CONSIDERATIONS ON THE PLANNING OF THE FINANCIAL AUDIT MISSION

In order to exercise efficiently the financial audit it is required to adequately plan it, for each individual mission. This document synthesizes the essential aspects which must be taken into account by the auditors and divides them into groups, according to the following paths of action: obtaining a detailed knowledge of the entity audited; estimating an acceptable level of the audit risk; understanding the internal control system of the entity; determining rigorously of the terms of the mission.audit, planning, particularities, risk, engagement

Experimental Research for Operating Thermal Electric Plant Ash in Coating Powder Production Used in the Continuous Steel Casting Lines

In this paper the authors present obtaining of a technological powder used for the continuous casting of steel. The powder is applied in the casting plant distributor to ensure thermal insulation, prevent oxidation of the steel and especially to capture the steel inclusions. The powder helps to purify the steel and thus improve its quality. The paper presents the physical and chemical characteristics of the power plant ash, which is a fine-grained waste, stored in dumps and which can be used for the production of coating powders. Several experimental recipes were made, in which the proportion of power plant ash was between 65-80%. To characterize the recipes, measurements were made to determine the humidity, volumetric mass, spread area, particle size analysis, melting temperature and chemical analysis. After analyzing the physical, chemical and thermal characteristics, pilot experimental batches were performed for testing in the steelworks. The favourable effects of the use of Cenoterm powder were highlighted by analyzing the slag samples taken from the experimental batches. These showed an increase in the MnO content of steel up to 30%, an increase in alumina, magnesium and a decrease in silica. They show that the molten powders in the formed slag are reactive and play a beneficial role in the quality of the steel. Consumption of ash-based coating powder was between 0.065 and 0.22 kg/t liquid steel

Spark versus Flink: Understanding Performance in Big Data Analytics Frameworks

International audienceBig Data analytics has recently gained increasing popularity as a tool to process large amounts of data on-demand. Spark and Flink are two Apache-hosted data analytics frameworks that facilitate the development of multi-step data pipelines using directly acyclic graph patterns. Making the most out of these frameworks is challenging because efficient executions strongly rely on complex parameter configurations and on an in-depth understanding of the underlying architectural choices. Although extensive research has been devoted to improving and evaluating the performance of such analytics frameworks, most of them benchmark the platforms against Hadoop, as a baseline, a rather unfair comparison considering the fundamentally different design principles. This paper aims to bring some justice in this respect, by directly evaluating the performance of Spark and Flink. Our goal is to identify and explain the impact of the different architectural choices and the parameter configurations on the perceived end-to-end performance. To this end, we develop a methodology for correlating the parameter settings and the operators execution plan with the resource usage. We use this methodology to dissect the performance of Spark and Flink with several representative batch and iterative workloads on up to 100 nodes. Our key finding is that there none of the two framework outperforms the other for all data types, sizes and job patterns. This paper performs a fine characterization of the cases when each framework is superior, and we highlight how this performance correlates to operators, to resource usage and to the specifics of the internal framework design

Toward a Push-based Stream Programming Model with AIMSS: An Active In-Memory Storage System Approach

Today's passive (on-disk and/or in-memory, employing a pull-based data access approach) storage architectures are performance- and energy- insufficient for handling the data-intensive demands of tomorrow's exascale machine learning and artificial intelligence (ML/AI) workloads. Industry projections forecast beyond-exascale clusters consuming energy between 500 MW and 1 TW, highlighting the need for a paradigm shift in data movement and processing, necessitating novel solutions that can improve performance, reduce energy consumption, and simplify application development and deployment. We believe exascale computing will require in-memory storage systems with a global perspective on I/O and processing, strategically positioned between traditional disk-based storage systems and CPU-GPU compute engines. We present the vision for an Active In-Memory Storage System (AIMSS), a novel architecture that shifts data movement management, such as source/sink handling and data shuffling, from ML/AI applications and big data streaming engines, directly to AIMSS. Operating on a log-structured in-memory storage framework, leveraging immutable data access patterns, and facilitating efficient real-time data movement, the AIMSS architecture will be deployed on tens of thousands of large many-core CPU-GPU nodes, harnessing their memory and ensuring efficient and transparent communication with traditional disk-based file storage systems. We propose a push-based streaming execution model enabling AIMSS to cost-effectively harness application-specific data (such as consumer/producer offsets and data access patterns including read, write, and shuffle) and thereby enable a set of optimizations such as scalable data movement partitioning algorithms, faster stream storage recovery, mitigation of application stragglers, mitigating power fluctuation issues during large-scale ML/AI training by efficiently leveraging idle GPU resources for other computing tasks, and minimizing I/O interference in multi-CPU-GPU setups for multiple applications sharing an exascale high-performance computing infrastructure. Through its global view of I/O enabled by a push-based in-memory computing approach, AIMSS promises significant performance improvements for data-intensive applications by actively handling data movement, while eliminating the need for manual tuning and inefficient application-based data management

DIOXINS / FURANS (PCDDs/PCDFs) CONTROL IN CEMENT INDUSTRY IN ROMANIA

The most important sources of dioxins are waste incineration, industrial (dioxins are by-products), volcanic eruptions, fire, etc. The Romanian law 278/2013 provides the air emissions limit of 0.1 ng/Nm3 gases for dioxins and furans emitted by the incineration and co-incineration plants. Some aspects regarding the control of these substances in the gases from a cement factory in Romania are presented in this paper. In incineration plant were used various fuels. However, the substances level mentioned above was within the allowed limits