Changing EAP assessment practices in the age of generative artificial intelligence: The case of Scottish higher education institutions

The impact of generative artificial intelligence (GenAI) on higher education has been widely discussed since the public release of ChatGPT-3.5 in late 2022. However, there has been little empirical research on changes in English-for-Academic-Purposes (EAP) assessment practices in response to GenAI. This qualitative case study intends to fill this gap by examining how Scottish universities changed EAP assessments in response to GenAI, how effective those changes were perceived by EAP academics, and what recommendations EAP academics offered for future assessment practices. Data were collected from six semi-structured interviews conducted with EAP academics at five Scottish universities in mid-2024 and thematically analysed. The findings reveal that while substantial changes in assessment task design were limited, modifications to task requirements (e.g., GenAI declarations, context-specific prompts) and grading practices were more common. Moreover, our participants expressed scepticism about the effectiveness of some changes (e.g., AI use declarations) but positively perceived others (e.g., the use of context-specific questions, spontaneous speaking tasks, and named marking). As for their recommendations, the participating EAP academics generally advocated authentic and innovative tasks, such as portfolio-based assessment, reflections, multimodal projects, and GenAI output evaluation over reverting to traditional exams while simultaneously highlighting issues with workload and learning outcomes. The study implies a need for clearer institutional guidance, ongoing professional dialogue, and support for experimentation with GenAI-integrated assessment design in EAP contexts

Dynamic Mechanical Properties of Polymer Dispersed–Silica Nanoparticle Composites

A series of nanocomposites were prepared by dispersing various silica nanoparticles in polystyrene (PS) and poly(methyl methacrylate) (PMMA) and analyzed by differential scanning calorimetry (DSC) and dynamic mechanical thermal analysis (DMTA). Colloidally dispersed silica nanoparticles and structured fumed silica were used in the synthesis, leading to well-dispersed systems. A detailed investigation was conducted into the thermal and dynamic mechanical behavior of the nanocomposites. The findings of this study demonstrate that the incorporation of filler particles increases the glass transition temperature (Tg) and suppresses polymer flow, resulting in an extended rubbery plateau. Significant reinforcement as evidenced by an increased plateau modulus above Tg was only observed for samples containing fumed silica. While neat PMMA begins to flow and deform irreversibly above 150 °C, the fumed silica/–polymer hybrid materials remain stable up to 240 °C, exceeding the polymer’s Tg by over 100 °C. The polymer nanocomposites exhibited slight mechanical damping at high temperature as evidenced by a surprisingly low tan δ (&lt;0.1). Compared to the structured fumed silica hybrids, colloidally dispersed silica had very slight effect on polymer reinforcement.</p

Efficient Time-Stepping Methods for Isogeometric Analysis of Nonlinear Heat Conduction in Composites

In this paper, we propose a class of high-order time integration schemes combined with high-order IsoGeometric Analysis (IGA) in three space dimensions. The combined methods offer robust solutions of nonlinear heat diffusion in three-dimensional composites that pose numerical challenges. This tailored strategy significantly enhances computational efficiency, especially crucial when addressing nonlinear heat transfer in three-dimensional enclosures. Leveraging precise geometry representation and seamless high-order element continuity of the IGA, this method effectively exploits these advantages. It emphasizes the vital synergy between high-order spatial discretization and an equivalent high-order time integration scheme. This study also highlights the risks of overlooking this pairing, which can lead to a degradation of the overall high-order accuracy and increased computational demands due to the complexity of high-order nonuniform rational B-splines. Numerical examples, such as applications involving a furnace wall segment and a rail wheel heat transfer, are used to validate the efficiency and accuracy of the combined approach. Consistently surpassing the conventional methods in both aspects, the proposed method notably excels in providing precise solutions for steep heat gradients even on coarse meshes. Consequently, this approach constitutes a substantial advancement in the field of transient heat transfer analysis within composite domains

ICPPNet: A semantic segmentation network model based on inter-class positional prior for scoliosis reconstruction in ultrasound images

Objective:Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately identify spine regions in images due to relatively small target areas and the presence of a lot of interfering information. Therefore, we developed a novel neural network that incorporates prior knowledge to precisely segment spine regions in ultrasound images.Materials and methods:We constructed a dataset of ultrasound images of spine regions for semantic segmentation. The dataset contains 3,136 images of 30 patients with scoliosis. And we propose a network model (ICPPNet), which fully utilizes inter-class positional prior knowledge by combining an inter-class positional probability heatmap, to achieve accurate segmentation of target areas. Results:ICPPNet achieved an average Dice similarity coefficient of 70.83% and an average 95% Hausdorff distance of 11.28 mm on the dataset, demonstrating its excellent performance. The average error between the Cobb angle measured by our method and the Cobb angle measured by X-ray images is 1.41 degrees, and the coefficient of determination is 0.9879 with a strong correlation.Discussion and conclusion:ICPPNet provides a new solution for the medical image segmentation task with positional prior knowledge between target classes. And ICPPNet strongly supports the subsequent reconstruction of spine models using ultrasound images

The State of Garment Repair and Alteration Services in the UK: Typology, Evaluation of Online Information and Thematic Analysis of Customer Reviews

