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Simplex-anchored regressors for fast global optimization-oriented miniaturization of microwave circuits
(2026-12) Koziel, Slawomir; Pietrenko-Dabrowska, Anna; Department of Engineering
The small physical size of high-frequency components is an essential prerequisite in an increasing number of applications. While size reduction of microwave circuits can be achieved with a suitable choice of architecture, ensuring the best available compromise between electrical parameters and size necessitates meticulous adjustment of all system dimensions. Due to the complexity of electrically small structures, parameter adjustment should be executed globally. Nevertheless, it is an expensive endeavor because it entails a large number of system evaluations executed through electromagnetic (EM) simulation. This article introduces an innovative approach to expedited miniaturization of microwave passives, in which the primary goal is miniaturization, while the conditions imposed on performance metrics are treated as constraints. Our framework operates in three stages, including parameter space pre-screening, machine-learning (ML)-driven globalized search, and gradient-based fine-tuning. The first stage involves random sampling aimed at identifying the most promising parameter space region. The ML process launched therein employs simplex-based regression models, the use of which contributes to a competitive computational efficiency. On the other hand, the final tuning improves design quality at a low cost, as it is realized as a local optimization process. The performance of the suggested algorithm is illustrated using two planar devices and demonstrated to be superior to several benchmark methods. The major advantages of our technique include low running costs, equivalent to only a few dozen full-wave analyses, and straightforward implementation.
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THE INFLUENCE OF PUBLIC EXPENDITURE ON SPORTS SERVICES AND GOODS ON PRIVATE CONSUMPTION AND SEDENTARY BEHAVIOUR IN EUROPEAN COUNTRIES
(2026-08-01) Saavedra, Yzan; Durst, Susanne; Department of Business and Economics
The objectives of this study were (i) to examine the influence of public expenditure on sports services and goods on private consumption and (ii) to assess whether the prevalence of sedentary behaviour could be predicted by public expenditure in sports, taking into account educational level, and sex differences in European countries. Data from 27 European countries were used; one additional country was included for objective (i) (n=29) and three for objective (ii) (n=30). A Pearson bivariate correlation and a distributed lag model were used to examine the relationship between public expenditure and private consumption, with GDP per capita included as a control variable. Additionally, multiple linear regression models were applied to predict sedentary behaviour based on GDP, educational level, and sex. Results showed a strong positive correlation between public expenditure and private consumption (r=0.846–0.922, p<0.001). The lagged model, including public expenditure over five years and GDP per capita, explained more than 75% of the variance in private sports consumption. GDP per capita predicted sedentary behaviour in over 44.3% of cases across medium, high, and total education groups. Public expenditure was also a significant predictor in selected subgroups, particularly in the total education group and among women.
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Editorial : Advancing cartilage regeneration and repair: biomaterials and biomechanical strategies
(2026) Todros, Silvia; Berardo, Alice; Cavinato, Cristina; Gargiulo, Paolo; Department of Engineering
Verk
The Effects of Light Therapy on Cognitive Function and Stress in Women With Breast Cancer Before Systemic Treatment
(2025-11) Aspelund, Snaefridur Gudmundsdottir; Halldorsdottir, Thorhildur; Agustsson, Gudjon; Tobin, Hannah Ros Sigurdardottir; Wu, Lisa M.; Amidi, Ali; Johannsdottir, Kamilla R.; Lutgendorf, Susan K.; Telles, Rachel; Daly, Huldis Franksdottir; Sigurdardottir, Kristin; Figueiro, Mariana G.; Redd, William H.; Valdimarsdottir, Heiddis B.; Baldursdottir, Birna; Department of Psychology
Background: Cancer-related cognitive impairment (CRCI), for example, impairments in reaction time, processing speed, memory and executive function, may be associated with breast cancer (BC) surgery which can disrupt biological and psychological stress markers. Evidence suggests that light therapy may ameliorate cognitive impairment and stress. In this double-blind, randomized controlled trial, we investigated the efficacy of light therapy on mitigating the impact of BC surgery on CRCI in a national Icelandic cohort of women with BC. Methods: Participants were randomly allocated to receive circadian-effective blue light (BL, N = 60) or circadian-ineffective dim light (DL, N = 57) for 4 weeks after surgery. The primary outcome was overall cognitive performance (assessed via global composite score), and secondary outcomes were specific cognitive domains, cognitive complaints, psychological (depressive symptoms, overall cancer-related stress and its symptoms: hyperarousal, avoidance, and intrusive thoughts) and biological (cortisol and α-amylase) stress markers. Linear regression and path analyses within structural equation modeling frameworks were conducted, adjusted for baseline cognitive performance, age, education, subsequent cancer treatment, cancer stage, and treatment credibility. Results: No group differences were found in overall cognitive performance or in specific cognitive domains, except for a non-significant trend for faster reaction times in the BL group (p < 0.11). Additionally, the BL group reported significantly fewer cognitive complaints compared with the DL group (p < 0.05), as well as a non-significant trend for less intrusive thoughts (p < 0.11). No group differences were observed in the biological stress markers. Conclusion: Overall, these findings suggest that light therapy may help mitigate the adverse effects associated with BC surgery on CRCI and intrusive thoughts, although further research is warranted. Trial Registration: NCT04418856.
Verk
Multimodality in Media Retrieval
(Association for Computing Machinery, Inc, 2024-06-07) Pegia, Maria Eirini; Department of Computer Science
The quest for retrieving relevant media for a given query is well-studied and has various applications. Modern publicly available media collections provide diverse modalities of the same objects, which can enhance search. Our research delves into enhancing media retrieval by effectively representing and querying multimodal data. In the retrieval methods’ ranking procedure, we examine efficiency through techniques like approximate nearest neighbor (ANN) indexing and high-performance computing (HPC). Our method, MuseHash, is proposed for single media object retrieval and is applied to images and 3D objects, outperforming existing methods on diverse datasets. Moreover, it significantly reduces execution times with ANN and HPC. Future plans include considering multimodality in the video retrieval domain.

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