1. Analysis of the Effect of Eco-Efficiency on Asset Return in Food and Beverage Manufacturing Companies Listed at the Johannesburg Stock ExchangeDimakatso Hellen Malapa, Collins C. Ngwakwe, 2025, izvirni znanstveni članek Opis: This article examines the effect of eco-efficiency on corporate return on assets (ROA). The paper aimed to analyse whether corporate eco-efficiency performance (represented by energy consumption, water consumption, carbon emission and waste generation) affects the performance of ROA. Data on the eco-efficiency and ROA was collected from fourteen food and beverage companies listed in the Johannesburg Stock Exchange for a period of ten years (2012 to 2021). Using the STATA Software, the data was analysed by applying the Generalised Method of Moment (GMM) statistical technique, which enhanced the statistical analysis robustness. Findings from the GMM analysis showed different results. On the one hand, the results indicate that energy and water consumption in the food and beverage companies have a positive (but insignificant) effect on ROA. On the other hand, the results show that waste generation has a negative (but insignificant) effect on ROA; and that carbon emission has a negative and significant effect on ROA.
Ključne besede: environmental accounting, return on assets, financial performance, eco-efficiency, energy consumption, water consumption, carbon
emission, waste generation, sales revenue
Objavljeno v RUP: 18.12.2025; Ogledov: 522; Prenosov: 1
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2. Bridging the question–answer gap in retrieval-augmented generation : hypothetical prompt embeddingsDomen Vake, Jernej Vičič, Aleksandar Tošić, 2025, izvirni znanstveni članek Opis: Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of responses. However, bridging the style gap between user queries and relevant information in document text remains a persistent challenge in retrieval-augmented systems, often addressed by runtime solutions (e.g., Hypothetical Document Embeddings (HyDE)) that attempt to improve alignment but introduce extra computational overhead at query time. To address these challenges, we propose Hypothetical Prompt Embeddings (HyPE), a framework that shifts the generation of hypothetical content from query time to the indexing phase. By precomputing multiple hypothetical prompts for each data chunk and embedding the chunk in place of the prompt, HyPE transforms retrieval into a question-question matching task, bypassing the need for runtime synthetic answer generation. This approach does not introduce latency but also strengthens the alignment between queries and relevant context. Our experimental results on six common datasets show that HyPE can improve retrieval context precision by up to 42 percentage points and claim recall by up to 45 percentage points, compared to standard approaches, while remaining compatible with re-ranking, multi-vector retrieval, query decomposition, and other RAG advancements.
Ključne besede: LLM, hypothetical prompt embedding, Retrieval-Augmented Generation (RAG)
Objavljeno v RUP: 04.08.2025; Ogledov: 1372; Prenosov: 31
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3. Inferring population history and demography using microsatellites and major histocompatibility complex genes in european roe deerElena Bužan, Luka Duniš, Urška Gerič, Sandra Potušek, Boštjan Pokorny, 2021, objavljeni povzetek znanstvenega prispevka na konferenci Ključne besede: microsatellites, major histocompatibility complex, next-generation sequencing, spatial genetic diversity, roe deer
Objavljeno v RUP: 04.02.2021; Ogledov: 4000; Prenosov: 14
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