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Boron-functionalized graphitic carbon nitride materials for photocatalytic applications: effects on chemical, adsorptive, optoelectronic, and photocatalytic properties
No full textGraphitic carbon nitride (gC3N4, or CN herein) is widely studied as a photocatalyst owing to its ease of synthesis, high stability, and optoelectronic properties. However, its photocatalytic performance often remains limited, and a common approach to tune its function and enhance its performance is by doping. Boron (B) functionalization of CN has showed a potential benefit on photocatalytic performance for several reactions. However, the reason for this improvement and the links between synthesis method, exact B chemical environment, and performance remain unclear. Here, we present a fundamental study that elucidates the influence of (i) B functionalization, (ii) B content, and (iii) choice of B precursor on the physicochemical, adsorptive, optoelectronic, and photocatalytic properties of bulk B-CN. We synthesized two sets of B-CN materials (0.5–11 at% B), using either elemental boron or boric acid as precursors. The samples were characterized using several imaging and spectroscopic techniques, which confirm the integration of B into the material through B–O bonding and the creation of B clusters in the case of the boron precursor, with density functional theory (DFT) calculations supporting our analyses. The distribution of B atoms within B-CN particles remained heterogeneous. Compared to CN, B-functionalized materials show enhanced porosity and CO2 uptake, with similar degrees of light absorption and deeper energy band positions. Transient absorption spectroscopy (TAS) measurements showed that charge carrier populations, lifetimes, and kinetics were not significantly affected by B functionalization; however, at 5 at% B doping, an increase in the concentration of charge carriers was seen. Higher B content enhances the photocatalytic NOx removal under UVA irradiation (almost two-fold) and the selectivity to NO3– from NOx photooxidation, but has no significant effect on CO2 photoreduction, compared to pristine CN. Overall, this study provides fundamental insights to build on and more rationally produce better-performing B-CN photocatalysts
Impact of global indices on forecasting the S&P 500 index
No full textThis study presents a hybrid Random Forest–Long Short-Term Memory (RF-LSTM) framework for forecasting the S&P 500 Index, utilizing daily data from 25 global stock indices spanning 2003 to 2024. By combining Random Forest’s feature selection with LSTM’s deep temporal modeling, the approach reveals significant geographic asymmetries in predictive influence, with North America contributing 49%, East Asia 31%, ASEAN–Oceania 10%, Europe 6%, South Asia 3%, and Latin America 1%. At the index level, the Dow Jones Industrial Average (42%), KOSPI (18%), and Russell 2000 (15%) are identified as primary predictors, highlighting both domestic and international spillover effects. Optimized using Bayesian Optimisation, the RF-LSTM model achieves superior out-of-sample performance with an R² of 0.9952, RMSE of 16.85, MAE of 13.29, and MAPE of 4.72%, reflecting error reductions of up to 32.5% compared to baseline LSTM models. The Random Forest’s permutation importance effectively isolates high-impact indices, reducing noise and dimensionality to enhance temporal modeling accuracy. In our implementation, the residual variation left after Random Forest feature selection is further modeled using LSTM, consistent with a residual-hybrid forecasting design. These findings offer investors a robust, geographically informed tool for portfolio optimization and provide policymakers with insights into global risk transmission. The results underscore the efficacy of integrating interpretable feature selection with deep learning to advance financial forecasting in a globally interconnected market
Multimodal 3D-printed passive samplers to monitor, model and prioritise in situ pharmaceutical and pesticide pollution risks to an aquatic freshwater invertebrate, Gammarus pulex
No full textCalibrated 3D-printed multi-modal passive sampler devices (3D-PSDs) were used herein both to monitor contaminants of emerging concern (CECs) in freshwater and to estimate in situ chemical toxic and effect units for the aquatic invertebrate, Gammarus pulex, to support prioritisation strategies. A six-month study of water, biota, and 3D-PSDs in a heavily wastewater-impacted urban river catchment in London revealed 112 CECs detected, including pesticides, pharmaceuticals, illicit drugs and transformation products (water = 50; 3D-PSDs = 99; and G. pulex = 58 CECs). In G. pulex, the top three most concentrated CECs were citalopram (an antidepressant, at 101 ± 11 ng g−1), imidacloprid and clothianidin (both neonicotinoid pesticides, at 63 ± 12 and 52 ± 39 ng g−1, respectively). Principal component analysis revealed that passive sampler data represented chemical occurrence in the G. pulex better than using water data. Strong correlations existed between the passive sampler and biomonitoring data (R2 > 0.84, p < 0.05) indicating a possibility to infer risk from the device directly and without using calibrated PSD uptake rates (Rs). This new approach showed promise as a potentially cost-effective way to rapidly prioritise sites and CECs for large-scale risk assessment campaigns for these species
Regional and national estimates of children affected by all-cause and COVID-19-associated orphanhood and caregiver death in Brazil, by age and family circumstance: a modeling study
No full textBackground
Orphanhood and caregiver death can have severe consequences for children. Timely and accurate data can guide policy, particularly during health crises like COVID-19. The aim of our study is to present national and subnational analysis of both all-cause and COVID-19-associated orphanhood and caregiver death in Brazil and compare our model outputs with bespoke administrative datasets.
