Explore the vast range of titles available.

Reservoir computing is a form of machine learning that utilizes nonlinear dynamical systems to perform complex tasks in a cost-effective manner when compared to typical neural networks. Recent advancements in reservoir computing, in particular quantum reservoir computing, use reservoirs that are inherently stochastic. In this paper, we investigate the universality of stochastic reservoir computers which use the probabilities of each stochastic reservoir state as the readout instead of the states themselves. This allows the number of readouts to scale exponentially with the size of the reservoir hardware, offering the advantage of compact device size. We prove that classes of stochastic echo state networks form universal approximating classes. We also investigate the performance of two practical examples in classification and chaotic time series prediction. While shot noise is a limiting factor, we show significantly improved performance compared to a deterministic reservoir computer with similar hardware when noise effects are small. Many recent advances in reservoir computing utilize inherently stochastic dynamics and can be designed so that the number of readouts scales exponentially with device size. Here, authors prove the universality of stochastic echo state networks and test the performance of two practical examples.

Ullazines and their π-expanded derivatives have gained much attention as active components in various applications, such as in organic photovoltaic cells or as photosensitizers for CO2 photoreduction. Here, we report the divergent synthesis of functionalized diazaullazines by means of two different domino-reactions consisting of either a Povarov/cycloisomerization or alkyne–carbonyl metathesis/cycloisomerization protocol. The corresponding quinolino-diazaullazine and benzoyl-diazaullazine derivatives were obtained in moderate to good yields. Their optical and electronic properties were studied and compared to related, literature-known compounds to obtain insights into the impact of nitrogen doping and π-expansion.

We report the synthesis of pyrrolo[3′,2′:3,4]fluoreno[9,1-gh]quinoline and pyrrolo[2′,3′,4′:4,10]anthra[1,9-fg]quinoline derivatives. This novel class of N-doped polycyclic heteroaromatic compounds was synthesized by a site-selective cross-coupling reaction followed by acid-mediated cycloisomerization and Pd-catalyzed CH arylation as the final ring-closing reactions. Preliminary optical and aromatic properties were studied by means of steady-state absorption and fluorescence spectroscopy and DFT calculation. Special emphasis was placed on the impact of ring alternation and position of the N-doping within the scaffold.

Climate change, driven by greenhouse gas emissions, threatens human health and biodiversity. While the digitalization of health care, including telemedicine and artificial intelligence, offers sustainability benefits, it also raises concerns about energy use and electronic waste. Balancing these factors is key to a sustainable health care future. The objective of this review was to examine the extent to which digitalization in the health care sector influences environmental sustainability. Specifically, it aimed to assess how digitalization can contribute to reducing the health care sector's impact on global climate change. From these findings, conclusions were drawn regarding the extent to which digitalization aligns with the objectives of the Planetary Health movement and how these 2 movements may mutually reinforce each other. A scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines using databases such as PubMed and Scopus was conducted, and 58 quantitative studies from 2009 to 2024 were analyzed for environmental, social, and economic outcomes aligned with Planetary Health goals. This review analyzed 58 studies on the environmental impact of digitalization in health care primarily focusing on telemedicine, which was examined in 91% (53/58) of the studies. Most studies (56/58, 97%) quantified transport-related emissions avoided through digitalization, with some also assessing emissions from health care facilities, medical equipment, and energy consumption. Findings indicated that telemedicine significantly reduces carbon dioxide emissions, with total avoided emissions amounting to approximately 830 million kg. A substantial proportion of the studies (36/58, 62%) focused on social aspects, highlighting factors such as patient satisfaction, time efficiency, and overall convenience. In addition, economic considerations were analyzed in 48% (28/58) of the studies, emphasizing cost reductions and resource optimization. However, only 12% (7/58) of the studies evaluated the full life cycle impact of digital technologies, highlighting the need for further research on their long-term environmental sustainability. This review calls for further research beyond telemedicine, advocating for life cycle analyses and actionable strategies for a sustainable digitalization in health care systems. The Planetary Health framework is highlighted as a guide for ensuring sustainable digital transformation in health care.

