Browse Articles

Design and fabrication of Hong Kong’s first constructional 3D-printed metal pavilion ‘Weaving Love’

Abstract ‘Weaving Love’ is Hong Kong’s first outdoor pavilion constructed using the constructional 3D metal printing through Wire Arc Additive Manufacturing (WAAM) technology, making a transformative milestone in the application of this emerging technology for large-scale construction in the region. This paper documents the entire process—from the concept and design to fabrication and construction—of the “Weaving Love” pavilion, a constructional 3D-printed metal structure situated at the New Immigration Headquarters of Hong Kong. The project demonstrates the seamless integration of advanced WAAM technology, innovative parametric design, and collaborative efforts among government, industry, and academia. By leveraging these advanced technologies, the project team has created a structure that is not only visually stunning but also environmentally friendly and cost-effective—achievements that would have been unattainable using conventional construction methods. The project achieved significant reductions in construction time, cost, and material waste while pushing the boundaries of architectural design and structural engineering. The structure is one of the largest 3D-printed steel structures in the Hong Kong region, designed in accordance with established codes of practice, just like conventional steel structures. This pioneering project successfully utilized WAAM technology to bring the innovative design of an artistic expression to fruition, incorporating advanced structural analysis methods, optimization techniques, and supplementary physical tests. This paper presents summarized project data and methodological frameworks for implementing WAAM technology in construction applications, thereby contributing to the advancement of WAAM technology in construction industry.

Osteoporosis prediction from hand X-ray images using segmentation-for-classification and self-supervised learning

Intelligent low carbon reinforced concrete beam design optimization via deep reinforcement learning

Evaluation of operational challenges and technological performance of wastewater treatment plants in Addis Ababa

Prevalence and associated factors of acne among patients attending dermatovenereology outpatient department at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia, 2023

Machine learning enhanced design and knowledge discovery for multi-junction photonic power converters

A quantum approximate optimization method for finding Hadamard matrices

Shared immune biomarkers in necrotizing enterocolitis and neonatal sepsis identified via bioinformatics and machine learning

Population-based validation of a frailty index using electronic regional healthcare records for public health use

Abstract Frailty indices derived from electronic health records offer an efficient approach to identify vulnerable individuals in the general population. We aimed to validate the electronic-regional healthcare database frailty index (e-RHD-FI) in the general adult population, describe its distribution by sex and age, evaluate its predictive validity for mortality, hospitalization and fragility fractures, and assess the performance of frailty cut-off points. We conducted a population-based study of 8,404,004 adult beneficiaries of the Lombardy Regional Health System. The e-RHD-FI was calculated from 40 health deficits using electronic health records from 2008 to 2018. We assessed its distribution, predictive validity for 1-year mortality, hospitalizations and fragility fractures through multi-state analysis and multivariable models evaluating performance across subgroups. The e-RHD-FI distribution was highly asymmetrical (median 0.0125, first-third quartiles 0–0.0375), with 45.8% of adults having no deficits. The index was higher in older adults. Each 0.1-point increment in e-RHD-FI was associated with doubled 1-year mortality risk (HR 2.12, 99% CI 2.11–2.14), 2.5-fold increased hospitalization rate (IRR 2.51, 99% CI 2.49–2.53), and a 55% higher risk of fragility fracture (HR 1.55, 99% CI 1.53–1.57). The AUC for 1-year mortality was 0.883 (99% CI 0.881–0.884). The e-RHD-FI demonstrates strong predictive validity for adverse outcomes in the general population and can effectively identify at-risk individuals for targeted interventions.

Investigating orientation adaptation following naturalistic film viewing

Abstract Humans display marked changes to their perceptual experience of a stimulus following prolonged or repeated exposure to a preceding stimulus. A well-studied example of such perceptual adaptation is the tilt-aftereffect. Here, prolonged exposure to one orientation leads to a shift in the perception of subsequent orientations. Such a capacity to adapt suggests the tuning of the visual system can change over time in response to our current visual environment. However, it remains unclear to what extent adaptation occurs in response to statistical regularities of features present in naturalistic scenes, such as oriented contrast. We therefore investigated orientation adaptation in response to natural viewing of filtered live-action film stimuli. Within a session, participants freely viewed 45 min of a film which had been filtered to include increased contrast energy within a specified orientation band (0°, 45°, 90°, or 135°; i.e., the adaptor). To measure adaptation effects, the film was intermittently interrupted to have participants perform a simple orientation judgement task. Having participants complete behavioural trials throughout the testing session, including 45 min of total adaptation time, allowed investigation of the accumulation of response biases and changes in such biases over the course of the session. We found very little evidence of adaptation across our conditions. Indeed, in the very few conditions where significant adaptation was observed, these effects were much weaker than those observed under typical tilt-aftereffect paradigms. Further, within a single session, we observed inconsistent development of adaptation effects. The current findings therefore suggest very minimal and, where present, inconsistent effects of adaptation in response to naturalistic viewing conditions. The divergence of our results from those predicted by prior studies using minimalistic studies, and suggests consideration of further barriers to understanding perceptual adaptation as experienced in nature are needed.

