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The Prevalence and Potential Problem of Cuteness in Zoomorphic Robots
No full textCuteness is a powerful aesthetic, and psychological research shows that cute things such as infants, baby animals, and toys capture and secure our attention, promote nurturing behaviour, and influence our preferences. Therefore, cuteness is a common design outcome in many consumer products, including robotics. However, we suggest that making cute zoomorphic robots may not be without its issues due to the complexities introduced by the analogies they make to various animals. We summarise the impact of cuteness in animals and robotics and analyse the intersection of the two domains by comparing the presence of baby schema features in different canine zoomorphic robots and dog breeds. Finally, we speculate on the benefits and drawbacks to cute zoomorphic robots, and provide suggestions for a new design approach that centres animals’ well-being. The aim of this work is to synthesise research on cuteness from different disciplines and prompt robot designers to be more conscious of cuteness and its potentially detrimental consequences in zoomorphic robots
Direct Quantification of Coal Pore Dynamics during Methane Depletion via Low-Field Nuclear Magnetic Resonance
No full textThis study presents a novel low-field nuclear magnetic resonance (LF-NMR) framework to directly quantify sorption-induced pore strain and pore compressibility in coal reservoirs and thereby provides key parameters for predicting permeability during coalbed methane (CBM) production. Three coal samples of varying ranks (high-, middle-, and low-rank) were subjected to controlled methane adsorption/desorption and confining stress experiments under constant effective stress. By correlating transverse relaxation time (T2) spectra with methane phase dynamics, we resolved adsorbed gas (micropores) and free gas (mesopores, macropores, fractures) contributions, enabling real-time tracking of pore deformation. Analysis on the measurements reveals that the sorption-induced pore volumetric strain displays a linear relationship with adsorption gas content, ranging from 0.0108 to 0.0613 g·cm–3; the range of pore compressibility variation was calculated using an exponential relationship between transport pore volume and effective stress, and it ranges from 0.0509 to 0.0902 MPa–1. These two factors directly characterize the volumetric strain of the methane transport space within the coal reservoir, providing a direct, assumption-free approach to characterize pore-scale mechanics, particularly for heterogeneous coal reservoirs
Seismic performance of demountable diagonal connection RCS joints:Experimental and theoretical study
No full textThis study proposed demountable diagonal connection joints consists of Reinforced Concrete columns and Steel beam (RCS) joint with the slab and systematically analyzed its seismic performance through cyclic loading tests. The investigation focuses on the effects of different steel beam flange thicknesses and concrete strengths on key seismic indicators, including hysteretic curves, stiffness degradation, energy dissipation, and ductility. The experimental results demonstrate that the joint with the slab outperforms traditional casting joints in terms of stiffness degradation, energy dissipation capacity, and ductility coefficient. The joint exhibits excellent hysteretic behavior within a drift ratio range of 0.5 %-2 %. Compared to the cast-in-place joint, the demountable specimens achieved improved energy dissipation and comparable deformation capacity while enabling post-loading disassembly without major damage. Additionally, a formula for calculating the initial rotational stiffness of the joint is proposed using the component method, with the calculated values closely matching the experimental results.</p
Seismic performance of demountable diagonal connection RCS joints:Experimental and theoretical study
No full textThis study proposed demountable diagonal connection joints consists of Reinforced Concrete columns and Steel beam (RCS) joint with the slab and systematically analyzed its seismic performance through cyclic loading tests. The investigation focuses on the effects of different steel beam flange thicknesses and concrete strengths on key seismic indicators, including hysteretic curves, stiffness degradation, energy dissipation, and ductility. The experimental results demonstrate that the joint with the slab outperforms traditional casting joints in terms of stiffness degradation, energy dissipation capacity, and ductility coefficient. The joint exhibits excellent hysteretic behavior within a drift ratio range of 0.5 %-2 %. Compared to the cast-in-place joint, the demountable specimens achieved improved energy dissipation and comparable deformation capacity while enabling post-loading disassembly without major damage. Additionally, a formula for calculating the initial rotational stiffness of the joint is proposed using the component method, with the calculated values closely matching the experimental results.</p
