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Artificial intelligence as a linguistic interface in interior design education: Reconceiving design tools as communication media
No full textThe rapid evolution of artificial intelligence (AI) has fundamentally transformed the role of design tools, extending their function beyond conventional applications to become linguistic interfaces that mediate and structure cognitive and communicative processes in design. This study critically examines the reconfiguration of AI-driven design tools within interior design education, foregrounding their role as discursive agents in design communication. Through an in-depth analysis of an AI-based workshop conducted with interior design students, the research investigates how linguistic engagements with AI systems influences decision-making, conceptualization, and procedural methodologies within the design process. The findings reveal a shift from traditional design heuristics toward a linguistically embedded computational paradigm, elucidating how AI-generated textual and visual interactions inform cognitive schemata and foster epistemic innovation in design education. The study argues that AI-driven design tools not only enhance visualization capabilities but also act as cognitive co-agents that refine and articulate linguistic constructs within design discourse. By proposing an integrative framework for embedding language-oriented AI applications into interior design pedagogy, this research advances a reconceptualization of digital design engagement and advocates for a deeper integration of computational linguistics into design cognition and practice.https://doi.org/10.1177/1478077125135295
Investigating Impacts of Climate Change and War on the Green Cover Area in Northeast Syria Between 2000 and 2023
No full textDuring the 20th century and continuing into the present, significant warming was observed due to the emission of greenhouse gases, primarily CO2 and CH4, into the atmosphere. The sixth assessment report of the Intergovernmental Panel on Climate Change estimates a warming of 1.1°C above 1850-1900 in 2011-2020. As climate warming continues to reshape atmospheric conditions and trigger extreme weather events such as drought, forest fires, and floods. The intricate relationship between these changes and vegetation dynamics becomes increasingly evident, profoundly affecting ecological systems, agriculture, and politics. Vegetation is an essential component in ecological systems since it serves as a connection between soil, atmosphere, and water; and plays a crucial role in maintaining the balance of carbon and water, facilitating the exchange of materials and energy, ensuring climate stability, and reducing greenhouse gas emissions. Generally, changes in vegetation are analyzed to assess the environmental conditions at both regional and global levels. The normalized difference vegetation index (NDVI) is a commonly employed tool for analyzing variations in vegetation dynamics. Examining these changes and their triggers is crucial for comprehending the relationships between vegetation and ecosystems. Syria, located at the intersection of Asia and the Mediterranean, is an area with a high level of water scarcity and is susceptible to extreme droughts, especially in the northeastern region, where temperature and evaporation have significant impacts. The land cover in the northeastern region has undergone significant alterations in recent decades due to the armed conflict, which its effects on the land use and land cover (LULC) are neither unidirectional nor spatially uniform. Research and policy alike have given careful consideration to the relationship between conflict and climate change. Extreme weather events, like droughts, have been shown to correspond with the start of armed conflicts occasionally. The most widely proposed mechanism between climate change and violent conflict is the relationship between shocks to agricultural productivity and the degradation of vegetation. In this study, the ERA5-Land data has been used to analyze the climatic conditions in northeast Syria between 2000 and 2023. In addition, the satellite images of Landsat 5, 7, 8, and 9 have been used to generate NDVI maps. Then, a correlation between the meteorological parameters and the NDVI was established to examine how climate change and drought have affected the green cover in the study area, especially after 2011, when the armed conflict started. Meteorological parameters such as temperature, soil temperature, precipitation, and evaporation on an hourly scale have been applied. The drought events have been addressed by the number of precipitation events, precipitation accumulation, and precipitation intensity. Moreover, the Standardized Precipitation Index (SPI), which is considered as a global standard for evaluating the severity of drought, has also been used for various time scales (3, 6, 9, and 12 months). The study highlighted how climate change had affected the vegetation areas in the northeastern region of Syria. The results emphasized different drought events and mapped the change in the LULC through the time period of the study.https://doi.org/10.5194/egusphere-egu24-2052
Recent advances in mechanically strong and tough hyaluronic acid hydrogels and cryogels
No full texthttps://doi.org/10.1080/10601325.2025.257307
Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC
No full textAbstract We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the {\uptau } {\uptau } <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>τ</mml:mi> <mml:mi>τ</mml:mi> </mml:mrow> </mml:math> decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.https://doi.org/10.1140/epjc/s10052-025-14713-whttps://dx.doi.org/10.48550/arxiv.2502.13047https://dx.doi.org/10.3204/pubdb-2025-05283https://dx.doi.org/10.5445/ir/1000189265https://dx.doi.org/10.3929/ethz-c-000790187https://dx.doi.org/10.3204/pubdb-2025-00886http://arxiv.org/abs/2502.13047https://hdl.handle.net/11368/3121298https://bib-pubdb1.desy.de/record/624418https://doi.org/10.5445/IR/1000189265https://publikationen.bibliothek.kit.edu/1000189265https://publikationen.bibliothek.kit.edu/1000189265/171440517https://hdl.handle.net/11384/15956
Enhanced Vitamin D3 Adsorption Through Novel Hydrophobic Halloysite–Alginate Biopolymer Composites
No full textThis study presents a sustainable strategy to enhance polymer encapsulation, adsorption, and functional properties by chemically modifying sodium alginate with hydrophobic groups. Hydrophobic alginate derivatives were synthesized via a solvent-free method using hexadecyl trimethylammonium bromide, resulting in nanoparticles capable of effectively capturing non-polar compounds. To further improve compatibility within alginate-based biocomposites, halloysite nanotubes were modified through ball milling and surfactant-assisted treatments. The resulting nanocomposites (MBHA and MHHA) exhibited significantly enhanced adsorption and controlled release behavior, as confirmed by FTIR analysis of hexadecyl alginate ester conjugation. Vitamin D3 adsorption followed the Langmuir isotherm, with high correlation coefficients (R2 = 0.998 for MBHA and R2 = 0.991 for MHHA), indicating monolayer adsorption on a homogenous surface. Kinetic modeling revealed that the adsorption process adhered to a pseudo-second-order model (R2 = 0.9969 for MBHA and R2 = 0.999 for MHHA), suggesting that chemisorption was the dominant rate-controlling mechanism. These results demonstrate the critical role of surface modification in designing nano-engineered biopolymers with superior adsorption, stability, and release profiles, offering sustainable applications in medicine, agriculture, and environmental remediation.https://doi.org/10.3390/polym17081083https://pubmed.ncbi.nlm.nih.gov/40284348http://dx.doi.org/10.3390/polym1708108
Parameter Characterization and Low-Temperature Capacity Fade Modeling of LFP Cell Chemistry for Electric Vehicle Applications
No full texthttps://doi.org/10.1109/access.2025.362487
A Note on Payout Functions and Probabilities Related to Shin's Model
No full textIn this extended abstract, we investigate the payout functions derived by Shin’s model, especially in the context of two-outcome events. We develop an analytical framework to calculate the expected payouts for both insider and outsider bettors and explore a new analytical solution with curious structure and details. Our findings provide new insights into the mechanics of betting markets and offer a deeper understanding of probability estimation from fixed odds.https://doi.org/10.33774/coe-2025-v0d2
Responsive Procedural Morphologies Adapting to Environmental Factors in XR
No full texthttps://doi.org/10.1007/978-3-031-97775-6_
Collecting behavioural data across countries during pandemics: Development of the COVID-19 Risk Assessment Tool
No full textAbstract Tools that can be used to collect behavioural data during pandemics are needed to inform policy and practice. The objective of this project was to develop the Your COVID-19 Risk tool in response to the global spread of COVID-19, aiming to promote health behaviour change. We developed an online resource based on key behavioural evidence-based risk factors related to contracting and spreading COVID-19. This tool allows for assessing risk and provides instant support to protect individuals from infection. The Risk Estimation Questions assessed users’ location, age, gender, work environment, day-to-day behaviours currently performed, and conditions under which these behaviours would change. Users were also asked to estimate how often they keep their distance from others in public and regularly wash their hands, and the procedures they follow to do so. A multidisciplinary research team of more than 150 international experts developed the tool. Over 60,000 users in more than 150 countries have assessed their risk and provided data. The majority of respondents reported that they almost always keep their distance from others in public places, and most participants reported washing their hands after touching public or shared surfaces or when entering buildings. The tool, data, and results were openly shared to support government and health agencies developing behaviour change interventions. This tool creates a blueprint for similar digital infrastructure that can be replicated and used in future pandemics.https://doi.org/10.3758/s13428-025-02743-xhttps://dx.doi.org/10.48620/89551http://dx.doi.org/10.3758/s13428-025-02743-xhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=29024https://ucrisportal.univie.ac.at/de/publications/1a84bcf2-08c8-475f-afd2-ee11db8cb643https://research.rug.nl/en/publications/d566f46e-ae16-4af5-b7b0-ff5435c7beechttps://hdl.handle.net/11370/d566f46e-ae16-4af5-b7b0-ff5435c7beechttps://ciencia.ucp.pt/en/publications/71255650-61d3-4f0e-b54d-dda3496c1780https://cris.maastrichtuniversity.nl/en/publications/d245d2e2-d7bf-46b2-a894-7c05885374d8https://research.ou.nl/en/publications/99674e7b-8606-45c4-8f68-8e1b8212d3a6https://hdl.handle.net/10362/186141http://hdl.handle.net/10138/599789https://hdl.handle.net/10067/2161820151162165141https://repository.uantwerpen.be/docstore/d:irua:30341https://avesis.deu.edu.tr/publication/details/93821bcb-6f90-45a3-920a-37cb0ea18e57/oaihttps://pure.qub.ac.uk/en/publications/993bab3f-83a3-47d3-b49a-b6938657cd1dhttps://www.repository.cam.ac.uk/handle/1810/388002https://www.repository.cam.ac.uk/handle/1810/38702