Quantum Reservoir Computing (QRC) leverages quantum systems to perform complex computational tasks with exceptional efficiency and reduced energy consumption. We introduce a minimalistic QRC framework utilizing as few as five atoms in a single-mode optical cavity, combined with continuous quantum measurement. The system is conveniently scalable, as newly added atoms naturally couple with existing ones via the shared cavity field. To achieve high computational expressivity with a minimal reservoir, we include two critical elements: reservoir feedback and polynomial regression. Reservoir feedback modifies the reservoir’s dynamics without altering its internal quantum hardware, while polynomial regression nonlinearly enhances output resolution. We demonstrate significant QRC performance in memory retention and nonlinear data processing through two tasks: predicting chaotic time-series data via the Mackey-Glass task and classifying sine-square waveforms. This framework fulfills QRC’s objectives to minimize hardware size and energy consumption, marking a significant advancement in integrating quantum physics with machine learning technology.

We report the synthesis of polycyclic uracil derivatives. The method is based on palladium-catalysed Sonogashira–Hagihara and Suzuki–Miyaura cross-coupling reactions followed by Brønsted acid-mediated cycloisomerisation. The developed methodology tolerates various functional groups and leads to moderate up to quantitative yields of the final products. The impact of different functional groups on the optical properties was studied by UV–vis and fluorescence spectroscopy.

The development of a new and straightforward chemoselective method for the synthesis of uracil-based structures by combining Suzuki–Miyaura and Sonogashira–Hagihara cross-coupling is reported. The methodology was applied to synthesize a series of novel compounds. The tolerance of the combination of different functional groups was tested. The influence of different functional groups on the physical properties was studied by ultraviolet–visible (UV–vis) and fluorescence spectroscopy, providing new insights into the potential applications of uracil-based structures.

Three bis- or tris-brominated 2-trifluoromethylquinolines have been successfully applied in palladium-catalysed Sonogashira reactions, leading to several examples of alkynylated quinolines in good to excellent yields. Optical properties of selected products have been studied by steady state absorption and fluorescence spectroscopy which give insights of the influence of the substitution pattern and of the type of substituents on the optical properties.

A variety of pyrrolo[3,2- d ]pyrimidine-2,4(3 H )-diones were prepared by a combination of Sonogashira reaction and subsequent cyclization by domino C–N coupling/hydroamination reaction. The optical properties (UV–vis absorption and fluorescence) depend on the substitution pattern of the compounds.

Thiazolopyridines are a highly relevant class of small molecules, which have previously shown a wide range of biological activities. Besides their anti-tubercular, anti-microbial and anti-viral activities, they also show anti-cancerogenic properties, and play a role as inhibitors of cancer-related proteins. Herein, the biological effects of the thiazolopyridine AV25R, a novel small molecule with unknown biological effects, were characterized. Screening of a set of lymphoma (SUP-T1, SU-DHL-4) and B- acute leukemia cell lines (RS4;11, SEM) revealed highly selective effects of AV25R. The selective anti-proliferative and metabolism-modulating effects were observed in vitro for the B-ALL cell line RS4;11. Further, we were able to detect severe morphological changes and the induction of apoptosis. Gene expression analysis identified a large number of differentially expressed genes after AV25R exposure and significant differentially regulated cancer-related signaling pathways, such as VEGFA-VEGFR2 signaling and the EGF/EGFR pathway. Structure-based pharmacophore screening approaches using in silico modeling identified potential biological AV25R targets. Our results indicate that AV25R binds with several proteins known to regulate cell proliferation and tumor progression, such as FECH, MAP11, EGFR, TGFBR1 and MDM2. The molecular docking analyses indicates that AV25R has a higher binding affinity compared to many of the experimentally validated small molecule inhibitors of these targets. Thus, here we present in vitro and in silico analyses which characterize, for the first time, the molecular acting mechanism of AV25R, including cellular and molecular biologic effects. Additionally, this predicted the target binding of the molecule, revealing a high affinity to cancer-related proteins and, thus, classified AVR25 for targeted intervention approaches.