Towards Adversarial Policy Discovery via Evolutionary Program Synthesis
No full textRecent work has shown that even superhuman reinforcement learning (RL) policies can be vulnerable to adversarial agents. Most existing approaches for generating such adversaries rely on RL-based methods similar to those used to train the original policy under attack, potentially limiting the diversity of discovered exploits. We present a proof of concept showing that genetic programming (GP) can evolve symbolic adversarial agents that expose flaws in trained RL policies. By framing adversarial discovery as a program synthesis task, our approach enables broader and more interpretable search than conventional methods. We evaluate this approach in two competitive game environments against agents trained by OpenAI, showing that GP-evolved agents can outperform RL-based adversaries. These early results suggest that GP is not only effective for discovering unconventional exploits, but may serve as a useful stress-testing tool for RL systems more generally
A replaceable corrugated web shear link for seismic resilience of double-column bridge bent: Experimental, numerical, and theoretical study
No full textThis study introduces an innovative replaceable corrugated steel web (CSW) shear link system for double-column bridge bents, designed to enhance seismic performance and enable rapid post-earthquake recovery. Through a comprehensive experimental program, eight full-scale specimens with varying geometric parameters (span-to-height ratios: 1.46–3.89; corrugation angles: 30–60°; orientation configurations) were subjected to quasi-static testing to evaluate their seismic behaviors, including damage process, energy dissipation, strength, stiffness and ductility. The experimental investigation revealed four characteristic failure modes: (1) CSW tearing, (2) coupled CSW and flange buckling, (3) combined CSW tearing and flange-to-web weld fracture, and (4) endplate-to-CSW connection failure. Key findings demonstrate that specimens with span-to-height ratios below 1.0 and corrugation angles exceeding 45° exhibit superior hysteretic performance, with the vertical-oriented specimen (VL1.89-θ45-a0.29) achieving optimal energy dissipation per unit volume (4.34 × 107J/m3) at the expense of accelerated stiffness degradation (60 % reduction after 3 % drift). Analytical results indicate a nonlinear relationship between ductility enhancement and span-to-height ratios, with measured improvement by 40 % as L/H increased from 1.46 to 3.89. Complementing the experimental work, advanced finite element models incorporating ductile fracture criteria were developed, achieving a 1.06 % correlation with test results. The study further proposes and validates simplified design equations for yield strength and lateral stiffness of CSW links, providing practical tools for engineering implementation. These findings establish a technical foundation for developing resilient bridge systems with rapid recovery capabilities
Shear behavior of SFRC beams reinforced with FRP stirrups:Experimental and analytical investigations
No full textThe shear properties of eight steel fiber reinforced concrete (SFRC) beams reinforced with glass fiber reinforced polymer (GFRP) stirrups, referred to as GFRP-R-SFRC beams, are reported under four-point loading. Two parameters of the volume fraction of steel fibers (Vf) and the shear span ratio (λ) are considered, and their effects on the failure mode, mid-span deflection, crack width, strains of SFRC and longitudinal rebars, and shear capacity of GFRP-R-SFRC beams are then investigated. As the λ increases from 1.5 to 3.0, the GFRP-R-SFRC beams sequentially experience three failure modes: diagonal compression failure, shear compression failure, and diagonal tension failure. The incorporation of 1.5% steel fibers results in a reduction of the maximum deflection, maximum crack width, rebar strain and concrete strain by 2.3%, 16.8%, 15.7%, and 5.1%, respectively, indicating an enhancement in the post-cracking stiffness of GFRP-R-SFRC beams. Due to the crack-bridging effect of steel fibers, the average strain, maximum strain, and utilization ratio of GFRP stirrups increase with the increase of Vf. The shear capacity of GFRP-R-SFRC beams increases by 25.6% as the Vf increases from 0% to 1.5%, and the enhancement in shear capacity (25.6%) due to the addition of steel fibers shows a similar effect to that observed in conventional SFRC beams (12.7%). However, an increase in λ leads to a decrease in shear capacity, as the failure mode of the beam shifts from a shear-dominated pattern to a flexure-dominated pattern, which is similar with conventional SFRC beams. Considering the positive contribution of steel fibers, a modified computational model is proposed for evaluating the shear capacity of FRP-R-SFRC beams. A good agreement between the predicted and experimental results is shown.</p
