The need to analyze and summarize critical information from electronic health records places a significant burden on clinicians. Clinical-note summaries are vital for making healthcare more efficient and helping healthcare personnel keep track of their paperwork. While transformer models and large language models (LLMs) are efficient in this task, the computational cost and stringent performance requirements in healthcare present major challenges. This work addresses these challenges by evaluating parameter-efficient fine-tuning with low-rank adaptations (LoRA) techniques for abstractive clinical-note summarization.We conducted a full evaluation on the MIMIC-IV-Ext-BHC dataset, comparing four transformer models (T5, PEGASUS-XSUM, BART, and FLAN-T5) and four LLMs (Mistral-7B, LLaMA-3-8B, Gemma-2-9B, and Falcon-7B) across zero-shot, full fine-tuning, and LoRA fine-tuning strategies. We also integrated LexRank, TextRank, LSA, and Luhn with clinical NLP functionalities from cTAKES for graph-based extractive summarization. In our investigation, LoRA achieved higher scores than full fine-tuning on key metrics while reducing the computational resource burden. Specifically, our LLaMA-3-8B model, fine-tuned with LoRA, achieved a ROUGE-1 score of 0.7022, ROUGE-2 of 0.5312, ROUGE-L of 0.6718, METEOR of 0.6787, and BERTScore F1 of 0.9180 on the test set. This represents a 2.6× improvement in ROUGE-1 over the extractive baseline, though direct comparison is limited by fundamental differences between extractive and abstractive approaches.These results were accomplished using approximately 97%–99% fewer trainable parameters than full fine-tuning. The findings indicate that LoRA could facilitate the implementation of LLMs within resource-constrained medical environments, and has the potential to improve healthcare professionals’ access to information and support clinical decision-making processes.
Heap leaching is widely used for the recovery of copper from low-grade oxide ores. Continuous monitoring of copper concentration in the pregnant leach solution (PLS) is essential for tracking leaching kinetics, determining optimal irrigation termination, and balancing metallurgical inventories at the individual-heap level; however, measurement at heap drainage channels remains operationally challenging owing to limited accessibility and reliance on manual sampling with delayed laboratory analysis. This study characterizes the photographic and modeling parameters of a laboratory prototype that estimates copper concentration in copper sulfate solutions using empirical, image-based colorimetric regression with low-cost consumer imaging hardware rather than a laboratory spectrometer, as a basis for future in-line sensing. Synthetic PLS solutions (0 to 40 g Cu/L in sulfuric acid) were imaged under controlled illumination (fixed-color, fixed-intensity LED light, diffused inside a dark chamber and not collimated) using a Nikon D3100 digital single-lens reflex (DSLR) camera across four channels (red, green, blue, and a white composite) at three intensity levels. For each channel, an absorbance-like feature was derived from the channel intensity relative to a zero-copper blank. A dataset of 432 images was subdivided into 256 spatial sub-samples per image across four channels, generating 442,368 channel-level observations, which were reduced to 15,120 physically coherent observations by sequential filtering (Kendall rank correlation, then linear and quadratic absorbance-concentration bounds). A feedforward artificial neural network (ANN) predicted copper concentration from the four color features and was benchmarked against a multivariate linear model used as a Lambert-Beer baseline. On a separate simulated, controlled dataset, the ANN achieved an RMSE of approximately 0.55 g/L (residual standard deviation of 0.40–0.41 g/L), a reduction of approximately 37 % in residual standard deviation relative to the best linear baseline (from 0.64 to 0.40 g/L) on simulated data. Experimental validation yielded a prediction error of approximately 25 %. This error is structured rather than random, arising from intensity-level displacement caused by specular reflections on the cylindrical sample cell, and is comparable to the ∼ 20 % uncertainty of current manual sampling; it is therefore a hardware and data-acquisition limitation rather than a limitation of the sensing concept. Under real PLS conditions, the error would be expected to increase, so the laboratory value should be read as a best-case bound. A flat-window flow cell, collimated illumination, and inclusion of the recorded illumination intensity as a model input are identified as the priority improvements.
We consider an elastic model for a circular arch that incorporates membrane, transverse shear, and bending effects. The central line of the arch is partitioned into elements, and an ultra-weak variational formulation is developed alongside a discontinuous Petrov–Galerkin (DPG) approximation procedure based on so-called optimal test functions. The formulation uses discontinuous stress and displacement interpolations on the element mesh, with corresponding interface variables defined at the nodes. Theoretical analysis establishes well-posedness and quasi-optimal convergence properties of the DPG approximation, while also revealing potential error amplification influenced by the curvature of the arch and the imposed boundary conditions. The method is tested on examples with different support configurations. The numerical experiments confirm the theoretical predictions and further demonstrate that the accuracy of the DPG method can be improved by employing a suitably scaled test space norm.
Graphitic carbon nitride (g-C3N4) nanosheets are extensively used in photocatalytic applications but are often decorated with noble-metal co-catalysts to yield relevant efficiencies. Herein, we report a dual-metal co-catalyst system comprising cobalt and molybdenum (CoMo) nanoclusters uniformly integrated into heptazine g-C3N4 nanosheets for enhanced visible-light-driven hydrogen evolution reaction (HER). The intimate contact between metals as well as with gC3N4 layer was verified through X-ray absorption and X-ray photoelectron spectroscopies. Mechanistic insights obtained through a combination of experimental and computational studies suggest that Co acts as primary catalytic center, while Mo plays a preliminary role in facilitating surface hydrogen coverage, collectively accelerating the redox rates and achieving 10 times higher performance compared to bare gC3N4. Post-HER analysis reveals disintegration of co-catalyst into Co single-atoms, which then takes the leading role in sustaining the HER. The role of co-catalyst was mainly limited to the surface, acting as electron trap, reducing charge recombination and as a HER catalytic center. The composite exhibits excellent photostability over 13 h of ON/OFF illumination cycles, highlighting its potential for intermittent operations under practical conditions. This study presents a co-catalyst design strategy leveraging dual-metal synergy as a non-noble, scalable alternative to conventional noble-metal-based HER co-catalysts for solar fuel generation.
Resilience thinking has become central to addressing environmental and societal challenges, yet it focuses primarily on ecological and social dimensions while physical foundations remain underrepresented. This systematic scoping review examined 90 geodiversity and geoheritage studies (2012–2025) analysing connections to resilience concepts. While most reviewed studies lack explicit resilience frameworks, they demonstrate extensive implicit resilience engagement, particularly through maintaining diversity and redundancy, managing slow variables, encouraging learning, and broadening participation. Geodiversity enriches resilience thinking by treating physical environments not as passive backdrops but as active participants in system change, and by bridging natural and social dimensions that are typically managed separately. Three interrelated barriers limit integration of resilience and geodiversity: disciplinary communities remain disconnected, evidence emphasizes description over mechanisms, and institutional infrastructure for geodiversity governance lags behind that for biodiversity. Overcoming these barriers through collaborative efforts could ground resilience thinking in the geological reality that underlies all sustainability challenges.
