Underwater image enhancement using Divide-and-Conquer network.

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network. Specifically, the multi-axis attention block is presented in the texture network, which combine different region/channel features into a single stream structure. And the color network employs an adaptive 3D look-up table method to obtain the color enhanced results. Meanwhile, the refinement network is presented to focus on image features of ground truth. Compared to state-of-the-art (SOTA) underwater image enhance methods, our proposed method can obtain the better visual quality of underwater images and better qualitative and quantitative performance. The code is publicly available at https://github.com/zhengshijian1993/DC-Net.

An online survey capturing the views of stakeholders on primary school food systems across the four UK nations.

BACKGROUND: In 2020, the Generating Excellent Nutrition in UK Schools (GENIUS) Network was established to develop an understanding of the school food system across the four UK nations. This study explores stakeholders' views (headteachers, teachers, parents and pupils) on what works well, the challenges, and what an ideal primary school food system includes. METHODS: An online 'School Food Survey' was created in Qualtrics XM including closed and open-ended questions about the primary school food system. The Qualtrics link was distributed to stakeholders with an interest in school food through key contacts and networks across the four UK nations (21st June to 21st July and September 2021). Responses from the open-ended questions were exported from Qualtrics into Excel and analysed using SPSS. Aspects of qualitative content analysis were applied to summarise, code and quantify responses. Identified codes were entered by stakeholder, for example, parents and their response to the question into a Matrix table to allow identification of categories, themes and interpretation. RESULTS: A total of 509 participants completed the survey: most participants were from Scotland (n = 281; 55%) and England (n = 213; 42%) and were parents (n = 394). There were some consistent views across stakeholder responses, for example, the range of healthy options, costs, and portion sizes offered to pupils. Parents views varied, with some expressing the range of healthy options worked well and others reporting too many unhealthy choices. The cost of school food and school food funding presented challenges for both parents and schools. For parents, an ideal school food system would include a wide variety of fresh healthy food choices that were made on site, use quality produce, be inclusive for all cultures and diets, and provide food portion sizes appropriate for pupils ages. CONCLUSIONS: The findings iterate the diversity and some inconsistencies between stakeholders, emphasising the complexity and competing tensions school food systems encounter. Parental involvement and consideration of school-level and national factors are important when identifying challenges, what works well and describing an ideal primary school food system.

Assessment of differentially private synthetic data for utility and fairness in end-to-end machine learning pipelines for tabular data.

Differentially private (DP) synthetic datasets are a solution for sharing data while preserving the privacy of individual data providers. Understanding the effects of utilizing DP synthetic data in end-to-end machine learning pipelines impacts areas such as health care and humanitarian action, where data is scarce and regulated by restrictive privacy laws. In this work, we investigate the extent to which synthetic data can replace real, tabular data in machine learning pipelines and identify the most effective synthetic data generation techniques for training and evaluating machine learning models. We systematically investigate the impacts of differentially private synthetic data on downstream classification tasks from the point of view of utility as well as fairness. Our analysis is comprehensive and includes representatives of the two main types of synthetic data generation algorithms: marginal-based and GAN-based. To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic dataset generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness. Our findings demonstrate that marginal-based synthetic data generators surpass GAN-based ones regarding model training utility for tabular data. Indeed, we show that models trained using data generated by marginal-based algorithms can exhibit similar utility to models trained using real data. Our analysis also reveals that the marginal-based synthetic data generated using AIM and MWEM PGM algorithms can train models that simultaneously achieve utility and fairness characteristics close to those obtained by models trained with real data.

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents.

There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

Tabular, Annotated, Visual, or Trends + Contextual Information? Preferences for Online Laboratory Results Displays.

