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A methodological framework for evaluating thermal fluids in solar power applications via the simple additive weighting technique
No full textBased on the findings of this study, the sensitivity analysis demonstrates how variations in weighting scenarios quantitatively influence the ranking of heat transfer fluids. Under equal weighting, Syltherm 800 achieves approximately 4-8% higher overall scores than the other fluids, emerging as the most balanced option. When operating temperature is prioritized, Therminol VP-1 outperforms Syltherm 800 by about 3-4%, confirming its suitability for high-temperature CSP applications. In cost-dominated scenarios, Syltherm 800 provides more than a 10% advantage over Dowtherm A, indicating its economic competitiveness in budget-constrained systems. When thermal efficiency is emphasized, Marlotherm LH exhibits a 2-3% performance advantage, supporting its relevance for efficiency-driven designs. Overall, these results indicate that no single fluid serves as a universally optimal solution; instead, the ideal choice depends on the specific priorities of the application. Therminol VP-1 is most appropriate for high-temperature and stability-critical large-scale CSP systems, Syltherm 800 is advantageous where cost constraints dominate, Marlotherm LH is preferable in efficiency-focused configurations, and Dowtherm A offers a balanced alternative where supply reliability, safety considerations, or infrastructure compatibility are project-specific constraints. Given that these conclusions rely on numerical modeling and a WSM-based multi-criteria evaluation framework, further investigations are warranted. Future work should include experimental validation using parabolic trough test facilities, long-term assessments of fluid thermal stability and degradation behavior, and integration of environmental metrics such as toxicity, leakage risk, and life-cycle impacts. Moreover, comparing WSM with AHP, TOPSIS, fuzzy logic, or hybrid MCDM methods would provide insights into methodological consistency and robustness, further strengthening the position of this approach within the literature
Artificial Intelligence and Machine Learning in Bone Metastasis Management: A Narrative Review
No full textBackground: Artificial intelligence (AI) and machine learning (ML) are increasingly used in the diagnosis and management of bone metastases, spanning lesion detection, segmentation, prognostic modeling, fracture risk assessment, and surgical decision support. However, the literature is heterogeneous and rapidly evolving, making it difficult for clinicians to contextualize these developments. Methods: We performed a narrative review of the literature on AI/ML applications in bone metastasis management, focusing on studies that address clinically relevant problems such as detection and segmentation of metastatic lesions, prediction of skeletal-related events and survival, and support for reconstructive decision-making. We prioritized recent, peer-reviewed work that reports model performance and highlights opportunities for clinical translation. Results: Most published studies center on imaging-based diagnosis and lesion segmentation using radiomics and deep learning, with generally high internal performance but limited external validation. Emerging work explores prognostic models and biomechanically informed fracture risk estimation, yet these remain at an early proof-of-concept stage. Very few frameworks are integrated into routine workflows, and explainability, bias mitigation, and health-economic impacts are rarely evaluated. Conclusions: AI and ML tools have substantial potential to standardize imaging assessment, refine risk stratification, and ultimately support personalized management of bone metastases. Future research should focus on externally validated, multimodal models; development of AI-augmented alternatives to the Mirels score; federated multicenter collaboration; and routine incorporation of explainability and cost-effectiveness analyses
Dose-dependent effectiveness and patient-reported outcomes with JAK1 inhibitors in atopic dermatitis: a 36-week multicenter real-world cohort.
No full textEuropean energy performance of buildings directive and cultural heritage
No full textThis report summarises the work of the ICOMOS Expert Group on the Implementation of the Energy Performance of Buildings Directive (EPBD) in the Built Heritage Sector, established in 2023 to evaluate the impact of the EPBD recast on Europe’s historic buildings.Adopted under the European Green Deal, the recast Directive introduces Minimum Energy Performance Standards (MEPS), Zero-Emission Building (ZEB) targets, and life-cycle carbon assessment obligations. These measures represent a significant step towards climate neutrality, but they also present complex challenges for culturally and architecturally significant buildings.The Expert Group gathered evidence from over nineteen EU and non-EU countries through national fact sheets, thematic analyses, and case studies. Executive summary distills the main findings, strategic recommendations, and future priorities emerging from this research.Typetechnical reportTitleEuropean energy performance of buildings directive and cultural heritageLanguagesEnglishAuthorsAlexandrou, Eleni / Berggren, Kersti / Bourgès, Ann / Broström, Tor / Brito, Nelson da Silva / Bruguerolle, Antoine / Donnelly, Jacqui / Erikkson, Petra / Foster, Jez / González-Longo, Cristina / Gould, Anthony / Hughes, David / Huttunen, Marku / Haas, Franziska / Karamyan, Anna / McDermott, Deirdre / Mirouze, Chloé / Paulus, Ave / Pissarides, Chrysanthos / Radivojević, Ana / Shaffrey, Gráinne / Silvestru, Claudiu / Stoškus, Liutauras / Villalba Montaner, ClaraEditorsAlatalu, Riin / Iaquinta, Maria Teresa / Kishalı, Emre / Ritson, James / Tomšič, Miha / Vernimme, NathalieCorporate authorsICOMOS Expert Group on EPBD ImplementationPlace of publicationCharenton-le-PontCountry of publicationFrance</div
Long-Term Oncological Outcomes for Locally Advanced Rectal Cancer Patients with Pathological Complete Response After Neoadjuvant Chemoradiotherapy: A Turkish Oncology Group Study
No full textThe goal of this study was to look at the long-term survival outcomes and clinical characteristics of stage II/III locally advanced rectal cancer (LARC) patients who acquired pathological complete response (pCR) following neoadjuvant chemoradiotherapy (NCRT). The clinicopathological characteristics and treatment details of 277 LARC patients with pCR, relapse-free survival (RFS), overall survival (OS), and locoregional and systemic recurrence rates, were assessed. The 5-year RFS and OS rates were 85.6% and 90.9%. The rates of local and systemic recurrence were 3.6% and 7.9%. Our study confirmed the favorable results in survival in patients with LARC who achieved pCR
