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Nevzdržna svoboda vzporednih svetov
Get PDFPrispevek je nadaljevanje besedila »Vzhodno od raja« z »drugimi sredstvi«. Če se prvo besedilo ukvarja z retorično optiko nasprotnikov poziva, skuša pričujoče besedilo osvetliti ideološko podstat te optike, vključno z vzroki za rusko-ukrajinsko vojno
Osteoclast-expanded supercharged NK cells perform superior antitumour effector functions
Get PDFObjective. Natural killer (NK) cells are the largest innate lymphocyte subset with potent antitumour and antiviral functions. However, clinical utilisation of human NK cells is hampered due to a lack of reliable methods to augment their antitumour potential. We demonstrated technology in which human NK cells were cocultured with osteoclasts in the presence of probiotic bacteria. This approach significantly augmented the antitumour cytotoxicity and polyfunctionality of human NK cells, resulting in the generation of supercharged NK (sNK) cells. Methods and analysis. We explored the proteomic, transcriptomic and functional characterisation of sNK cells using cell imaging, flow cytometric analysis, 51-chromium release cytotoxicity assay, ELISA, ELIspot, IsoPLexis single-cell secretome analysis, proteomic analysis, RNA analysis, western blot and enzyme kinetics. Results. We found that sNK cells were less susceptible to split anergy and tumour-induced exhaustion. Proteomic analyses revealed that sNK cells significantly increased their cell motility and proliferation. Single-cell transcriptomes uncovered sNK cells undertaking a unique differentiation trajectory and turning on STAT1, JUN, BHLHE40, ELF1, MAX and MYC regulons essential for augmenting antitumour effector functions and proliferation, respectively. Both proteomic and single-cell transcriptomes revealed that an increase in Cathepsin C helped to augment the quantity and function of Granzyme B. Conclusions. These results support that this unique method produces potent NK cells for clinical utilisation and delineate the molecular mechanisms associated with this process
On the Wiener-like root-indices of graphs
Get PDFIn this paper, we examine roots of graph polynomials where those roots can be considered as structural graph measures. More precisely, we prove analytical results for the roots of certain modified graph polynomials and also discuss numerical results. As polynomials, we use, e.g., the Hosoya, the Schultz, and the Gutman polynomial which belong to an interesting family of degree-distance-based graph polynomialsthey constitute so-called counting polynomials with non-negative integers as coefficients and the roots of their modified versions have been used to characterize the topology of graphs. Our results can be applied for the quantitative characterization of graphs. Besides analytical results on bounds and convergence, we also investigate other properties of those measures such as their degeneracy which is an undesired aspect of graph measures. It turns out that the measures representing roots of graph polynomials possess high discrimination power on exhaustively generated trees, which outperforms standard versions of these indices. Furthermore, a new measure is introduced that allows us to compare different topological indices in terms of structure sensitivity and abruptness
N-Beats architecture for explainable forecasting of multi-dimensional poultry data
Get PDFThe agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework. Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model’s robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture
Effectiveness of social and emotional learning programmes in schools
Get PDFObstajajo številne študije, ki potrjujejo koristi socialnega in čustvenega učenja. Pregled teh študij pa razkriva tudi pomanjkljivosti v raziskovanju, primerjanju in pregledu raziskav na tem področju. Merjenja niso enotna in nimajo enako dolge tradicije. Glede na literaturo v ZDA socialno in čustveno učenje merijo že tri desetletja, medtem ko v Evropi to področje raziskujemo šele zadnje desetletje, in to v veliko manjšem obsegu. V tem pregledu smo se omejili na metaanalize in sistematične študije s področja socialnega in čustvenega učenja. Z namenom osvetlitve tovrstnih raziskav, ki nam omogočajo pregled učinkovitosti programov socialnega in čustvenega učenja, smo pregledali baze Web of Science, Psycarticles in Scopus. V končno analizo je bilo vključenih 24 raziskav, ki sistematično prikazujejo opis posamezne študije, število študij, zajetih v posamezen pregled, ter učinke programov socialnega in čustvenega učenja ter ugotovitve programov. Oblikovali smo dve raziskovalni vprašanji: 1) kaj kažejo sistematični pregledi in metaanalize glede na državo izvajanja, vzorec in raziskovalni fokus in 2) kakšni so učinki in ugotovitve posameznih programov. Pripravili smo tabelo, v kateri smo navedli vključene države, velikost vzorcev, raziskovalni fokus, pozitivne učinke in ugotovitve. S tem smo prikazali obsežnost raziskovanja socialnega in čustvenega učenja ter se hkrati dotaknili dilem, ki se pojavljajo pri raziskovanju. Ugotovitve kažejo, da imajo različni programi socialnega