People are increasingly offered access to their personal health information (e.g., laboratory results, clinical notes, diagnostic imaging results). However, this information is the same as that used by health care providers with clinical expertise and training in medical terminology, which citizens typically do not have. In this study, we examined participants (N = 24) preferences for four different types of displays for online laboratory (lab) results: Tabular, Annotated, Visual, and Trends + Contextual Information. The Friedman test of difference comparing participants' ratings of the four displays was significant, χ2(3)=10.8, P=.013, and the Wilcoxon signed rank pairwise comparison tests revealed that participants rated the visual lab results display significantly more favourably than the traditional display (Z=-2.746, P=.006). These findings indicate that many people prefer lab results displayed using more visual cues and some perceived this format as easier to understand than the other display formats. Given the importance of people accessing, understanding, and using their own health information, it is crucial for displays and systems to provide a better user experience. Displaying data (e.g., lab results) visually is one possible way to improve interpretability of personal health information provided to the public.

Health and Safety Executive research on wood dust exposure controls in British manufacturing.

To generate new intelligence on occupational exposure to wood dust in woodworking manufacturing activities in Britain, the Health and Safety Executive (HSE) performed 22 occupational hygiene site visits to assess exposure and exposure controls between 2014 and 2017. The work aimed to characterise good practice and therefore sites with a poor health and safety record, as identified from HSE inspection records, were not invited to participate. Sites selected covered furniture production, joinery, saw milling, and boat building and repair. Twenty-three follow-up telephone interviews were also carried out across 15 of the companies with supervisors and managers to explore how they tried to promote good practice among the workforce, and if there are any potential challenges encountered. The aim of the interviews was to gain a better understanding of how to enable organisations to improve the management of wood dust exposure. This study found that 6.0% of all wood dust exposure measurements (15 out of 252) were above 5 mg/m³, and 17.6% of exposures to hardwood dust or mixtures of hardwood and softwood dust (38 out of 216) were above 3 mg/m³ (the then current and future workplace exposure limits). Sanding, cleaning, and maintenance activities were of particular concern. Improvements to exposure controls are required, in particular, improvements to local exhaust ventilation controls for hand-held power tools and hand sanding. The management, selection, and use of respiratory protective equipment were poor. All the managers and supervisors recognised that exposure to wood dust can pose serious health risks, and that controls were crucial to protecting workers' health. The findings from the telephone interviews suggest that supervision and provision of information about the health effects of exposure to wood dust were common approaches that organisations used to raise awareness and promote good practice, in relation to managing wood dust exposure. Worker attitudes towards controls, such as perceptions that they hinder task completion and habitual ways of working, were identified as factors influencing the use of controls. Risk communication approaches that focus on increasing workers' awareness of their susceptibility to ill-health using credible sources, such as peers, can help enhance the uptake of messages on the use of controls. Financial constraints were identified as a challenge to improving the control of wood dust, particularly for small companies.

Simulation analysis of hazardous chemicals released from the furniture plant using ALOHA software.

The manufacturing of wooden furniture is extensive in Thailand's east. Hazardous chemicals were used in the wooden furniture industry's manufacturing process. Hazardous substances released into the surrounding atmosphere appear to have an impact on the environment and individuals. The ALOHA model is frequently used to assess hazardous chemicals released into the environment; this simulation model is an effective tool for modeling chemical compounds and detecting chemical disasters. It has a tremendous potential for preventing mishaps in potentially hazardous or emergency situations. Acetone and butyl acetate were extracted from the hardwood furniture business to identify accidents such as leaking, spillage, and evaporation. It is described as a highly poisonous, combustible, and explosive material. Toxic accident releases have negative implications for the surrounding areas. The goal of this work was to examine each accident using ALOHA software, and the computation of acetone and butyl acetate accidents was shown in this study. This project provides critical data for the furniture plant's chemical emergency rescue strategy as well as recommendations for emergency evacuation site decision-making.

Trends in flame retardant levels in upholstered furniture and children's consumer products after regulatory action in California.