Methods
We use publicly available national datasets to estimate the number of Brazilian children experiencing parent and caregiver loss due to all causes and COVID-19 in 2020–2021.
Findings
An estimated 1,300,000 (95% uncertainty interval, 1,190,000, 1,430,000) children in Brazil experienced loss of one or multiple parents and/or co-residing caregivers. 673,000 (652,000, 690,000) were estimated to have lost one or both parents, of which 149,000 (144,000, 154,000) were COVID-19 associated; 635,000 (534,000, 758,000) children were estimated to have lost a co-residing grandparent or other kin, of which 135,000 (85,900, 199,000) were COVID-19 associated. Orphanhood varied substantially across states, with the rate of all cause parental orphanhood highest in Roraima at 17.5 (15.6, 20.6) per 1000 children and the lowest in Santa Catarina at 9.5 (8.7, 10.4) per 1000 children. COVID-19-associated orphanhood was also unevenly distributed, with Mato Grosso experiencing the greatest rate, at 4.4 (3.9, 5.3) per 1000 children, while Pará experienced the lowest rate of 1.4 (1.2, 1.8) per 1000 children. Comparisons with limited data from Brazil’s civil registry offices and (manually reviewed death certificates in Campinas found a similar demographic distribution of orphanhood. However, our estimates suggested that administrative sources undercount orphanhood.
Interpretation
Our findings highlight the extent of orphanhood in Brazil and large inequalities between states. Comparisons between administrative data and model estimates show similar temporal patterns and proportions of maternal and paternal orphanhood but different magnitudes. This suggests that strengthening vital registration systems can put children at the center of public health responses globally.
Funding
This study was funded by “Building Global Public Health Capacity to Link Real-Time Modelling Data on COVID-19-associated Orphanhood and Caregiver Deaths to Inform Prevention, Preparedness and Protection from COVID-19 consequences” (2023 CDC/WHO grant) and the Moderna Charitable Foundation
Whitecaps, bubbles and advection: insights from concurrent measurements in the open ocean
No full textField measurements of breaking waves and bubble depths were obtained using a stereo video system collocated with a submerged acoustic Doppler current profiler (ADCP) in the central North Sea. We discriminate between two bubble depths that define an active near-surface layer and a deeper layer. The active layer intermittently sees short-lived injected bubble depths from breakers whereas the deeper layer is dominated by persistent passive bubble plumes that remain visible for more than 50 mean wave periods. We augment traditional single-beam bubble detection methods by utilizing all five beams of the ADCP to achieve broader spatial coverage of bubble plume measurements. The combined wave and bubble observations reveal that deep bubble plumes often occur offset spatially from surface whitecaps, suggesting that Langmuir-type circulation plays a role in the formation and persistence of deep bubble plumes through vertical and horizontal advection
Harnessing reconfigurable hardware capabilities for 3D CNN acceleration
No full textAs computer vision research shifts towards video and volumetric data, the demand for efficient processing of spatiotemporal and multi-dimensional data has grown significantly. This thesis investigates the optimisation and mapping of 3D Convolutional Neural Networks (CNNs) onto Field-Programmable Gate Arrays (FPGAs), addressing the growing demand for hardware acceleration in applications like human action recognition (HAR), and medical image segmentation. Compared to 2D CNNs, 3D CNNs introduce additional complexity, larger workloads and higher memory demands. The research explores novel FPGA-based methodologies for both throughput- and latency-optimized designs to address these challenges.
