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'We Have Built No National Temples but the Capitol': The Pentagon ‘Patriotic’ Rotating Art Exhibit Program (OSDPAEP)
No full textThis article will explore the complexities of curating the Pentagon’s ‘'Patriotic’” Rotating Art Exhibit Program (OSDPAEP) from the perspective of the curatorial team. The parameters of the program require an innate understanding of the historical and current legacy of the Pentagon, headquarters of the Department of Defense (DoD) in Arlington County, Virginia. Semi-structured interviews were undertaken with a narrative analysis revealing how the curators negotiated their roles, particularly in a contemporary art context. The participants’ roles as creative leaders at the Pentagon were informed by the following three themes that emerged from the interviews: moral framework, creative opportunities and leadership
An explainable deep learning approach to enhance the prediction of shield tunnel deviation
No full textAlthough machine learning models have achieved high enough accuracy in predicting shield position deviations, their “black box” nature makes the prediction mechanisms and decision-making processes opaque, leading to weaker explanations and practicability. This study introduces a novel explainable deep learning framework comprising the Informer model with enhanced attention mechanisms (EAMInfor) and deep learning important features (DeepLIFT), aimed at improving the prediction accuracy of shield position deviations and providing interpretability for predictive results. The EAMInfor model attempts to integrate channel attention, spatial attention, and simple attention modules to improve the Informer model's performance. The framework is tested with the four different geological conditions datasets generated from the Xiamen metro line 3, China. Results show that the EAMInfor model outperforms the traditional Informer and comparison models. The analysis with the DeepLIFT method indicates that the push thrust of push cylinder and the earth chamber pressure are the most significant features, while the stroke length of the push cylinder demonstrated lower importance. Furthermore, the variation trends in the significance of data points within input sequences exhibit substantial differences between single and composite strata. This framework not only improves predictive accuracy but also strengthens the credibility and reliability of the results
Closed-form seismic earth pressure solutions via adaptive limit analysis and hybrid learning models
No full textThe need for accurate seismic earth pressure solutions in earthquake geotechnical engineering demands a cost-effective method. This paper employs a three-stability-factor approach to determine seismic earth pressures by considering cohesion, surcharge, and unit weight effects that is analogous to Terzaghi’s traditional superposition method for bearing capacity determination proposed by Terzaghi. To achieve this goal, adaptive finite element limit analysis is used to explore seismic earth pressure intricacies using both upper-bound and lower-bound approaches. Numerical findings highlight the influence of internal friction angle, wall roughness, and surcharge pressure on seismic earth pressure factors. Distinct failure mechanisms of smooth and rough retaining walls under seismic loads offer vital insights for practical design. Incorporating cutting-edge machine learning techniques such as Bayesian regularization feed forward neural network and multivariate adaptive regression splines, a series of closed-form solutions for stability factors is established. These data-driven solutions ensure precision, simplicity, and efficiency in determining seismic lateral earth pressure. This approach transcends theoretical boundaries, providing insights for designing stable retaining walls in seismic zones. Rigorous validation against published results confirms the accuracy and reliability of the developed solutions. This research represents a significant advancement in seismic design methodologies, contributing to enhanced infrastructure resilience in the face of seismic challenges
30th ICDE World Conference 2025
No full textThis study examines the impact of pathway studies on preparation for success in first-year university courses by analysing demographic, socioeconomic, and academic performance trends. Conducted at a regional Australian university, it draws on de-identified data (2017-2023) within a structured pathway program designed to support students who do not meet direct entry requirements. The analysis focuses on two core English language development courses (entry-level and advanced), which act as critical entry points to university and reflect broader characteristics of the pathway cohort.
Student demographic and academic data were merged with publicly available socioeconomic indicators from the Australian Bureau of Statistics to explore how these factors influence student outcomes. Findings show a shift toward a younger, predominantly female cohort, with female and older students achieving higher academic results. While most participants study online, there is an increasing presence of younger students on campus.