In 2013, California revised its upholstered furniture flammability standard TB 117-2013 to improve fire safety without the need for flame retardant (FR) chemicals. Subsequent legislation (SB 1019) required disclosure of FR content. In 2020 California expanded restriction on FR chemicals to include juvenile products and upholstered furniture (AB 2998). To monitor trends in FR use, and assess the effectiveness of the new regulations, we analyzed 346 samples from upholstered furniture (n = 270) and children's consumer products (n = 76), collected pre- and post-regulatory intervention for added FR chemicals (i.e., ∑FR > 1000 mg/kg). Upholstered furniture samples, collected from products before enactment of the new regulations, had a median FR concentration of 41,600 mg/kg (range: 1360-92,900 mg/kg), with 100% of the foam samples and 13.7% of the textile samples containing ∑FR > 1000 mg/kg. Firemaster formulations (FM 550 and FM 600), a mixture of triphenyl phosphate (TPHP), 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EH-TBB), bis(2-ethylhexyl)-3,4,5,6-tetrabromophthalate (BEH-TEBP) and a mixture of isopropyl- or tert-butyl-triphenyl phosphates (ITPs or TBPPs), were the most frequently detected FR (34%), followed by tris(1,3-dichloroisopropyl) phosphate (TDCIPP; 25%), TPHP with a mixture of polybrominated diphenyl ethers (BDE-47, 99, 100, 153 and 154; 20%) and tris(2-chloroethyl) phosphate (TCEP; 11%). Upholstered furniture components collected after enactment of the new legislation had a median FR concentration of 2600 mg/kg (range: 1160-49,800 mg/kg, outlier sample 282,200 mg/kg), with 11.9% of the foam samples and no textile samples containing ∑FR > 1000 mg/kg. Of these samples, tris(1-chloro-2-propyl) phosphate (TCIPP) was the most frequently detected FR (55%), followed by TDCIPP (30%) and Firemaster (FM 550, 15%). No PBDEs were detected in the post-regulatory intervention products. Our initial work on children's products showed 15% of the samples contained ∑FR > 1000 mg/kg. In our post- AB 2998 work, no regulated children's product components failed compliance (i.e., ∑FR > 1000 mg/kg). The data confirm successful adoption of the new regulations with most samples in compliance, demonstrating the efficacy of regulatory intervention. Given these results, environmental FR exposure is expected to decrease as older FR treated consumer products are replaced with FR free products.

The Impact of a Family-Friendly Hospital: A Patient Perspective.

INTRODUCTION: When a seriously ill and dying parent is hospitalized, the families are at risk of developing problems. Only sparse evidence is available on the effects of creating family-friendly rooms in hospitals. AIM: This study investigates how a seriously ill parent to children aged 0-18 experiences staying in a hospital room with family-friendly furnishing. METHODS: From September 2012 to September 2015, seriously ill and dying patients with children ages 0-18 were offered the opportunity to stay in a family-friendly room at the North Denmark Regional Hospital. The sick parents were interviewed about how the changed room impacted themselves, their children, and their family. RESULTS: The analysis led to three themes: (1) The ability to visit a sick parent, (2) maintaining family functions and relationships, and (3) room for children and adults. CONCLUSION: By offering the families a hospital room with cozy furniture and a big TV screen, seriously ill parents see that their children are more comfortable when visiting the hospital. A family-friendly hospital is not just an idealistic thought, it is a real opportunity to improve the final period of a dying parent's life.

Environmental sustainability assessment of wooden furniture produced in Pakistan / Avaliação da sustentabilidade ambiental de móveis de madeira produzidos no Paquistão

Abstract Life cycle assessment was carried out for a conventional wooden furniture set produced in Mardan division of the Khyber Pakhtunkhwa province of Pakistan during 2018-19. Primary data regarding inputs and outputs were collected through questionnaire surveys from 100 conventional wooden furniture set manufacturers, 50 in district Mardan and 50 in district Swabi. In the present study, cradle-to-gate life cycle assessment approach was applied for a functional unit of one conventional wooden furniture set. Production weighted average data were modelled in the environmental impacts modelling software i.e., SimaPro v.8.5. The results showed that textile used in sofa set, wood preservative for polishing and preventing insects attack and petrol used in generator had the highest contribution to all the environmental impact categories evaluated. Total cumulative energy demand for wooden furniture set manufactured was 30,005 MJ with most of the energy acquired from non-renewable fossil fuel resources.