Immobilized Ni and Cu on eucalyptus/MGO as a green heterogeneous catalyst for tetrazoles synthesis through RSM optimization

Optimization of carbon capture and storage transportation networks in Thailand using the artificial hummingbird algorithm

Assessment of oral morbidity and self rated health in the Indian geriatric population with a propensity score matched approach

Predicting and understanding non-adherence in chronic disease: cross-cohort validation and structural equation modeling of the SPUR 6/24 tool

Abstract The SPUR tool measures the risk of non-adherence for patients with chronic disease, as well as measuring the relative importance of thirteen behavioral drivers contributing to that risk. Over a period of four years, five different cohorts of patients in three countries and three different pathologies were studied to contribute to the elaboration and refinement of two patient-reported adherence measures: SPUR 6 and SPUR 24. This article examines the results of retrofitting of both of these tools to earlier patient cohorts as well as analyzing the pooled dataset via the use of both tools in order to further study the predictive potential of both. A further analysis was carried out using structural equation modeling both to test the structural validity of the SPUR tools and to examine both indirect and direct influence of the thirteen drivers on patient behavior.Direct comparisons of the SPUR tools to other patient-reported adherence measures across datasets and across the pooled dataset was carried out by analysis of Spearman’s ranked correlation coefficients. The structural equation modeling was carried out using path analysis based on the decision-making schema hypothesized in the foundational SPUR article.The retrofitted analysis and the pooled data analysis both support the use of SPUR 6 and SPUR 24 to assess the risk of non-adherence of patients with chronic disease with respect to other widely used patient reported adherence measures. The structural equation modeling reinforced the hypothesis that the social and psychological drivers of SPUR have a significant indirect impact on non-adherence risk via the rational and usage drivers as well as their direct impact on non-adherence risk.SPUR 6 and SPUR 24 have demonstrated predictive value in assessing the risk of patient non-adherence as compared to their predecessors as well as to other widely-used patient adherence measures, across countries and pathologies. The social and psychological drivers of SPUR seem to drive behavior largely through their influence on rational and usage factors, indicating a cognitive rationalization process . These insights have direct implications for communication strategy towards patients in efforts to enhance medication adherence.

Application of waste from biodiesel synthesis for the production of fuel pellets and biogas

7T MRI and histology reveal early tissue and perfusion changes after radiofrequency ablation in a murine colorectal cancer model

Indoor WiFi fingerprint localization based on dual population PSO of stacked autoencoder and multi label classification

Exploring burnout, resilience and the coping strategies among critical healthcare professionals in post-COVID Taiwan

The sensory profile of students with probable developmental coordination disorder (DCD)

Temporal convolutional transformer for EEG based motor imagery decoding

Abstract Brain-computer interfaces (BCIs) based on motor imagery (MI) offer a transformative pathway for rehabilitation, communication, and control by translating imagined movements into actionable commands. However, accurately decoding motor imagery from electroencephalography (EEG) signals remains a significant challenge in BCI research. In this paper, we propose TCFormer, a temporal convolutional Transformer designed to improve the performance of EEG-based motor imagery decoding. TCFormer integrates a multi-kernel convolutional neural network (MK-CNN) for spatial-temporal feature extraction with a Transformer encoder enhanced by grouped query attention to capture global contextual dependencies. A temporal convolutional network (TCN) head follows, utilizing dilated causal convolutions to enable the model to learn long-range temporal patterns and generate final class predictions. The architecture is evaluated on three benchmark motor imagery and motor execution EEG datasets: BCIC IV-2a, BCIC IV-2b, and HGD, achieving average accuracies of 84.79, 87.71, and 96.27%, respectively, outperforming current methods. These results demonstrate the effectiveness of the integrated design in addressing the inherent complexity of EEG signals. The code is publicly available at https://github.com/altaheri/TCFormer.