Juraj Dobrila University of Pula

Climate change profoundly affects the phytochemical profiles and therapeutic potentials of medicinal plants through environmental stressors such as rising temperatures, altered precipitation patterns, and increased atmospheric CO2 levels. This review critically examines the mechanisms underlying these impacts, focusing on physiological plant responses, shifts in primary and secondary metabolite biosynthesis, and the consequent effects on medicinal efficacy and toxicity. Our findings indicate that elevated CO2 often enhances biomass production but exerts variable effects on bioactive compound concentrations; temperature fluctuations disrupt phenological phases, thereby altering medicinal quality; and water stress significantly modulates secondary metabolite profiles. While these environmental challenges threaten plant-based healthcare, potential mitigation strategies—including sustainable agricultural practices, genetic engineering, and conservation approaches—are discussed as viable solutions. We recommend future research to emphasize metabolomics, interdisciplinary methodologies, and integration of traditional knowledge to bolster resilience and preserve the therapeutic efficacy of medicinal plants amid ongoing climatic uncertainties.

Organizations communicate across many channels, yet official websites remain a controlled, authoritative space where firms articulate identity and strategy. This study examines how Croatia's top enterprises (n = 100) describe themselves on their websites and which emotional tones they use to signal strategic intent. Our goal is to identify recurring strategic signifiers and map distinct sentiment profiles in corporate narratives. We compiled company descriptions from official sites; texts were originally in Croatian and machine-translated into English, and all analysis was conducted on the English corpus. Using lexicon-based sentiment methods (AFINN, Bing, NRC), we quantified polarity and discrete emotions, aggregated scores at the firm level, and applied k-means clustering to normalized emotion vectors. Results show a consistent emphasis on mission-vision-values language and a dominance of positive emotions-especially trust and anticipation. We interpret, based on cluster exemplars, that higher trust/anticipation tones can function as soft governance cues, while transparency about negatives characterizes an issue-addressing regime without eroding overall positivity. Cluster analysis reveals three stable profiles: optimistic consumer-oriented narratives, transparent issue-addressing messaging, and low-affect technical descriptions. We conclude that sentiment profiling offers a practical audit tool for aligning website copy with stakeholder expectations and governance communication, supporting benchmarking, and future tests linking narrative tone to investor behavior and firm performance.

This paper presents BPMN Assistant, a tool that leverages Large Language Models for natural language-based creation and editing of BPMN diagrams. While direct XML generation is common, it is verbose, slow, and prone to syntax errors during complex modifications. We introduce a specialized JSON-based intermediate representation designed to facilitate atomic editing operations through function calling. We evaluate our approach against direct XML manipulation using a suite of state-of-the-art models, including GPT-5.1, Claude 4.5 Sonnet, and DeepSeek V3. Results demonstrate that the JSON-based approach significantly outperforms direct XML in editing tasks, achieving higher or equivalent success rates across all evaluated models. Conformance checking evaluation confirms that generated models preserve executable semantics, with JSON achieving an average F1 score of 0.72 compared to 0.69 for XML, though frontier models like GPT-5.1 and Claude 4.5 Sonnet demonstrated superior precision with direct XML generation. Furthermore, despite requiring more input context, our approach reduces generation latency by approximately 43% and output token count by over 75%, offering a more reliable and responsive solution for interactive process modeling.

Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability of smaller, open-weight models as a resource-efficient alternative. We present a methodology for optimizing a one-billion-parameter Llama 3.2 model for multilingual e-commerce intent recognition. The model was fine-tuned using Quantized Low-Rank Adaptation (QLoRA) on a synthetically generated dataset designed to mimic real-world user queries. Subsequently, we applied post-training quantization techniques, creating GPU-optimized (GPTQ) and CPU-optimized (GGUF) versions. Our results demonstrate that the specialized 1B model achieves 98.8% accuracy, approaching the performance of the significantly larger GPT-4.1 model. A detailed performance analysis revealed critical, hardware-dependent trade-offs: while 4-bit GPTQ reduced VRAM usage by 41%, it paradoxically slowed inference by 82% on an older GPU architecture (NVIDIA T4) due to dequantization overhead. Conversely, GGUF formats on a CPU achieved a speedup of up to 4.3 & times; in inference throughput and up to a 72% reduction in RAM consumption compared to the FP16 baseline. We conclude that small, properly optimized open-weight models are not just a viable but a more suitable alternative for domain-specific applications, offering state-of-the-art accuracy at a fraction of the computational cost.

Breast cancer is a highly heterogeneous malignancy, characterized by diverse genetic, epigenetic, and phenotypic variations, as well as by metabolic reprogramming and oxidative stress. Lipid peroxidation bioactive product 4-hydroxynonenal (4-HNE) plays a significant role in the development and progression of cancer. In this study, we quantified circulating 4-HNE-modified proteins and performed comprehensive untargeted metabolomic profiling of the patients' plasma using LC-ESI-QTOF-MS and GC-EI-QMS, aiming to investigate systemic metabolic pathways associated with oxidative damage in breast cancer. Significantly elevated levels of 4-HNE-modified proteins were detected in breast cancer patients compared to healthy controls, accompanied by distinct metabolomic signatures enriched in lipid metabolism. Several metabolites, including specific long-chain fatty acids, exhibited significant correlations with circulating 4-HNE-modified proteins, suggesting an interaction between lipid peroxidation-driven protein modification and breast cancer-associated metabolic reprogramming. Overall, this study provides evidence of associations between systemic 4-HNE-mediated protein modification and altered metabolic profiles in breast cancer, highlighting oxidative stress-related metabolites as potential biomarkers and pointing to redox-metabolic crosstalk in breast cancer patients.