Resumo A abordagem de avaliação do ciclo de vida foi realizada para um conjunto de móveis de madeira convencional produzido na divisão Mardan da província de Khyber Pakhtunkhwa do Paquistão durante 2018-19. Os dados primários sobre entradas e saídas foram coletados por meio de pesquisas por questionário de 100 fabricantes de conjuntos de móveis de madeira convencionais, 50 no distrito de Mardan e 50 no distrito de Swabi. No presente estudo, a abordagem de avaliação do ciclo de vida do berço ao portão foi aplicada para uma unidade funcional de um conjunto de móveis de madeira convencional. Os dados da média ponderada da produção foram modelados no software de modelagem de impactos ambientais, isto é, SimaPro v.8.5. Os resultados mostraram que os têxteis usados ​​no conjunto de sofás, o preservativo de madeira para polir e prevenir o ataque de insetos e a gasolina usada no gerador tiveram a maior contribuição em todas as categorias de impacto ambiental avaliadas. A demanda total acumulada de energia para o conjunto de móveis de madeira fabricado foi de 30.005 MJ, com a maior parte da energia adquirida de recursos de combustíveis fósseis não renováveis.

An initial set of reference values for the Balance Tracking System (BTrackS) Limits of Stability protocol.

BACKGROUND: The Balance Tracking System (BTrackS) Limits of Stability (LOS) protocol is a relatively new means of evaluating unconstrained dynamic postural control ability. While the reliability of this protocol has previously been established, reference data is currently unavailable to assist in the interpretation of results. RESEARCH QUESTION: What are typical reference values for the BTrackS LOS protocol with respect to sex, height, and BMI? METHODS: A cross= -section of 800 healthy, young adults (aged 18-29 years; 368 men, 432 women) were administered the BTrackS LOS protocol. Sex, height and weight variables were also captured for the participants. RESULTS: Results of a stepwise linear regression showed that the outcome measure for BTrackS LOS testing (i.e. LOS Area) was larger in taller individuals and in men. Based on these findings, four percentile ranking categories were established and associated look-up tables created. SIGNIFICANCE: The reference values provided by this study offer much needed guidance to clinicians and researchers for the determination of dynamic balance abnormalities based on BTrackS LOS testing.

Devices and furniture for small and sick newborn care: systematic development of a planning and costing tool.

BACKGROUND: High-quality neonatal care requires sufficient functional medical devices, furniture, fixtures, and use by trained healthcare workers, however there is lack of publicly available tools for quantification and costing. This paper describes development and use of a planning and costing tool regarding furniture, fixtures and devices to support scale-up of WHO level-2 neonatal care, for national and global newborn survival targets. METHODS: We followed a systematic process. First, we reviewed planning and costing tools of relevance. Second, we co-designed a new tool to estimate furniture and device set-up costs for a default 40-bed level-2 neonatal unit, incorporating input from multi-disciplinary experts and newborn care guidelines. Furniture and device lists were based off WHO guidelines/norms, UNICEF and national manuals/guides. Due to lack of evidence-based quantification, ratios were based on operational manuals, multi-country facility assessment data, and expert opinion. Default unit costs were from government procurement agency costs in Kenya, Nigeria, and Tanzania. Third, we refined the tool by national use in Tanzania. RESULTS: The tool adapts activity-based costing (ABC) to estimate quantities and costs to equip a level-2 neonatal unit based on three components: (1) furniture/fixtures (18 default but editable items); (2) neonatal medical devices (16 product categories with minimum specifications for use in low-resource settings); (3) user training at device installation. The tool was used in Tanzania to generate procurement lists and cost estimates for level-2 scale-up in 171 hospitals (146 District and 25 Regional Referral). Total incremental cost of all new furniture and equipment acquisition, installation, and user training were US$93,000 per District hospital (level-2 care) and US$346,000 per Regional Referral hospital. Estimated cost per capita for whole-country district coverage was US$0.23, representing 0.57% increase in government health expenditure per capita and additional 0.35% for all Regional Referral hospitals. CONCLUSION: Given 2.3 million neonatal deaths and potential impact of level-2 newborn care, rational and efficient planning of devices linked to systems change is foundational. In future iterations, we aim to include consumables, spare parts, and maintenance cost options. More rigorous implementation research data are crucial to formulating evidence-based ratios for devices numbers per baby. Use of this tool could help overcome gaps in devices numbers, advance efficiency and quality of neonatal care.

