Gdańsk University of Technology

W języku fenomenologii koncepcję świadomości Karola Wojtyły można określić jako czysto noematyczną: kontakt człowieka z bytem jest bezpośredni, zachodzi przed progiem świadomości, nie jest zatem zapośredniczony przez świadomość, która stanowiłaby odrębny rejon bytu. Świadomości nie można utożsamiać z Ja. Świadomość nie może istnieć jako substancjalny podmiot ani jako ontyczne podłoże przeżyć, ani jako władza. Podstawowym pojęciem w tej koncepcji jest pojęcie samowiedzy, świadomość nie obiektywizuje, ale tylko odzwierciedla to, co zostało zobiektywizowane przez wiedzę i samowiedzę, co gwarantuje pierwotność bytu wobec świadomości. Prawdziwy realizm może być budowany nie na świadomości myślenia, ale świadomości działania, która jest ustawianiem człowieka w konkretnym bycie a zarazem jego tworzeniem. Artykuł pokazuje miejsce tej koncepcji świadomości w obrębie filozofii XX wieku. Stara się też wskazać na stan jej recepcji. Wybitność tej koncepcji i jej znaczenie dla filozofii współczesnej zostaje uwyraźnione przez jej konfrontację z poglądami ważnych myślicieli współczesności, takich jak Ingarden, Husserl, Heidegger czy Sartre.

The spectral flow is an integer-valued homotopy invariant for paths of selfadjoint Fredholm operators. Lesch as well as Pejsachowicz, Fitzpatrick and Ciriza independently showed that it is uniquely characterised by its elementary properties. The authors recently introduced a $G$-equivariant spectral flow for paths of selfadjoint Fredholm operators that are equivariant under the action of a compact Lie group $G$. The purpose of this paper is to show that the $G$-equivariant spectral flow is uniquely characterised by the same elementary properties when appropriately restated. As an application, we introduce an alternative definition of the $G$-equivariant spectral flow via a $G$-equivariant Maslov index.

High-density polyethylene’s (HDPE) chemical inertness, the foundation of its commercial success, renders it nearly impossible to chemically recycle. Furthermore, strict purity requirements limit mechanical recycling rates to ∼30% despite the legislative targets being 75%. Chemically recyclable polyolefin mimics containing cleavable ester linkages offer a solution, but their compatibility with commodity polyethylene waste streams remains unexplored, raising concerns that contamination could worsen rather than solve the global recycling crisis. Here, we demonstrate that HDPE-like polyesters (HDPE-like) exhibit complete miscibility with HDPE across all compositions (20–80 wt %), a crucial result given the expected difficulty to separate the HDPE-like from HDPE waste streams. Single melting transitions, cocrystallized lamellar structures, and retention of ductility confirm molecular-level mixing without mechanical failure. Strikingly, incorporation of just 20 wt %HDPE-like increases adhesion to aluminum by more than 10-fold (from 0.4 to 4.4 MPa), an effect that persists in postconsumer recycled HDPE, enabling upcycling of low-value waste into metal-laminate applications. Both components retain their recycling pathways: HDPE undergoes mechanical reprocessing, while HDPE-like achieves quantitative methanolysis to monomers that repolymerize with full property retention. By demonstrating that chemically recyclable polyesters can be miscible with, and functionally beneficial to, commodity polyolefins, this work establishes a dual-recycling framework where a contamination becomes a useful additive.

Gas hydrates are of great relevance to both the oil industry and the environment. Understanding how these solid structures nucleate from aqueous solutions is essential for controlling their formation. Experimental studies have often suggested that hydrate nucleation originates at the interface between the aqueous phase and the guest-molecule reservoir. To assess this hypothesis, we perform molecular dynamics simulations of CO2 hydrate nucleation. First, we place hydrate seeds at different positions relative to the interface and monitor their evolution, finding that seeds embedded in the bulk are more likely to grow than those located near or at the interface. Second, we analyze spontaneous nucleation simulations with and without an interface. Our previous work showed that nucleation rates are indistinguishable in both systems, strongly indicating that the interface does not play a role. Here, trajectory analysis reveals that hydrates nucleate in regions of locally high CO2 concentration, which arise spontaneously in the bulk and are not associated with the interface. Our results indicate that hydrate nucleation does not preferentially occur at the interface, at least under the deep supercooling conditions explored in this work. Further work at higher temperatures, and considering alternative nucleation locations, is needed to reconcile experiments and simulations, and thereby reach a deep understanding of the mechanism of hydrate formation.

Glycolysate and glycerolysate-organic substances recovered from the chemical recycling of polyurethane waste-were investigated as sustainable plasticizers for acrylonitrile-butadiene rubber composites filled with 30 phr of calcium lignosulfonate or kraft lignin. The study evaluated the impact of these recycled plasticizers (added at 10 and 15 phr) on the curing process, morphology, rheology, mechanical and dynamic mechanical performances. Rheological analysis confirmed that both plasticizers significantly reduced the complex viscosity of the rubber compounds, with the effect being most pronounced at the 15 phr loading. While the incorporation of glycolysate and glycerolysate slightly extended the optimum cure time and decelerated the curing process, the cross-link density remained consistently within the range of 3.5-4 × 10-4 mol·cm-3. Morphological studies revealed that the plasticizers facilitated better dispersion of both lignin types and improved interfacial adhesion. However, the mechanical response differed significantly depending on the filler type. A consistent increase in elongation at break was observed only for composites filled with kraft lignin, where values rose from 341% for the reference up to 571% for the sample with 15 phr of glycolysate. In contrast, the application of plasticizers to calcium lignosulfonate-filled composites led to an initial decrease in both tensile strength and elongation at break. Notably, kraft lignin-filled composites exhibited superior overall mechanical performance, with glycolysate effectively maintaining tensile strength levels comparable to the reference. While both recovered substances performed effectively as processing aids, they had a negligible effect on the glass transition temperature. The results demonstrated that these recovered polyurethane derivatives are highly effective, sustainable alternatives to conventional plasticizers, showing a clear synergistic effect particularly with kraft lignin.

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments.