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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The revitalization of endangered heritage buildings: A decision-making framework for investment and determining the highest and best use in Egypt

Background

Egypt's major cities have been losing heritage and historical buildings due to neglect and misuse, prompting non-governmental organizations, academic institutions, and researchers to advocate for adaptive reuse strategies to preserve the cities' heritage and identity. Adaptive reuse involves changing, modifying, or reusing a space based on community needs, business model, location, and proximity to facilities and services. Heritage buildings offer many tangible and intangible benefits that enhance financial returns, making them challenging but feasible and attractive for investors who value authenticity, uniqueness, and sustainability.

Methods

This study examines how market value, acquisition opportunities, target clients, age groups, and socioeconomic status affect decision-making. A comparative analysis of three buildings in the Egyptian cities of Alexandria and Cairo is utilized to establish development guidelines and decision-making parameters that significantly impact project design and building functions to determine the highest and best use. In order to complete this study, AutoCAD by Autodesk was used for 2D drawings, SketchUp by Trimble for 3D models, Adobe Photoshop for diagram presentation, and Microsoft Office for tables and diagrams.

Results

The comparative analysis provided valuable insights into the adaptive reuse of heritage buildings in developing countries. Findings highlighted how cultural heritage preservation could foster socioeconomic development. Key success factors included stakeholder and community engagement, financial viability, and architectural compatibility. The decision-making framework provides a practical tool for evaluating heritage building reuse.

Conclusions

The analysis illustrates successful reuse strategies and considerations. Decision-making frameworks and tools offer practical guidance for future investments and decisions. These findings affect heritage conservation and urban development policymakers, planners, and investors. Stakeholders can make informed decisions and implement strategies to preserve cultural and environmental value by realizing challenges and opportunities. This study hopes to inspire more research and help preserve and revitalize heritage buildings in developing countries, preserving their cultural and socioeconomic value.

Area-based social inequalities in adult mortality: construction of French deprivation-specific life tables for the period 2016-2018

Background

In order to tackle social inequalities in mortality, it is crucial to quantify them. We produced French deprivation-specific life tables for the period 2016-2018 to measure the social gradient in adult all-cause mortality.

Methods

Data from the Permanent Demographic Sample (EDP) were used to provide population and death counts by age, sex and deprivation quintile. The European Deprivation Index (EDI), applied at a sub-municipal geographical level, was used as an ecological measure of deprivation. Smoothed mortality rates were calculated using a one-dimensional Poisson counts smoothing method with P-Splines. We calculated life expectancies by age, sex and deprivation quintile as well as interquartile mortality rate ratios (MRR).

Results

At the age of 30, the difference in life expectancy between the most and least deprived groups amounted to 3.9 years in males and 2.2 years in females. In terms of relative mortality inequalities, the largest gaps between extreme deprivation groups were around age 55 for males (MRR = 2.22 [2.0; 2.46] at age 55), around age 50 in females (MRR = 1.77 [1.48; 2.1] at age 47), and there was a decrease or disappearance of the gaps in the very older adults.

Conclusions

There is a strong social gradient in all-cause mortality in France for males and females. The methodology for building these deprivation-specific life tables is reproducible and could be used to monitor its development. The tables produced should contribute to improving studies on net survival inequalities for specific diseases by taking into account the pre-existing social gradient in all-cause mortality.

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.

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.

Federated learning for generating synthetic data: a scoping review

Introduction

Federated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format.

Objectives

The objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps.

Methods

A scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk.

Results

A total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data.

Conclusions

Federated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.

A blended model of interior design studio practice due to COVID-19

Background: The coronavirus disease 2019 (COVID-19) pandemic upended the educational system around the globe. During this challenging period, universities and colleges looked for other effective alternative methods of learning, such as Virtual Learning Environments (VL). Besides, Ahlia University has implemented E-learning in response to COVID-19. There needs to be more attention given to the challenges associated with technology adoption facing interior design and architecture programs, where over 60% of courses are practical, especially design studios, which form the core of the curriculum. According to a review of the relevant literature, there needs to be more research on blended learning in interior design and architecture. In order to enhance the teaching and learning of interior design and architecture, further research is required to combine cutting-edge techniques and technology. The aim of this study was to review the classroom materials for Ahlia University's interior design studio. Methods: After completing the INTD 212, INTD 216, INTD 311, and INTD 404 studios in mid-March 2022, a short Qualtrics poll was done to assess the difficulties of e-learning and offer potential consequences. Results: Though students were conveniently attending courses online, there was not much discussion and interaction like in the face-to-face model. Blended teaching in design studio courses offered many benefits. The results showed that blended design studios achieved pedagogical results as students developed their knowledge. Conclusions: Based on the findings, this research concludes that teaching and learning should be shifted from face-to-face and online learning to the best practice of a blended format.