Reconciling chemical flame retardant exposure and fire risk in domestic furniture.

INTRODUCTION: Evidence suggests that standards for resistance of furniture to ignition may lead to an increase in use of chemical flame retardants (CFRs). This is motivating the development of new approaches that maintain high levels of fire safety while facilitating a reduction in use of CFRs. However, reconciling potential fire risk with use of CFRs in relation to specific policy objectives is challenging. OBJECTIVES: To inform the development of a new policy in the UK for the fire safety of furniture, we developed for domestic furniture quantitative models of fire risk and potential for CFR exposure. We then combined the models to determine if any lower fire risk, higher CFR exposure categories of furniture were identifiable. METHODS: We applied a novel mixed-methods approach to modelling furniture fire risk and CFR exposure in a data-poor environment, using literature-based concept mapping, qualitative research, and data visualisation methods to generate fire risk and CFR exposure models and derive furniture product rankings. RESULTS: Our analysis suggests there exists a cluster of furniture types including baby and infant products and pillows that have comparable overall properties in terms of lower fire risk and higher potential for CFR exposure. DISCUSSION: There are multiple obstacles to reconciling fire risk and CFR use in furniture. In particular, these include a lack of empirical data that would allow absolute fire risk and exposure levels to be quantified. Nonetheless, it seems that our modelling method can potentially yield meaningful product clusters, providing a basis for further research.

odML-Tables as a Metadata Standard in Microneurography.

Metadata is essential for handling medical data according to FAIR principles. Standards are well-established for many types of electrophysiological methods but are still lacking for microneurographic recordings of peripheral sensory nerve fibers in humans. Developing a new concept to enhance laboratory workflows is a complex process. We propose a standard for structuring and storing microneurography metadata based on odML and odML-tables. Further, we present an extension to the odML-tables GUI that enables user-friendly search functionality of the database. With our open-source repository, we encourage other microneurography labs to incorporate odML-based metadata into their experimental routines.

Usability Analysis of a Medication Visualization Tool for Decision Support.

BACKGROUND: In Germany, patients are entitled to a medication plan. While the overview is useful, it does not contain explicit information on various potential adverse drug events (ADEs). Therefore, physicians must continue to seek information from various sources to ensure medication safety. OBJECTIVE: In this project a first functional prototype of a medication therapy tool was developed that focuses on visualizing and highlighting potential ADEs. A usability analysis about the tool's functionality, design and usability was conducted. METHODS: A web application tool was developed using the MMI Pharmindex as database. ADEs are color coded and can be displayed in three different ways - as a list, a table, or a graph. To test the tool, an online survey was conducted amongst healthcare professionals (n = 9). The test included two real medication plans to check ADEs through the tool. RESULTS: The survey results indicated that the web tool was clear and self-explanatory. It scored overall "good" (score: 76.5) on the System Usability Scale questionnaire. Due to the free-text information of the database used, there were some inconsistencies in the visualized ADEs. CONCLUSION: There is a demand for a visualization tool for medications. The high quality of the database is crucial in order to correctly visualize all necessary information, such as drug-drug interactions and inclusion of patient data. This is essential to provide a trustworthy tool for medical professionals.

Probabilistic approaches for investigating species co-occurrence from presence-absence maps.

Background: In this research, we propose probabilistic approaches to identify pairwise patterns of species co-occurrence by using presence-absence maps only. In particular, the two-by-two contingency table constructed from a presence-absence map of two species would be sufficient to compute the test statistics and perform the statistical tests proposed in this article. Some previous studies have investigated species co-occurrence through incidence data of different survey sites. We focus on using presence-absence maps for a specific study plot instead. The proposed methods are assessed by a thorough simulation study. Methods: A Chi-squared test is used to determine whether the distributions of two species are independent. If the null hypothesis of independence is rejected, the Chi-squared method can not distinguish positive or negative association between two species. We propose six different approaches based on either the binomial or Poisson distribution to obtain p-values for testing the positive (or negative) association between two species. When we test to investigate a positive (or negative) association, if the p-value is below the predetermined level of significance, then we have enough evidence to support that the two species are positively (or negatively) associated. Results: A simulation study is conducted to demonstrate the type-I errors and the testing powers of our approaches. The probabilistic approach proposed by Veech (2013) is served as a benchmark for comparison. The results show that the type-I error of the Chi-squared test is close to the significance level when the presence rate is between 40% and 80%. For extremely low or high presence rate data, one of our approaches outperforms Veech (2013)'s in terms of the testing power and type-I error rate. The proposed methods are applied to a tree data of Barro Colorado Island in Panama and a tree data of Lansing Woods in USA. Both positive and negative associations are found among some species in these two real data.