The study highlights key challenges and opportunities in supporting diverse student groups and improving access to higher education. It contributes to the evidence on how pathway programs promote educational equity and inform more inclusive, sustainable practices in tertiary settings. The findings also offer practical insights for policy, curriculum development and future research
European Association for Computer-Assisted Language Learning 2025 (EuroCALL 2025)
No full textThis study explores pause patterns as indicators of fluency in spoken presentations, focusing on differences between low-intermediate ESL learners and advanced ESL speakers. The research examines pauses in one-minute extracts at three distinct points—beginning, middle, and end—from each speaker's presentation. TED Talks, as an open-source resource, provide data representing advanced ESL speakers, while low-intermediate speakers are represented by three ESL learners, with a total of six participants in the study.
Using Praat software, this study analysed the location, frequency, and potential reasons for pauses. A multimodal approach is employed, integrating video analysis to capture both auditory and visual elements of communication. This method provides a nuanced understanding of why pauses are used by speakers at different proficiency levels and this underpins the pedagogical implications of the findings.
The study provides practical guidance for educators, trainers, and curriculum designers by identifying best practices in pause management. It highlights how professional speakers strategically employ pauses to enhance clarity and engagement while identifying common challenges ESL learners face, such as excessive or misplaced pauses.
As a follow up to previous studies, this study investigates the pedagogical value of benchmarking professional speakers' practices for ESL learners. It underscores the critical role of pause management in achieving effective oral communication. By integrating multimodal analysis and open-source resources, this study contributes to the development of innovative methodologies in CALL, based on research-informed teaching practices
A Bayesian approach to modeling fast chargers functionality for grid frequency support
No full textAs governments around the world commit to achieving net zero emissions, the upcoming years will witness a significant increase in fully electric vehicles (EVs), predominantly supported by DC fast charging (DCFC) infrastructure. While DCFCs are primarily used to provide rapid and convenient charging, their widespread adoption highlights the need to integrate them into power system operations, such as frequency control. Thus, it is crucial to estimate the level of support that DCFCs can provide for frequency control, especially in future EV adoption plans subject to data uncertainty. Existing methods, including individual EV modeling and clustering-based approaches, fall short due to high data requirements and incompatibility with transmission network models. Aggregated modeling and averaging techniques, while simpler to apply, overlook critical factors such as EV owner preferences and primarily focus on the vehicles rather than the chargers. Additionally, these methods are primarily designed for low-power chargers intended for prolonged charging sessions with more predictable plug-in patterns and are not suitable for DCFCs, where the behavior of EV owners is more dynamic and subject to higher uncertainties. EV owners are also more sensitive to meeting their expectations when using DCFCs rather than low-power chargers. To address these limitations, this paper developed a Bayesian probabilistic equivalent capacity model for DCFCs. This model uniquely incorporates deep discharge vulnerability, mobility requirements, and owner preferences, providing a comprehensive assessment of DCFC frequency support. A novel concept called mileage loss (ML) is also introduced, enabling DCFCs to contribute to frequency control. It also allows system operators and EV aggregators to analyze the risk-taking and risk-averse behaviors of EV owners in vehicle-to-grid (V2G) mode. Furthermore, the proposed model is validated using the Australian frequency regulation framework, demonstrating its scalability and applicability. The case study results demonstrate that the proposed modeling method can achieve a high level of accuracy, with an estimation precision of up to 97.7 % for the aggregated DCFC power capacity
A Low-temperature Hydrothermal Approach to Fabricate Bactericidal Nanostructures on 3D-Printed Polylactic Acid Surfaces Against Pseudomonas Aeruginosa Bacteria
