic scenarios were displayed either on whole screen or within the central 25% of the screen. VMHT was presented: 1) immediately after priming; 2) after primed in the same way adaptation of reactive visual saccade; 3) without priming and saccade adaptation. Thirty young and thirty older subjects took part in the study as they were divided equally into three groups with respect to the three conditions: 1) YP and OP (a group where the traffic test is performed before the sacccade adaptation); 2) YS and OS (after the saccade adaptation); 3) YC and OC (control group). The outcomes established that RTs of both YP and OP groups are higher while RTs of YS and OS groups are lower in comparison with the control groups. RTs of all older groups were significantly longer than those of the younger groups. Longer RTs were found with respect to large pictures than to small pictures. These results suggest that semantic priming has a rather negative effect on decision-making VMHT performance because priming with scrambled sentences also involves a decision-making process. Therefore, they both need similar cognitive resources of which probably VMHT is deprived by the preceding priming and as consequence its perceptual realization is delayed. The study suggests that priming and visuomotor performance are related and the effect of the former on the latter depends on which cognitive resources they need

Turkey And Bulgaria Transport

DergiPark: 73187ejovocThe development of transport vehicles and technologies, has been one of the factors that affect deeply the lives of humanity through the ages. The wheel discovered, the discovery of the motor, the invention of the airplane motor, the discovery of the container discovering our world even the universe competence of humanity are the main tools . Transport on the one hand the provision of human needs and desires of the most important means of creating, on the other hand has been replaced in the costs of meeting these needs. Total costs of delivering today due to hold an important place in the transportation costs have increased the importance of competition. Both raw materials and semi-finished materials and finished goods transportation advantage in international trade that countries become more successful, this growth in revenue and a positive impact on the country, the welfare of the people is increasing at the same rate. In this study transport system between Turkey and Bulgaria and what should be done to develop the system are researsche

Detecting reports of unsafe foods in consumer product reviews.

ObjectivesAccess to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com.Materials and methodsWe linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products.ResultsOur data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated.Discussion and conclusionChallenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public

Detecting reports of unsafe foods in consumer product reviews.

Objectives:Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. Materials and Methods:We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. Results:Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. Discussion and Conclusion:Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public