in čustvenega učenja pozitiven učinek na razvoj socialnih in čustvenih kompetenc učencev in na njihovo kasnejše življenje. Raziskovanje učinkov socialnega in čustvenega učenja je potrebno, ker se na tej podlagi ti programi lahko bolje implementirajo v šolske sisteme in se lahko izberejo tisti, ki kažejo ustrezne učinke.Many studies are showing the benefits of social and emotional learning. The review of studies also reveals shortcomings in researching, comparing and reviewing research in this area. Measurements in this field are not consistent, nor do they have the same long tradition. The literature shows that social and emotional learning has been measured for three decades in the United States, whereas in Europe it has only been measured in the last decade and on a much smaller scale. In this review, we have limited ourselves to meta-analyses and systematic studies in the field of social and emotional learning. We also note the lack of systematic studies on social and emotional learning in the Slovenian context. In order to highlight such research, which allows us to review the effectiveness of social and emotional learning programmes, we have searched the Web of Science, Psycarticles and Scopus databases. The final analysis included 24 studies in the field of social and emotional learning, which systematically show the type of review of each study, the number of studies included in each review, as well as the effects and findings of the programmes. For the purpose of this article, we formulated two research questions: 1) what do systematic reviews and meta-analyses show in terms of countries of implementation, samples and research focus, and 2) what are the effects and findings of individual studies. This was done to show the extent of the research on social and emotional learning and to address the dilemmas that arise in research. The findings show that social and emotional learning programmes have a positive impact on students and also later in life, and that there are different types of social and emotional learning programmes. Despite the variety of programmes, research on the effects of social and emotional learning is shown to be necessary, as it helps to better implement the programmes in school systems and to select among the different programmes those that show effects
Influence of Matrix, Filler, and structural design on the dielectric and energy storage properties of cellulose composites
Get PDFCellulose is a renewable, biodegradable, cost-effective, and easy-to-process natural biopolymer. Because of its dielectric, piezoelectric, and mechanical performance, cellulose and its composites are often used as a matrix, filler, substrate, gel electrolyte, and dielectric layer for flexible energy storage devices. They offer easy fabrication strategies and excellent mechanical properties. In this mini-review, we summarized the principles of dielectric and energy storage features of cellulose composites and factors affecting their dielectric properties, mainly exploring the incorporation of various filler materials in the cellulose matrices and cellulose materials acting as different types of fillers. Moreover, the fabrication strategies, structural design, and matrix-filler interactions of cellulose composites enhance their dielectric properties as systematically reviewed. This review summarizes the current state-of-the-art progress of cellulose dielectric composites, challenges, and future outlook for green dielectric and energy storage devices. The review suggests that optimizing the filler type, cellulose fiber content, fabrication techniques, and structural design can significantly enhance the dielectric properties and energy storage capacity of cellulose composites
Enhancing the reliability and accuracy of wireless sensor networks using a deep learning and blockchain approach with DV‑HOP algorithm for DDoS mitigation and node localization
Get PDFWireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments
Upcycling of plastic waste into multi-walled carbon nanotubes as efficient organic dye adsorbent
Get PDFMulti-walled CNTs with an average diameter of about 80 nm, a length of several micrometers and surface area (SBET) of 100 m2 g–1 were obtained by pyrolysis of low-density polyethylene waste. The potential of the resulting MWCNTs material to purify water containing organic dyes was tested with Bezaktiv Blau HE-RM (BB) and Bezaktiv Rot S-3B (BR) reactive dyes. 200 mg L–1 MWCNT material was used to follow the adsorption of 30 mg L–1, 40 mg L–1, 50 mg L–1 and 60 mg L–1 BB and BR at pH 3 and a temperature of ~25 °C. The results have shown that this material has a high potential as a sorbent, and its adsorption capacity of 257 mg g–1 (for Bezaktiv BlauHE-RM) and 213 mg g–1 (for Bezaktiv Rot) is close to some commercial MWCNTs and functionalized MWCNT-based adsorbents. The adsorption process was very fast, reaching 80–90% of the dye removal in 10–15 minutes, and the equilibrium time was reached in 40–60 minutes. The adsorption isotherm showed that the Langmuir model was more suitable than the Freundlich model for describing the adsorption properties of the pollutants