No full textSurface modification offers the opportunity to create nano-topographies on various materials. Such modifications are widely applied to enhance solar energy absorption and electromagnetic shielding. An additional high-impact application involves nano-topographic surfaces designed to combat biofilm formation by nature inspired surfaces, such as cicada and dragonfly wings, which physically lyse bacterial cells. Such mechano-bactericidal surfaces have attracted growing interest over the past decade, and ongoing efforts translate these structures into medical and industrial applications. While considerable progress has been achieved with metallic and ceramic surfaces, advancements in polymers remain limited, despite their widespread use. In this study, a low-temperature hydrothermal approach successfully modified the 3D-printed polylactic acid (PLA) surface to nano-topographies. Although the literature describes a limited number of strategies for producing nanostructures on 3D metallic surfaces, fabricating such structures on 3D polymeric surfaces remains challenging using conventional methods. This study demonstrates the successful fabrication of distinct nanostructures on both the top and bottom surfaces of a 3D-printed PLA substrate. The produced surfaces were characterised via scanning electron microscopy (SEM), atomic force microscopy (AFM), SEM-Energy Dispersive Spectroscopy (EDS), X-ray Photoelectron Spectroscopy (XPS), and Fourier-transform infrared spectroscopy (FTIR). The bactericidal efficacy (BE) was quantified via LIVE/DEAD™ BacLight™ bacterial viability assay with inverted fluorescence microscopy images. Among the developed structures, Nano-Pockets with an average pore diameter of ∼275 nm exhibited the highest BE, achieving a 48.8% reduction in Gram-negative Pseudomonas aeruginosa viability within 1 hour of incubation. This approach, therefore, lays the foundation for fabricating nanostructures on 3D-printed polymeric surfaces with complex geometries
Bio-based, selenium/Schiff base-containing co-curing agent enables flame-retardant, smoke-suppressive and mechanically-strong epoxy resins
No full textEpoxy resins (EPs) have excellent overall properties, but their inherent flammability severely limits their industrial applications. Most existing flame-retardant methods rely on phosphorus-based compounds. However, they suffer from bioaccumulation and potential risks to ecosystems and human health. The development of safer and more sustainable phosphorus-free flame-retardant EP systems based on biomass is highly promising but challenging. Herein, a selenium/Schiff base-containing, bio-based flame retardant (SNC) has been successfully synthesized and applied as a multifunctional co-curing agent in EP. The resultant EP containing 15 wt% SNC (EP-SNC15) exhibits enhanced mechanical strength. Additionally, EP-SNC15 achieves a high limiting oxygen index (LOI) of 31.8 % and a vertical burning (UL-94) V-0 rating, and its peak heat release rate (PHRR) and total heat release (THR) decrease by 73.3 % and 56.3 % compared to EP, respectively. Therefore, the excellent flame retardancy and mechanical properties of EP-SNC15 make it superior to previously reported phosphorus-free flame-retardant epoxy systems. The improvement in flame retardancy is attributed to the synergistic catalytic carbonization of selenium-containing and Schiff base groups in SNC in the condensed phase and the free radical quenching of organoselenium groups in the gas phase. Therefore, this work provides a novel design strategy for the development of next-generation, flame-retardant and bio-based EPs
Synergistic Optimization of Electronic and Thermal Properties in Single-Stage GeTe Thermoelectric Devices
No full textThermoelectric generators offer a promising route for converting low-grade waste heat into electricity; however, their practical deployment remains limited by their low conversion efficiency (η). Herein, we report a monolithic, single-stage, 7-pair device based on lead-free GeTe alloys that achieves a remarkable η of 14.1% and a power density of 2.19 W cm–2 at a temperature difference of 460 K. This breakthrough stems from the synergistic integration of ordered-disordered state modulation and multiband engineering. Specifically, multiband evolution suppresses intervalley scattering while promoting valence-band convergence, optimizing the balance between the carrier effective mass and mobility to elevate the weighted mobility and average power factor. The concurrent introduction of abundant lattice defects and localized disorder drives the lattice thermal conductivity toward the amorphous limit. Consequently, achieving a maximum ZT of 2.6 at 703 K with an average ZT of 1.7 across 303–803 K is realized. This study establishes a promising design paradigm for high-performance lead-free thermoelectric technologies