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Machine learning based determination of lateral displacement of fully grouted reinforced shear masonry walls under in-plane axial loading
No full textReinforced shear masonry (RSM) walls are critical for resisting lateral loads, but accurately estimating their displacement capacity is challenging due to single reinforcement layer. This study addresses this issue by developing and comparing three machine learning algorithms: Convolutional Neural Network (CNN), Gene Expression Programming (GEP), and Tree-Structured Parzen Estimator-Extreme Gradient Boosting (TPE-XGB), to predict the lateral displacement of fully grouted RSM walls. A database of 152 experimental instances was compiled, incorporating variables like masonry strength, reinforcement ratios, axial load ratio, and shear stress demand. The TPE-XGB model demonstrated the highest predictive accuracy (R² = 0.995), followed by CNN, while GEP provided a less accurate but interpretable empirical equation (R² = 0.884). Comparisons with simpler regression models confirmed the efficiency of the machine learning approaches. To enhance transparency, Shapley Additive (SHAP) and Individual Conditional Expectation (ICE) analyses were conducted, identifying aspect ratio, shear stress demand, and grouted masonry strength as key factors influencing displacement. An explainable computational tool was also created to facilitate practical implementation, enabling engineers to accurately predict the lateral displacement behavior of RSM walls
Effective wheat straw pre-treatment and saccharification using p-toluenesulfonic acid (pTSA)-based deep eutectic solvents and microwave heating
Get PDFLignocellulose is a promising source of biofuels by enzymatic conversion; however, its valorisation is often restricted due to the lignin present. This study investigated a novel and effective microwave-assisted ternary Deep Eutectic Solvent (DES) system composed of p-toluenesulfonic acid (pTSA), glycerol, and choline chloride (ChCl) to enable lignin recovery and increase polysaccharides bioconversion from wheat straw (WS), an industrial waste stream. Specifically, four DESs with varying pTSA content were evaluated. Higher pTSA also significantly improved delignification yield (up to 88.1 %), although at the expense of sacrificing the cellulose and hemicellulose contents. The DES with pTSA: glycerol: ChCl at 1:2:2 M ratio (DES 3) achieved the best balance, retaining 77.8 % cellulose and 49.4 % xylan while removing 78.3 % lignin after microwave heating at 120 °C for 20 min, and enabling the highest glucose (95.6 %) and xylose (52.0 %) yields after enzymatic hydrolysis. Structural analysis (FTIR and XRD) confirmed cellulose integrity and reduced crystallinity after DES pre-treatment, supporting improved enzymatic digestibility. This DES also recovered 51.7 % of total lignin with minimised lignin condensation and maintained ∼68 % of its initial delignification efficiency and >90 % of its initial saccharification performance after two recycling cycles, demonstrating good solvent reusability. Most important, this study elucidated the role of pTSA in determining DES-microwave interaction through dielectric property measurement and linked it with the microwave heating profiles. DES with higher pTSA exhibited a greater difference in delignification efficiency between microwave and conventional heating, due to more pronounced selective heating mechanisms
A recipe for success: exploratory studies of the predictors of cultivated meat provision by parents of children 6-10 and 11-15 years of age.
Get PDFDeformation-Recovery diffusion model (DRDM):Instance deformation for image manipulation and synthesis
Get PDFIn medical imaging, diffusion models have shown great potential for synthetic image generation. However, these approaches often lack interpretable correspondence between generated and real images and can create anatomically implausible structures or illusions. To address these limitations, we propose the Deformation-Recovery Diffusion Model (DRDM), a novel diffusion-based generative model that emphasizes morphological transformation through deformation fields rather than direct image synthesis. DRDM introduces a topology-preserving deformation field generation strategy, which randomly samples and integrates multi-scale Deformation Velocity Fields (DVFs). DRDM is trained to learn to recover unrealistic deformation components, thus restoring randomly deformed images to a realistic distribution. This formulation enables the generation of diverse yet anatomically plausible deformations that preserve structural integrity, thereby improving data augmentation and synthesis for downstream tasks such as few-shot learning and image registration. Experiments on cardiac Magnetic Resonance Imaging and pulmonary Computed Tomography show that DRDM is capable of creating diverse, large-scale deformations, while maintaining anatomical plausibility of deformation fields. Additional evaluations on 2D image segmentation and 3D image registration tasks indicate notable performance gains, underscoring DRDM’s potential to enhance both image manipulation and generative modeling in medical imaging applications. The project page: https://jianqingzheng.github.io/def_diff_rec/
Maximizing Ball Movement Unpredictability in Association Football: A Rényi Entropy-Based Approach to Optimizing Event Distribution Randomness
