Seven

Energy efficiency is a central element of the European Union’s approach to achieving its climate and energy objectives, underscoring the need for analytical tools that can support the assessment of future energy efficiency policies and trends. This paper applies the ODEX methodology within a combined ex-post and ex-ante framework to examine projected developments in energy efficiency across sectors. By extending ODEX, traditionally used for retrospective monitoring, to forward-looking national scenarios, the study provides a consistent basis for comparing historical progress with anticipated future improvements. The approach is illustrated using two EU Member States, demonstrating its broader applicability for assessing national strategies and identifying where additional measures may be necessary to meet future final energy consumption targets. The findings reflect patterns reported in recent literature, showing substantial differences in expected efficiency gains across sectors and highlighting areas where progress may need to accelerate to remain in line with EU ambitions. The results also indicate how a top-down, consumption-weighted index such as ODEX can complement existing evaluation practices by offering a transparent way to interpret efficiency trends and by supporting preliminary assessments of energy efficiency policy impacts. While the approach does not replace detailed modelling or bottom-up evaluation methods, it provides a reproducible and data-efficient tool that can assist with cross-country benchmarking, policy evaluation, and ongoing monitoring under the Energy Efficiency Directive. By linking observed trends (ex-post) with forward-looking (ex-ante) scenarios, the framework may contribute to strengthening national and EU-level discussions on future energy efficiency pathways.

Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras. A time resolution of less than 1ms eventually allows for the true localization of the initial and subsequent sound events as well as a clear separation of direct from reflected sound. The measurement instrumentation uses a rotating linear array of a few digital microphones which granularly scans the incident sound field on a circular area. The proposed method for computing an acoustic image is based on compensating the moving microphone signals for Doppler distortions and evaluating the coherence of the resulting signals with a non-moving reference microphone for each point in the acoustic image. The methodology is evaluated in the context of localizing the periodic engage event of an electromechanical clutch and the clicking noise of the aluminium frame of a passenger car seat under periodic excitation.

Energy efficiency is crucial for sustainable energy transitions, supporting global climate and energy goals. This paper presents a standardized framework for top-down assessment of future energy efficiency policies using the ODEX methodology, addressing critical gaps in evaluating and comparing policy impacts across Europe. By harmonizing key data on energy consumption, policy measures, and target alignment, the framework ensures consistency, reliability, and comparability, fostering more effective strategies. A key innovation is integrating ex-ante (forecasting) and ex-post (retrospective) evaluations into a single framework. This dual approach illuminates both past performance and future impacts, providing insights into drivers of energy savings and the alignment of national efforts with broader objectives. It also underscores how policy choices and market factors interrelate, informing strategic decisions and refining policy design across the European Union. The paper outlines the methodology’s core components, including recent advancements in the ODEX approach, primary data sources, and practical applications in selected EU countries, such as Croatia and Slovenia. These case studies illustrate the framework’s capacity to detect inefficiencies, identify emerging trends, and offer actionable recommendations for policy improvement. In addition, the research highlights the framework’s scalability and potential replication across diverse national contexts and at the EU level. By bridging the gap between historical performance and future planning, this robust, scalable methodology addresses the urgent need for reliable tools to evaluate and monitor energy efficiency policies. Ultimately, it supports the development of harmonized, evidence-based approaches, bolstering national capacities to meet ambitious energy efficiency targets and advancing broader climate and sustainability objectives.

Elemental impurities are substances present in drug products, excipients, or drug formulations. They may be formed by the presence of catalysts and environmental contaminants. Elemental impurities can be detected by a sophisticated method such as Inductively Coupled Plasma Mass Spectrometry (ICP–MS). ICP-MS is an advanced method to detect elemental impurities in drug substances. In this study Propafenone hydrochloride drug was used, Propafenone Hydrochloride is an antiarrhythmic medication belonging to class 1C used to prevent supraventricular and ventricular arrhythmias. The present study was aimed to develop and validate inductively coupled plasma mass spectroscopic (ICP–MS) method for detection of elemental contaminants, i.e., Class 1, Cd, Pb, As, Class 2A, Hg, Co, V, and Class 2B impurities such as Ni, T1, Se, Ag, Au, Pd, Ir, Os, Rh, Ru, and Pt. Total 17 elemental impurities were detected in Propafenone Hydrochloride and this method was employed for the regular sample analysis of 17 elemental impurities in Propafenone Hydrochloride for pharmaceutical use. The instrument conditions were set using RF power of 1550 W, auxiliary gas of 0.5 L/min, and nebulizer flow of 1.01 L/min nebulizer pump pressure was 0.10 rps, spray chamber temperature was 2°C, and mode used was He, He flow rate was 4.3 mL/min and the energy discrimination rate was 3.0 V. The technique is sensitive and may identify desirable elemental impurities within permissible regulatory limits when additional elements are present. The proposed ICP-MS approach has been found to be accurate, precise, linear, rugged, robust, and convenient for the quality control of the drug substance propafenone hydrochloride. The linearity results for each impurity were 0.9990. The methods were validated according to USP requirements and International Council for Harmonization ICH guidelines. The suggested approach is an excellent quality control tool for the concurrent quantitative assessment and detection of elemental contaminants at low levels in the drug substance propafenone hydrochloride.

Neural machine translation falls into the category of natural language processing tasks. Despite the availability of a big number of research papers that are devoted to the improvement of the quality of the machine translation of documents, the problem of the translation of the spoken language that contains the elements of the disfluency speech is still an actual task, especially for low-resource languages like the Ukrainian language. In this paper, the problem of the neural machine translation of the transcription results of the spoken language that incorporate different elements of the disfluency speech has been considered in the case of the translation from the English language to the Ukrainian language. Different methods and software libraries for the detection of the elements of disfluency speech in English texts have been analyzed. Due to the lack of open-access corpora of the speech disfluency samples, a new synthetic labeled corpus has been created. The created corpus contains both the original version of a document and its modified version according to the different types of speech disfluency: filler words (uh, ah, etc.) and phrases (you know, I mean), reparandum-repair pairs (cases when a speaker corrects himself during the speech). The experimental verification of the effectiveness of the usage of the method of disfluency speech detection for the improvement of the machine translation of the spoken language has been performed for the pair of English and Ukrainian languages. It has been shown that the current state-of-the-art neural translation models cannot produce the appropriate translation of the elements of speech disfluency, especially, in the reparandum-repair cases. The results obtained may indicate that the mentioned method of disfluency speech detection can be used for the previous processing of the transcriptions of spoken dialogues for the creation of coherent translations by the usage of the different models of neural machine translation.