Synchronous dataflow is utilised to generate intermediate representations of 3D CNNs, facilitating transformations to expand the design space. A key contribution is the development of automated toolflows for mapping and optimising 3D CNNs onto FPGA devices, addressing the lack of model-agnostic solutions. Adaptive optimization strategies are explored to automatically adjust to varying model architectures and hardware constraints. The research emphasizes on balancing between on-chip and off-chip memory utilisation, as the large data volumes of 3D models often exceed on-chip memory resources requiring off-chip access that can degrade performance. Strategies to address these challenges are investigated, particularly in streaming architectures featuring complex blocks like residual connections and branching. Another key contribution is the introduction of novel design space exploration and performance modelling methods, for efficiently navigating the 3D CNNs' broadened design space. The study additionally investigates runtime parametrisation and reconfigurable computing techniques to improve the flexibility and efficiency of the proposed accelerators. The findings demonstrate significant improvements in both throughput and latency oriented architectures across several 3D CNN models, highlighting the efficiency of the proposed solutions. While primarily focused on Human Action Recognition (HAR), the methodologies of this thesis are broadly applicable to other domains, like video analysis and medical imaging, enabling the 3D CNN implementation and deployment in resource-constrained settings.Open Acces
Recommendations for improving risk awareness, managing impacts, and delivering effective action in managing tree pests and pathogens in urban environments
No full textUrban trees are increasingly threatened by insect pests and pathogens, notably, but not exclusively, by accidental exotic introductions. The future health of these trees and preserving their benefits requires a full understanding of this threat and the options for its effective mitigation and management. Under the EU TREEPACT project, three interlinked studies sought to increase identification and understanding of the issues related to urban tree pests and pathogens. These comprised a systematic review of the topical global empirical evidence of impacts and their mitigation; a survey of key stakeholder groups associated with urban trees; and a case study assessment of regulations, policies, and guidance for urban trees at the city level. This short communication draws on insights from these studies and provides recommendations for future urban tree health management that consider specific urban challenges, stakeholder dynamics, and responsibilities. Together, these inform the development of activities that prevent or reduce the spread of tree pests and pathogens in urban areas, focusing on the post-border stage of the pest and pathogen invasion pathway
Kinematic and dynamic modeling of cable-object interference and wrapping in complex geometrical-shaped cable-driven parallel robots
No full textCable-Driven Parallel Robots (CDPRs) use cables as actuators to maneuver rigid mobile-platform in a parallel mechanism setup. Typically, CDPR kinematic and dynamic models avoid cable-object (cable-mobile-platform and cable-obstacle) interferences to prevent sudden cable tension changes that could deviate the end-effector’s trajectory. However, allowing these interferences can lead to cable wrapping, where cables wrap around complex-shaped surfaces upon contact, enhancing the CDPR’s workspace and reducing its footprint. Despite the potential benefits, there currently exists no kinematic and dynamic model that effectively incorporates cable wrapping around such complex-shaped surfaces. This paper introduces a novel numerical-based kinematic and dynamic modeling framework for CDPRs that detects and then manages cable wrapping around mobile-platform and multiple obstacles with the assumption that the cables remain taut and for every position along the cable, there is a unique and smooth way to describe its location on the surface. Simulation and hardware results on various complex-shaped mobile-platform and obstacles show that the proposed model framework can be conveniently and effectively applied to the real-time modeling of cable wrapping. Code and videos available at: https://github.com/bhattner143/GeoWrapSim-CDPR.git
Independent neural drives and distinct motor unit discharge characteristics in hamstring muscles during isometric knee flexion
No full textPurpose
Our study investigated the discharge characteristics of motor units (MUs) in the semitendinosus (ST) and biceps femoris (BF) at three knee-joint angles that varied muscle length.
Methods
Fifteen males (21.1 ± 2.8 years) performed steady isometric contractions with the knee flexors at four target torques (10%, 20%, 40%, and 60% of maximal voluntary contraction, MVC) at each of the three knee-joint angles (0°: long, 45°: intermediate, and 90°: short length). High-density electromyographic signals were recorded and decomposed into MU discharge times. We calculated mean discharge rate (MDR), the coefficient of variation for interspike interval (CoV ISI), and the standard deviation of the filtered cumulative spike train (SD of fCST). In addition, the neural drive within and between muscles was estimated from the cross-correlation of the fCST.
Results
Analysis of variance indicated that MVC was greatest at the long length and torque steadiness was worst at the intermediate length (p < 0.05). Linear mixed models revealed that BF exhibited greater MDR and variability in neural drive (SD of fCST), whereas the MUs in ST displayed greater discharge rate variability (CoV ISI) (p < 0.05). Cross-correlation of the estimated neural drives to ST and BF was relatively low, suggesting independent neural control of the two muscles. Moreover, the variability in neural drive for ST was more strongly correlated with torque steadiness (CoV torque) than that for BF.
Conclusion
The findings indicate that MU discharge characteristics differed for ST and BF across knee-joint angles, with each muscle receiving a distinct neural drive highlighting the importance of muscle-specific training strategies