On the importance of radiation in natural convection cooling of plate-finned heat sinks: A review
No full textPlate-finned heat sinks are essential components in natural convection-based thermal management systems, particularly in electronics, energy devices, and passive cooling technologies. While heat is transferred through conduction, convection, and radiation, the radiative component is frequently neglected in both experimental and numerical studies. This review compiles and evaluates over 60 published works to quantify the role of thermal radiation across different fin geometries, including rectangular, pin-fin, innovative, and microscale designs. In microscale systems using high-emissivity materials such as silicon, radiation may contribute up to 70 % of total heat dissipation due to weak convective transport. For conventional aluminum-based rectangular and pin-fin configurations, the radiative share typically ranges from 10 % to 50 %, depending on parameters such as surface emissivity, fin spacing, and thermal orientation. Enhanced geometries designed to improve convective performance may also increase view factors, thereby amplifying radiative transfer unintentionally. This review identifies key parameters influencing radiative contribution, including temperature gradients, input power, and surface treatment, and highlights the need for improved experimental methods to directly measure radiation. The compiled results offer practical guidance for optimizing passive cooling systems where accurate heat transfer prediction is essential. The review also emphasizes the importance of incorporating radiation in modeling and design to ensure thermal performance reliability, especially under low-convection or high-temperature conditions. In addition, the review highlights that the emissivity and temperature of surrounding walls play a non-negligible role in determining the net radiative heat transfer, particularly in confined or low-power systems. The survey also reveals that uncertainties related to emissivity measurement, view-factor estimation, and radiation–convection separation are rarely quantified, limiting the reliability of reported radiation fractions. Last but not least, a consistent correlation between geometrical parameters and thermal radiation that covers a considerable universal range seem to be still absent
Evaluating augmented deep networks for MRI-based brain anomaly classification
No full textAccurate and early detection of brain anomalies using MRI is critical for effective diagnosis and treatment planning. Recent advances in deep learning, particularly convolutional neural networks, have significantly improved the capabilities of automated medical image analysis, enabling more precise and scalable solutions in clinical settings. However, the performance of such models is highly dependent on the quality, diversity, and annotation detail of the training datasets. Building on this foundation, in this study, we investigate the performance of augmented deep neural networks for MRI-based brain anomaly classification using the BraTS 2021 dataset and our newly introduced Gazi Brains 2025 dataset, which includes MRI scans from 500 patients, a substantial portion of which are annotated for various brain anomalies. The dataset supports both binary (tumor versus normal) and multiclass (seven neurological conditions) classification tasks. We develop and evaluate a series of deep learning and transformer-based models for anomaly classification, with a particular focus on the impact of synthetic data augmentation. Using StyleGANv3 and Guided Diffusion, we generate synthetic MRI scans to enhance training data diversity and examine their effect on model performance. In addition, a ß-VAE–based generative pipeline is employed to further expand the synthetic dataset, providing controllable latent representations and additional variability through VAE sampling. Experimental results show that all three augmentation strategies-StyleGANv3, Guided Diffusion, and ß-VAE- significantly improve classification accuracy compared to baseline models trained solely on the original dataset. DenseNet achieved an accuracy value of up to 91% in binary classification when trained with augmented data, and EfficientNetV2S also achieved up to 72% in multiclass classification. Although StyleGANv3-generated images exhibited superior visual quality (low FID scores), the method was limited in sample volume. In contrast, the diffusion-based approach allowed the creation of larger synthetic datasets, though at the cost of extended training and sampling times. The ß-VAE model produced a moderate number of anatomically coherent samples with lower computational cost, offering a balanced alternative between quality and scalability
Infarct-to-spleen volume ratio as a novel volumetric predictor of splenectomy in splenic infarction
No full textBackground: Splenic infarction is a rare and often underrecognized condition with diverse etiologies and variable clinical presentations. While most cases are managed conservatively, identifying patients who may require surgical intervention remains a clinical challenge. This study aimed to evaluate the infarct-to-spleen volume ratio (ISR) as a radiologic predictor for splenectomy in patients with splenic infarction and to propose a risk stratification model incorporating ISR and fever. Methods: In this retrospective, two-center cross-sectional study, 236 patients diagnosed with splenic infarction between January 2015 and January 2023 were included. Volumetric analysis was performed using contrast-enhanced CT to calculate ISR. Clinical, laboratory, and radiologic features were compared between surgical (n = 13) and non-surgical (n = 223) groups. ROC curve and logistic regression analyses were used to evaluate predictive parameters for splenectomy. A risk score based on ISR and fever status was developed to stratify patients into risk categories. Results: The splenectomy rate was 5.6%. Patients who underwent splenectomy had significantly higher ISR (median 28.2 vs. 9.17, p = 0.02). ROC analysis identified ISR as a strong predictor for surgery (AUC = 0.69, 95% CI: 0.49–0.86), with a specificity of 77.9% and NPV of 97.2%. Multivariate logistic regression confirmed ISR (OR: 1.03, p = 0.002) and fever (OR: 2.69, p = 0.005) as independent predictors. A risk score (range 0–4) based on these variables stratified patients into low-, intermediate-, and high-risk groups with splenectomy rates of 1.5%, 6.3%, and 33.3%, respectively. Conclusion: ISR is a novel, objective predictor of the need for splenectomy in patients with splenic infarction. Incorporating ISR and fever into a simple risk score may aid clinical decision-making and help identify candidates for early conservative management or outpatient follow-up