Get PDFModern football prioritizes team play and tactical strategies over individual brilliance. However, its low-scoring nature makes evaluating team performance challenging. Unpredictable ball movement enhances offensive play while complicating defensive setups. To better capture this dynamic nature, authors’ prior work has proposed entropy-based time-series metric to assess unpredictable ball movement by quantifying Spatial Event Distribution Randomness (EDRan). However, some teams may prefer to dominate specific areas with unpredictability, while others utilize the entire field. Existing literature has not examined whether emphasizing dominant (frequently used field regions for ball movement) or considering all regions equally, including rarely used areas, is a more effective approach for computing randomness in event distribution. Moreover, existing research has not investigated the underlying patterns of event distribution randomness, particularly how these variations differ between winning and losing teams, both in terms of overall field coverage and concentration within dominant regions. This study addresses these gaps by analyzing event distribution randomness using Rényi entropy with varying alpha values (0).Correlation analysis indicated that assigning equal weight to all field regions, including rarely used areas, with Max entropy (alpha) was most strongly associated with match-winning performance. In men’s data, machine learning models trained with alpha and 0.5 achieved statistically significant improvements over models trained with the traditionally used Shannon entropy (alpha). These results suggest that unpredictability distributed across the entire field, maximizing the use of diverse regions, is more strongly associated with success than randomness restricted to dominant areas. The best-performing model, obtained with alpha, significantly outperformed both the baseline and existing models in the literature, achieving an accuracy of 80.61% in predicting match winners
Personalised antiemetic prophylaxis with NEPA for patients at high risk of chemotherapy-induced nausea and vomiting receiving moderately emetogenic chemotherapy: results from the randomised, multinational MyRisk trial
Get PDFBackground: Patients receiving moderately emetogenic chemotherapy (MEC) are commonly prescribed a 5-hydroxytryptamine-3 (5-HT3) receptor antagonist (RA) and dexamethasone (DEX) as standard-of-care (SOC) antiemetic prophylaxis. However, in patients with an elevated risk of chemotherapy-induced nausea and vomiting (CINV) due to individual risk factors, prophylaxis with an neurokinin-1 (NK1) RA-containing regimen may optimise their antiemetic prevention. To address this unmet need for a more personalised antiemetic strategy, the MyRisk trial incorporated a predictive risk factor algorithm to select patients at increased risk of CINV who may benefit from enhanced antiemetic prophylaxis. Patients and methods: MyRisk was a phase IV, randomised, open-label, multicentre, multinational trial. Adult patients scheduled to receive three cycles of MEC with a high-risk CINV score were randomly assigned to NEPA (a fixed combination of an NK1RA, netupitant, and 5-HT3RA, palonosetron) + DEX or SOC. The CINV risk score was calculated based on an algorithm that considered seven risk factors. The primary endpoint was complete response (CR: no emesis/no rescue medication) during the overall phase (0-120 h) across three consecutive cycles. Results: Of 401 randomly allocated patients, 388 were included in the efficacy analysis. The most common cancers were colorectal and lung; oxaliplatin and carboplatin were the most common MECs. Patients randomly assigned to NEPA were significantly more likely to experience a CR compared with SOC (odds ratio 1.67, 95% confidence interval 1.12-2.49, P = 0.012). The NEPA group had a significantly higher probability of CR, no nausea, no emesis, and complete protection (81.0%, 63.7%, 95.4%, and 71.8%, respectively) compared with the SOC arm (71.8%, 54.9%, 86.7%, and 62.4%, respectively) across three cycles of chemotherapy. Conclusions: When individual risk factors are considered before MEC, a three-drug regimen including NEPA provides superior CINV prevention across multiple cycles compared with the standard two-drug approach. These findings underscore the value of personalised risk-adapted antiemetic strategies and have practice-changing potential for optimising antiemetic control
Simplifying Depression Diagnosis: Single-Channel EEG and Deep Learning Approaches
No full textMajor depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts individuals' well-being and is often diagnosed at advanced stages, increasing the risk of suicide. Current diagnostic practices, which rely heavily on subjective assessments and patient self-reports, are often hindered by challenges such as under-reporting and the failure to detect early, subtle symptoms. Early detection of MDD is crucial and requires monitoring vital signs in daily living conditions. The electroencephalogram (EEG) is a valuable tool for monitoring brain activity, providing critical information on MDD and its underlying neurological mechanisms. While traditional EEG systems typically involve multiple channels for recording, making them impractical for home-based monitoring, wearable sensors can effectively capture single-channel EEG data. However, generating meaningful features from these data poses challenges due to the need for specialized domain knowledge and significant computational power, which can hinder real-time processing. To address these issues, our study focuses on developing a deep learning model for the binary classification of MDD using single-channel EEG data. We focused on specific channels from various brain regions such as central, frontal, occipital, temporal, and parietal. Our study found that the channels Fp1, F8 and Cz achieved an impressive accuracy of 90% when analyzed using a Convolutional Neural Network (CNN) with leave-one-subject-out cross-validation on a public dataset. Our study highlights the potential of utilizing single-channel EEG data for reliable MDD diagnosis, providing a less intrusive and more convenient wearable solution for mental health assessment