Analysis of sagittal spinal alignment at the adolescent age: for furniture design

Knowledge of the parameters of the human spine is essential in designing ergonomic furniture. The purpose of this study was to evaluate spinal alignment in adolescents of various ages. The lengths, curvatures, and concave-convex spacings of the spine were investigated in 268 participants aged 9-18 years. Ten ages were classified, and the rate of increase of parameters was calculated for each age and age group. The results showed that spinal parameters, except for cervical lordosis, increased with age. Adolescents were classified as 9-10, 11-12, 13-15, and 16-18 years old. A rapid increment of lengths and concave-convex spacings occurred at ages 13-15, while that of curvatures occurred at ages 16-18. Spinal parameters differed significantly among the age groups (p < 0.05). Concave-convex spacings reflected differences in the spine more clearly than the other parameters. This study suggests the necessity of designing spine-related furniture based on spinal parameters, thus providing adaptive support for the adolescent spine, particularly the lumbar spine. Practitioner summary: This study examined spinal lengths, curvatures, and concave-convex spacings in adolescents aged 9-8 years and then divided them into four age groups. Concave-convex spacings effectively reflected spinal differences between age groups, particularly the lumbar spine. These results can inform the ergonomic design of spine-related furniture.HIGHLIGHTSSpinal parameters increased progressively between 9 and 18 years. Regression analysis showed good linear correlations between TK, LL, SK, TS, and LS with age.Age classification of adolescents was Group I (9-10 years), Group II (11-12 years), Group III (13-15 years), and Group IV (16-18 years). The rapid increment of lengths and concave-convex spacings were in Group III while that of curvatures were in Group IV.Concave-convex spacings were vital parameters to evaluate the global balance of the spine.The lumbar spine is an essential segment for characterizing spinal alignment.

Implementation of distributed arithmetic-based symmetrical 2-D block finite impulse response filter architectures

Background: This paper presents an efficient two-dimensional (2-D) finite impulse response (FIR) filter using block processing for two different symmetries. Architectures for a general filter (without symmetry) and two symmetrical filters (diagonal and quadrantal symmetry) are implemented. The proposed architectures need fewer multipliers because of the symmetry of the filter coefficients. Methods: A distributed arithmetic (DA)- based multiplication method is used in the proposed architecture. A dual-port memory-based lookup table (DP-MLUT) is used in the multiplication instead of lookup-table (LUT) to reduce the area and power of the FIR filter. The filter's throughput is increased by using block processing. Memory reuse and memory sharing methods are introduced, which reduces the need for many registers and hence the circuit complexity. The architectures are written in Verilog Hardware Description Language and synthesized using Genus Synthesis tool-19.1 in 45nm technology with a generic library of Cadence vendor constraints. The synthesis tool generates the area, delay, and power reports. Power consumption of architectures is calculated with an image size of 64 X 64 and at 20 MHz frequency. Results: Compared to existing architectures, the synthesis results show improvements in power, area, area delay product (ADP), and power delay product (PDP). The proposed MLUT-based 2-D block Quadrantal Symmetry Filter (QSF) for length 8 with block size 4 consumes 58.94% less power, occupies 59.5% less area, 48.44% less ADP and 47.78% less PDP compared to best existing methods. Conclusions: A novel DA-based 2-D block FIR filter architecture with various symmetries is realized. Symmetry is incorporated into the filter coefficients to minimize the number of multipliers. The LUT size is optimized by odd multiples or even multiples storage techniques. Also, the overall area of the architecture is decreased by DP-LUT-based multipliers. The proposed filter architecture is area-power-efficient. It is best suited for applications that have fixed coefficients.

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.

Mapping the theories, content, and outcomes of family-based interventions for children and young people with gaming disorder: A scoping review protocol

Background  Despite the growth of gaming disorders globally, evidence of the formal involvement of family in treating gaming disorders is limited. When children are affected by gaming disorder, the family may encounter challenges in managing the behavior and in the lack of information regarding the gaming disorder, resulting in inconsistent parenting, which may further exacerbate the problem. Thus, it is essential to involve the family in formal interventions. The current scoping review plans to identify the theories, content, and outcomes of family-based interventions for children and young people with gaming disorders. Methods  This scoping review will follow the Joanna Briggs Institute (JBI) methodology. The population, Concept, and Context (PCC) were used to develop the review question. The studies published in the indexed databases will be searched systematically, and the reference list of included full texts will be searched for relevant studies. Intervention studies published in English from January 2010 to December 2022 will be included. Two independent reviewers will screen the studies against eligibility criteria. The data will be extracted and presented in a tabular and narrative style. Discussion  This scoping review will help better understand content, outcomes, and theories underpinning family-based interventions for children and young people with gaming disorders. Findings will inform the stakeholders about the current topic and guide the potential research areas. Registration details: The protocol has been registered in Open Science Framework with the DOI: https://doi.org/10.17605/OSF.IO/TXWBH.