Categorising residential energy demand datasets in the UK
No full textAccess to high-quality residential energy demand data is crucial for research and policymaking. In the transition to a modern, digitalised energy system, datasets should be visible and accessible to end users. However, the absence of standardised data release guidelines and metadata standards creates challenges in data visibility, accessibility, and comparability. These challenges lead to repetitive and time-consuming searches for relevant datasets. This study examines twenty-four UK residential energy demand datasets, highlighting inconsistencies in how they are catalogued, documented, and structured. A novel classification scheme is introduced to systematically document dataset attributes, scope, and contextual variables. Subsequent categorisation of the twenty-four datasets using the classification scheme enhances data discovery, comparability, and consistency, while also identifying gaps. This article also addresses the evolving landscape of residential energy demand datasets and policy and the role of data in supporting efforts to decarbonise the building stock. This work highlights the need for greater standardisation and accessibility, emphasising the importance of harmonised metadata, improved documentation, and cross-dataset compatibility to support future research and policymaking
Impaired nuclear PTEN function drives macrocephaly, lymphadenopathy and late-onset cancer in PTEN Hamartoma Tumour Syndrome
No full textPTEN hamartoma tumour syndrome (PHTS), a rare disease caused by germline heterozygous PTEN variants, is associated with multi-organ/tissue overgrowth, autism spectrum disorder and increased cancer risk. Phenotypic variability in PHTS is partly due to diverse PTEN variants and the protein's multifaceted functions. PTEN is primarily a phosphatidylinositol(3,4,5)trisphosphate (PIP3) phosphatase regulating PI3K/AKT signalling but also maintains chromosomal stability through nuclear functions such as double-stranded (ds)DNA damage repair. Here, we show that PTEN-R173C, a pathogenic variant frequently found in PHTS and somatic cancer, has elevated PIP3 phosphatase activity that effectively regulates canonical PI3K/AKT signalling. However, PTEN-R173C is unstable and excluded from the nucleus. We generated Pten+/R173C mice which developed few tumours during their lifetime, aligning with normal PI3K/AKT signalling. However, they exhibited lymphoid hyperplasia, macrocephaly and brain abnormalities, associated with impaired nuclear functions of PTEN-R173C, demonstrated by reduced dsDNA damage repair. We integrated PHTS patient data with our mouse model results, and propose that defective nuclear functions of PTEN variants can predict the onset of PHTS phenotypes and that late-onset cancer in these individuals may arise from secondary genetic alterations, facilitated by compromised dsDNA repair
Introduction to artificial intelligence in chemical engineering
No full textThis book chapter examines how artificial intelligence (AI) has been used in chemical engineering, emphasizing its development, present uses, and prospects for the future. Examining machine learning (ML) and deep learning (DL) approaches for fault identification and process optimization is one of the goals, as is tackling data management issues. The main techniques used in this study are reinforcement learning for real-time process control, supervised learning for property prediction, and unsupervised learning for anomaly detection. Notably, the study uses methods like LIME for model interpretability and highlights the significance of explainable AI to promote trust in AI systems. According to the research, putting AI into practice can increase operational efficiency by up to 30%, lower expenses by about 20%, and increase safety by enabling proactive monitoring. The innovative aspect of this work is its all-encompassing framework, which incorporates AI techniques specifically designed to address the difficulties in chemical engineering. The creation of hybrid AI systems that integrate ML with process simulation tools is one example of future applications that will advance sustainable chemical manufacturing methods and allow for real-time decision-making. The chapter also emphasizes how important data management techniques, like feature engineering and data cleaning, are to the successful application of AI. Additionally, it tackles ethical issues, like AI bias and accountability, guaranteeing that AI solutions are not only efficient but also equitable and open. The study’s conclusions offer a fundamental understanding of how to use AI in the chemical industry with the ultimate goals of process optimization, innovation promotion, and navigating the intricacies of ethical and regulatory issues
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