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Temporal Trends in Inadequate Vegetable and Fruit Consumption Among Adolescents Aged 12-15 Years From 31 Countries in Asia, Africa, and the Americas
Get PDFBackground and Aims: A low intake of fruit and vegetable consumption has been found to be associated with a plethora of negative health outcomes in adolescents. However, there is a scarcity of literature on long-term trends in fruit and vegetable intake in the adolescent population. Therefore, we examined this trend in a nationally representative sample of adolescents (12–15 years) attending school in 31 countries, including Africa, Asia, and the Americas, where investigation of such trends has been scarce. Methods: The present study analyzed data from the Global School-based Student Health Survey 2003–2017. The prevalence (95% CI) of inadequate fruit and vegetable intake (i.e., consumption < 5 times/day) was calculated for each survey, and crude linear trends were examined by linear regression models for each country. Results: We analyzed data from students (n = 193,388) aged 12–15 years [mean (SD) age 13.7 (1.0) years; 49.0% boys]. A high overall prevalence of inadequate fruit and vegetable consumption was found (75%). We observed increasing, decreasing, and stable trends in 6, 3, and 22 countries, respectively. In countries where decreasing trends were found, this decrease was minimal. Moreover, the majority of countries with stable trends exhibited a high prevalence of inadequate fruit and vegetable intake across multiple years. Conclusion: Our data show that inadequate fruit and vegetable consumption among adolescents is a major global problem with almost no improvements being observed in recent years. Intensification of global efforts to combat inadequate fruit and vegetable consumption is necessary.</p
Extended recommendations on the nomenclature for microbial catabolites of dietary (poly)phenols, with a focus on isomers †
Get PDFThere is an increasing body of evidence indicating that phenolic compounds derived from microbiota-mediated breakdown of dietary (poly)phenolics in the colon are at least partially responsible for the beneficial effects of a plant-based diet. Investigating the role of these catabolites and defining their particular biological effects is challenging due to the complex microbial pathways and the diversity of structures that are produced. When reviewing the data this is further exacerbated by the inconsistency and lack of standardization in naming the microbial phenolics. Here we update the nomenclature of colonic catabolites of dietary (poly)phenols, extending the proposals of Kay et al. (Am. J. Clin. Nutr., 2020, 112, 1051–1068, DOI: 10.1093/ajcn/nqaa204), by providing additional structures, and addressing the difficulties that can arise when investigating regioisomers and stereoisomers, where subtle differences in structure can have a substantial impact on bioactivity. The information provided will help to better harmonize the literature, facilitate data retrieval and provide a reference for researchers in several fields, especially nutrition and biochemistry.<br/
Children and Young People's Priorities for Mental Health Research in Northern Ireland
Get PDFIntroduction: There are a number of factors contributing to the poor mental health of children and young people (CYP) specific to life in Northern Ireland (NI). Prevention and early intervention are of critical importance to the mental health and well‐being of CYP. Policy decisions and service provision in the health and education sectors must be informed by research so that we can understand the factors affecting the mental health of young people and develop effective policy responses. This study examines the perceptions of young people in NI regarding mental health research priorities. Methods: CYP who live in NI and are aged between 11 and 25 were invited to contribute to this priority setting exercise. A short anonymous online survey asked: ‘What do you think is the most important question that researchers should be trying to answer about the mental health and wellbeing of young people in NI? You may submit more than one question.’ Two‐hundred and seventy‐nine questions were submitted from 147 respondents. The priorities were then further discussed and expanded through focus groups with young people. Results: The study identified 12 research priorities. Using thematic analysis, these were grouped into four themes: (i) Ensuring that the voices of young people in NI are heard, (ii) Understanding and addressing the root causes, extent and impact of mental health challenges in young people, (iii) Creating accessible and effective youth mental health services in NI and (iv) Fostering a whole‐school approach to mental health and resilience. Conclusions: The research priorities of young people are discussed in relation to current governmental strategic policies and statistics. Suggestions are put forward regarding how these research priorities may be addressed
A Mixed Effects Machine Learning Framework for Dairy Cattle Methane Prediction
Get PDFDue to the difficulty of recording dairy cattle (DC) methane (CH4) emissions, as well as the scale of data required for robust generalisation, DC CH4 emission prediction models are commonly trained on datasets compiled from multiple experiments, involving various treatments, and different sets of cattle. These underlying sources of variation, also known as random effects (REs), such as the treatment/management in each specific experiment, or the genetics of each individual cow, can introduce biases into the model, and produce misleading results when the models trained upon them are applied in alternative scenarios. Therefore, we developed a Mixed Effects Machine Learning framework (MEML), which could incorporate biological, environmental and genetic data, to produce refined machine learning (ML) models that could address this issue. The framework initially makes predictions using a ML model, which are then passed into a linear mixed effects (ME) model as an offset, where the residual variation is partitioned between the REs within a dataset. These RE adjustments are then used to correct the original response and a new ML model is retrained on its corrected version, further isolating the remaining residual variation to redistribute between the REs within the dataset, with the process repeating until convergence. This allows the refined ML models produced through the MEML framework to gain a greater appreciation of the authentic relationships between the features and response, apathetic to the RE influence embedded within them, improving their generalisation to external datasets with inevitable differences in RE influences
Spatial Dynamics of Harbour Porpoise Phocoena phocoena Relative to Local Hydrodynamics and Environmental Conditions
Get PDFUnderstanding the spatial dynamics of harbour porpoise (Phocoena phocoena) is crucial for effective conservation and management. The study presents a multidisciplinary approach to modelling and analysing the site occurrence and habitat use of Phocoena phocoena within the Skerries and Causeway Special Area of Conservation (SAC), identifying areas where they were seen surfacing and/or spending the most time. Using data derived from multibeam echosounders (MBES), particle size analysis of sediments, hydrodynamic modelling, and theodolite tracking observations, the study examines the influence of local hydrodynamics and environmental conditions on the spatial distribution of harbour porpoises. Kernel density analysis of 451 porpoise sightings over an 11-day survey demonstrated that dense clusters and higher aggregations occurred within ~500 m of the shoreline. Generalised Additive Models (GAMs) identified slope, aspect, backscatter intensity and sediment grain size as the most significant environmental predictors, accounting for 47.6% of the deviance in harbour porpoise distribution. Porpoises' occurrence was particularly spatially coincident with coarser sediments (4.25–5 mm), and their distribution was highly concentrated around headlands, shoreline and within a 3-h window before and after high water. Overall, these findings highlight the dynamic nature of harbour porpoises' use of habitat in space and time, with models predicting a high probability of porpoise encounters (> 0.6) nearshore, particularly in headland areas characterised by local flow acceleration and coarser seabeds. The study presents a robust workflow for developing a porpoise-specific monitoring program. By leveraging multidisciplinary methodological approaches, the study provides a scientific basis for refining marine conservation measures, delivering long-term protection for harbour porpoise habitats under existing legal and management frameworks both within and beyond the SAC boundaries
Creativity is a journey, not a destination: A team flow theory perspective for service workers
Get PDFTeamwork, recognised for supporting employee creativity, is crucial to organisational sustainability and survival. However, the ways of unlocking teamwork for creativity remain an unrealised opportunity. By integrating team flow theory, we exemplify how flow can be activated in teams and give rise to creativity, adding to the nascent literature on teams achieving an optimal state. Using a creative problem-solving intervention, qualitative data was collected via participant observation and semi-structured interviews with front-line hotel managers. We show the creativity outcomes at the individual, team, and organisational levels resulting from team flow, with our data emphasising the longer-term impacts on individual creativity. Notably, we extend team flow theory with our model, contributing to the discussion of creativity processes and offering important insights into how the reciprocal relationship between teamwork and individual creativity can be achieved. Our findings have implications for business leaders seeking to drive high-performing, creative organisations
Songs of the people, places, and spaces in between: reimagining the Sam Henry Collection and the shared cultural landscapes of Northern Ireland
Get PDFUsing Mixture Models to Characterize the Process Durations of Daily Living
Get PDFIn recent years, process mining (PM) has found widespreaduse across the healthcare, education, logistics, and finance domain. Smarthomes employ PM to examine human behavior, health conditions, andenhance daily living. Existing research uses PM to study human behavior. However, it failed to provide a comprehensive approach that studied/compared the different mixture models (MM) to determine the bestmodel that closely characterizes human behavior. As a result, this paperuses the gamma, Weibull and Gaussian MMs to represents the processdurations of daily living to facilitate an accurate representation of humanbehavior. The Expectation-Maximization (EM) algorithm was employedwhere the Kolmogorov-Smirnov (KS), Kullback-Leibler (KL) divergence,and Cramer-von Mises (CvM) tests were chosen to determine the bestMM. The proposed approach was applied over the Kasteren, UCI and4TU dataset
Proof-of-principle:Automation of a photoelectrocatalytic wastewater treatment system for the inactivation of antibiotic resistant E. coli
No full textIntensive research has been applied to photoelectrocatalysis (PEC) for water/wastewater treatment; however, the scale-up & automation of these systems hasn't been examined. This is a critical step if PEC is to be a viable solution for water/wastewater treatment. In this work, a PEC system has been scaled up, holding a treating volume of 0.85 L in automated batch runs. Expanded titanium mesh was anodised to form titania nanotubes on the surface and was used as the photoanode with a length of 950 mm, with 3 meshes in concentric packing. The largest diameter was 28 mm giving an estimated geometric surface area of 929 cm2. This work is a proof-of-principle using the charge (coulombs) passed to solution at a fixed potential to establish the required time for treatment. To ensure effective treatment, a minimum threshold current (>1.5 mA) is used for calculating the total charge, this prevents any dark current from being summed should the irradiation or power turn off, and a 1.5 factor-of-safety is also used. When the system was operated in an automated mode with the addition of 0.3 mM of peroxymonosulfate, it achieved a >5 log reduction in antibiotic resistance E. coli in synthetic wastewater.</p
Bridging the Reality Gap: A Framework for Synthetic Data Generation in Smart Manufacturing
Get PDFAutomated material inspection in smart manufacturing relies on extensive, diverse, and annotated datasets for robust deep neural networks (DNNs). However, acquiring real industrial data, especially with varied defects like occlusion and misalignment, is challenging and costly. This study presents Bridging the Reality Gap (BRG), a novel synthetic data generation framework. The BRG pipeline uses a high-fidelity 3D CAD model to simulate camera perspectives and physically based rendering with HDRI to generate diverse load station samples under various conditions. It systematically creates normal and abnormal samples, including geometric and photorealistic obstructions for misalignment and occlusion. Key contributions include developing the BRG framework to bridge the reality gap in robotic training and demonstrating that BRG generated synthetic data significantly enhances DNN performance. The framework also optimises annotation efforts through a symmetric data generation approach. After preprocessing and hyperparameter tuning, transfer learning is applied to a YOLOv5 model using these synthetic samples. Results show BRG’s significant performance for load station inspection; the combined synthetic dataset (D3) achieved 100% Precision, 93.83% Recall, and 96.82% F1 on real industrial samples