Evaluation of the "Shifting Weight using Intermittent Fasting in night-shift workers" weight loss interventions: a mixed-methods protocol.

Introduction: Shift workers are at a greater risk for obesity-related conditions. The impacts of working at night presents a challenge for designing effective dietary weight-loss interventions for this population group. The Shifting Weight using Intermittent Fasting in night-shift workers (SWIFt) study is a world-first, randomized controlled trial that compares three weight-loss interventions. While the trial will evaluate the effectiveness of weight-loss outcomes, this mixed-methods evaluation aims to explore for who weight-loss outcomes are achieved and what factors (intervention features, individual, social, organisational and wider environmental) contribute to this. Methods: A convergent, mixed-methods evaluation design was chosen where quantitative and qualitative data collection occurs concurrently, analyzed separately, and converged in a final synthesis. Quantitative measures include participant engagement assessed via: dietary consult attendance, fulfillment of dietary goals, dietary energy intake, adherence to self-monitoring, and rates for participant drop-out; analyzed for frequency and proportions. Regression models will determine associations between engagement measures, participant characteristics (sex, age, ethnicity, occupation, shift type, night-shifts per week, years in night shift), intervention group, and weight change. Qualitative measures include semi-structured interviews with participants at baseline, 24-weeks, and 18-months, and fortnightly audio-diaries during the 24-week intervention. Interviews/diaries will be transcribed verbatim and analyzed using five-step thematic framework analysis in NVivo. Results from the quantitative and qualitative data will be integrated via table and narrative form to interrogate the validity of conclusions. Discussion: The SWIFt study is a world-first trial that compares the effectiveness of three weight-loss interventions for night shift workers. This mixed-methods evaluation aims to further explore the effectiveness of the interventions. The evaluation will determine for who the SWIFt interventions work best for, what intervention features are important, and what external factors need to be addressed to strengthen an approach. The findings will be useful for tailoring future scalability of dietary weight-loss interventions for night-shift workers.Clinical trial registration: This evaluation is based on the SWIFt trial registered with the Australian New Zealand Clinical Trials Registry [ACTRN 12619001035112].

Diagnostic accuracy of chest ultrasound scan in the diagnosis of childhood tuberculosis.

Chest Ultrasound Scan (CUS) has been utilized in place of CXR in the diagnosis of adult pneumonia with similar or higher sensitivity and specificity to CXR. However, there is a paucity of data on the use of CUS for the diagnosis of childhood TB. This study aimed to determine the diagnostic accuracy of CUS for childhood TB. This cross-sectional study was conducted at the Mulago National Referral Hospital in Uganda. Eighty children up to 14 years of age with presumptive TB were enrolled. They all had CUS and CXR performed and interpreted independently by radiologists. The radiologist who performed the CXR was blinded to the CUS findings, and vice versa. Radiologists noted whether TB was likely or unlikely. A two-by-two table was developed to compare the absolute number of children as either TB likely or TB unlikely on CXR or CUS. This was used to calculate the sensitivity and specificity of CUS when screening for TB in children, with a correction to accommodate the use of CXR as a reference test. The sensitivity of CUS was 64% (95% CI 48.5%-77.3%), while its specificity was 42.7% (95% CI 25.5%-60.8%). Both the CUS and CXR found 29 children with a likelihood of TB, and 27 children unlikely to have TB. CUS met the sensitivity target set by the WHO TPP for Triage, and it had a sensitivity and specificity comparable to that of CXR.