Information processing and enabling technologies for agricultural energy Internet applications
Get PDFThis study provides a systematic exposition of the Agricultural Energy Internet (AEI), offering an in-depth analysis within the context of smart agriculture. This work elucidates the evolutionary pathway and emerging trends of AEI, along with the technologies underpinning its implementation. Through a systematic analysis of literature published over the past five years, the study focuses on three key research domains: (1) agricultural electrification, (2) rural energy systems, and (3) smart agriculture, thereby mapping the current research landscape of AEI. Representative studies across different developmental stages are examined to trace the evolution of AEI and its future directions, revealing a clear shift toward integrated multi-scale system-level energy modeling and large-scale AI-driven optimization. This study systematically delineates the technical architecture and evolutionary trajectory of the AEI. Finally, drawing on recent high-impact publications, three frontier research directions are identified: (1) generative AI for smart agriculture, (2) high-efficiency carbon capture technologies, and (3) low-cost energy harvesting systems
Sensory sensitivity and visual discomfort are not associated with altered gamma oscillations; a test of the excitation-inhibition hypothesis
Get PDFMany people experience aversive hypersensitivity (discomfort/visual stress) to stimuli such as bright lights, striped patterns, strobing, motion or complex visual scenes such as supermarkets. Such sensory hypersensitivity is often associated with one or more of a range of neurological, psychiatric and neurodevelopmental conditions or neurodivergence. The cortical mechanisms of sensory hypersensitivity, and reasons why it occurs with such a range of conditions, remain unknown. For three decades theories have focussed on excitation/inhibition balance, where visual discomfort reflects over-excitation relative to inhibition. Visual gamma oscillations induced by viewing stripes are an accepted index of excitation/inhibition, and are successfully modelled by a cortical circuit. Visual gamma is therefore predicted to be altered in people with high visual discomfort. We tested this in two studies. The first used circular moving gratings to evoke visual gamma, alongside self-reported scales for sensory sensitivity and for discomfort induced by viewing images (N=166). We found no correlation of subjective sensitivity or discomfort with gamma frequency or amplitude (all r<0.1), or with the modelled excitation/inhibition parameters. In study 2, we recruited two groups of participants with high and low sensitivity to visual stripes (N=23,27), and induced gamma with gratings of four different spatial frequencies. We found no group differences in gamma frequency, amplitude or modelled parameters. We conclude that visual discomfort is not simply explained by higher excitation/inhibition ratio in visual cortex, despite the dominance of this assumed explanation
Enhancing Early Medical Education Through Patient Engagement: Creation of a Toolkit Informed by Experts by Experience
Get PDFIncorporating the patient voice into health professional education enhances empathy, promotes person-centred care and enriches learning. This cocreation article describes the development of a practical, feedback-informed toolkit to support early medical education through expert by experience (EBE) engagement. EBEs from The Silverlining Brain Injury Charity contributed to the design via a qualitative study using open-ended questionnaires. Thematic analysis identified six key themes: the importance of respectful engagement, logistical challenges, clarity of session expectations, recognition of EBE expertise, personal benefits of participation and ethical concerns. EBEs emphasised the need for dignity, structured facilitation, emotional safeguards and flexible delivery methods. The resulting toolkit is mapped directly to these themes. It includes guidance on planning, facilitation, ethical considerations, orientation and evaluation. Designed for Level 3 of the patient engagement spectrum, where EBEs share lived experiences in faculty-facilitated teaching, the toolkit promotes meaningful, sustainable involvement. It responds to growing calls for coproduction in health education and serves as a replicable model for integrating patient insights into curriculum design and delivery. While based on a small, specialised sample, the depth and clarity of EBE feedback offer strong foundations for this resource. Future work should explore its adaptability across different healthcare disciplines and settings. To increase accessibility, the toolkit is available in two formats: as a shareable webpage and a navigable PDF document. This approach enables wider reach and sustained use by educators, ensuring that patient voices remain central to shaping future health professionals
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