The fashion industry is a significant contributor to global environmental degradation, driving up carbon emissions and resource consumption. Many fashion consumers feel guilt associated with contributing to this damage. While garment repair and alteration services (GRAS) offer a pathway to mitigate these impacts by extending the lifespan of clothing, consumer engagement with these services remains low. This study investigates the state of commercial GRAS in the UK, identifying barriers to consumer participation and their potential role in fostering sustainable behaviour. Through a comprehensive typology of UK GRAS providers and a thematic analysis of customer reviews, we reveal obstacles such as limited repair skills, unclear service information, and gaps in consumer trust. Our findings suggest that bridging these informational and service quality gaps could promote greater use of GRAS, aligning the fashion industry more closely with circular economy principles. In addition, we highlight the potential for GRAS to enhance well-being by developing consumer's emotional attachment to clothing and positive feelings. These findings underscore the dual benefits of GRAS for environmental sustainability and consumer well-being, suggesting broader applications for GRAS in shaping more sustainable consumption patterns

A novel deep eutectic solvent-based liquid membrane for the extraction of glycerol from crude biodiesel

This study used deep eutectic solvent (DES) as the liquid membrane in a bulk liquid membrane system (BLM) to remove glycerol from waste cooking oil-based biodiesel. The DES was prepared from choline chloride and tetraethylene glycol at a molar ratio of 1:5. Diethyl ether was employed as a novel strip phase for the glycerol in BLM. The effects of the DES: biodiesel ratio, stirring speed, and extraction time on the extraction and stripping efficiencies were investigated. The results showed that BLM could give better glycerol removal from biodiesel than mechanical shaking. Increasing the DES: biodiesel ratio, stirring speed, and extraction time can enhance glycerol removal from the feed phase, achieving purified biodiesel that complies with biodiesel international standards. The purified biodiesel met the ASTM D6751 and EN 14214 international standards requirement for glycerol content of less than 0.24% under the following conditions of DES: biodiesel ratio of 1:1, stirring speed of 200 rpm, and extraction time of 240 min. The transport mechanisms of glycerol in the system were postulated based on two consecutive irreversible first-order extraction and stripping. The kinetic study shows that the extraction and stripping processes in this system could be explained by a first-order kinetic model, as the experimental results fitted into the model showed R2 values of 0.98, 0.97, and 0.97 for the feed phase, membrane phase, and strip phase, respectively. The extraction and stripping rate constants (k1 and k2) were 0.0031 and 0.0019 min−1, respectively.</p

StorySculptor: Offering a personalised text-based gaming experience using Large Language Models (LLMs)

This study explored the integration of large language models (LLMs) into the realm of interactive fiction (text-based gaming) and aimed to bridge natural language processing techniques with the domain of storytelling. The study dives into the current state-of-the-art applications of LLMs and their capacity to generate narratives in real-time gaming environments. The paper further highlighted the implementation steps and focused on proposing a novel application of LLMs: developing a game agent designed to act as an active participant in interactive text games such that it is capable of adapting narratives based on player input and contributing to a more personalized gaming experience. By developing a novel system, the research contributes to the field through the following key advancements: (1) the creation of a novel dataset, generated using GPT-4, specifically designed to fine-tune LLMs for interactive gaming scenarios, and (2) the successful fine-tuning of the Mistral 7B instruct model, enabling dynamic game narrative generation

Structure search for B₇Mn₂ clusters: Inverse sandwich geometry with a high-spin state

Herein, we present a density functional theory (DFT) investigation of the B7Mn2 cluster, a boron based system doped with two manganese atoms. The most stable structure adopts an inverse sandwich configuration, in which the B7 ring is symmetrically coordinated by two Mn atoms and exhibits a spin multiplicity of eight. Higher-energy isomers retain the B7 wheel-like motif, with Mn atoms positioned either above the ring or at peripheral sites. The Mn2–B7 complex exhibits moderate interaction energy, arising from a balance between favorable electrostatic and orbital contributions and significant Pauli repulsion. Strong π-type interactions between the Mn d-orbitals and the delocalized B7 ring lead to substantial charge transfer (∼1.3 e−), rendering the Mn centers electron-deficient. This behavior is consistent with their Lewis acidic character and a weak Mn–Mn bonding interaction. Nucleus-independent chemical shift (NICS) isosurface analysis reveals a pronounced antiaromatic character, with extended deshielding under a magnetic field applied along the z-axis. In contrast, fields oriented along the x- and y-directions produce more localized effects, highlighting the planar delocalization of the antiaromatic B7 framework

Dual congruence in live-streaming commerce:A mixed-method to examine the role of virtual influencers and live content on consumer purchase behavior

As virtual influencers increasingly become a fixture in live-streaming commerce, understanding how their brand congruence influences consumer behaviors is critical. Hence, this research investigates the dual congruence between live content, virtual influencers, and brands and how these congruences impact perceived value, source credibility, and, ultimately, purchase behaviors. Anchored in the congruity theory, perceived value theory, and source credibility theory, a mixed-method approach was employed in this research. Study 1 employs PLS-SEM and ANN to quantitatively demonstrate that utilitarian value and credibility, rather than hedonic content or attractiveness, significantly influence purchases. Study 2 offers qualitative insights to explain these findings, highlighting consumer preferences for informative content and credible influencers over mere entertainment or visual appeal. Theoretically and practically, this research contributes to digital marketing theory by clarifying how congruence mechanisms operate in virtual contexts and offers managerial strategies for brands seeking to leverage virtual influencers effectively in live-streaming commerce