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.

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.

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.

Evaluation of a Scintillating Plastic Optical Fiber Device for Measuring kV-Cone Beam Computed Tomography Dose

BACKGROUND

Justification of imaging procedures such as cone beam computed tomography (CBCT) in radiotherapy makes no doubt. However, the CBCT composite dose is rarely reported or optimized, even though the repeated CBCT cumulative dose can be up to 3% of the prescription dose. This study aimed to evaluate the performance and utility of a new plastic scintillating optical fiber dosimeter for CBCT dosimetric quality assurance (QA) applications before a potential application in patient composite CBCT dosimetry.

METHODS

The dosimeter, made of 1 mm diameter plastic fiber, was installed under a linear accelerator treatment table and linked to photodetectors. The fiber impact on the fluence and dose delivered was respectively assessed with an electronic portal imaging device (EPID) and EBT3 Gafchromic® film. The presence of artifacts was visually evaluated on kV images. The dosimeter performances were determined for various acquisition parameters by comparison with ionization chamber values.

RESULTS

The maximum impact of the fiber on the fluence measured by the EPID was -1.2% for the 6 MV flattening filter-free beam. However, the fiber did not alter the film dose profile when measured for all the beams tested. The fiber was not visible at energies ≥ 80 kV and was merely visible on the CBCT images. When the rate of images per second or mA was changed, the maximum relative difference between the device and the ionization chamber CTDIs was <5%. Changing collimation led to a -7.2% maximum relative difference with an absolute dose difference that was insignificant (-0.3 mGy). Changing kV was associated with a -8.7% maximum relative difference, as published in the literature.

CONCLUSIONS

The dosimeter may be a promising device for CBCT recurrent dosimetry quality control or dose optimization. According to these results, further developments are in progress in order to adapt the solution to the measurement of patient composite CBCT doses.

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].

Repeatability of pupil diameter measurements using three different topography devices

PURPOSE

To evaluate the intra- and inter-device repeatability of pupil diameter measurements using three different devices in patients prior to corneal refractive surgery.

METHODS

We examined preoperative measurements from a total of 204 eyes (102 patients) scheduled for corneal refractive surgery at two private centers between July and December 2021. Three consecutive scans were performed with three different devices (Sirius anterior segment analyzer, Pentacam HR, IOLMaster 500) in the same session by the same examiner under standardized conditions. To assess the intra- and inter-device repeatability, we calculated the Intraclass Correlation Coefficient (ICC) and demonstrated results using Bland-Altman plots.

RESULTS

The measurement accuracy (intra-device repeatability) of Sirius and IOLMaster was comparable (ICC = 0.64 and 0.61, respectively), with almost no statistically significant differences. Sirius showed the highest measurement accuracy among the three devices. Pentacam measurements resulted in lower precision, with an ICC of 0.09. The agreement between the pairs of devices (inter-device repeatability) was low (wide LoA ranges, Table 5).

CONCLUSION

In this study, the intra-device repeatability of Sirius and IOLMaster was higher than that of the Pentacam, although it did not achieve an optimal level across all three devices. The three devices examined cannot be used interchangeably.

Fully bio-based fire-safety composite from cotton/viscose wastes and alginate fiber as furniture materials

The increasing demand of textiles and apparel as global economy booms deepens environmental crisis associated with excessive textile waste disposed by landfill or incineration. This work implemented an eco-friendly and sustainable strategy to recycle up to 50 wt% textile waste with marine bio-based calcium alginate fiber into fire-proof fully bio-based composite textile by carding process. Incorporation of intrinsic nonflammable calcium alginate fibers endowed these needle-punching bio-composite felt with excellent inherent flame retardancy and improved safety. Horizontal burning test showed that by mixing with alginate fiber in proper ratio and pattern, extremely flammable cotton fiber and viscose fiber became totally inflammable. Analysis revealed that the generation of CaCO3 char residue and gaseous volatile of H2O inhibited the diffusion of O2 and heat, contributing to the outstanding fire proof performance of produced composite felt. The improved safety was affirmed by cone calorimetry test. It demonstrated limited heat, smoke and toxic volatile compound in the burning, as well as production of CO and CO2. All results showed that a straightforward yet economical method could recycle textile waste fibers into fully bio-based, fireproof and greener products, a potential candidate as fireproof structural filling and insulation materials for household textile or construction material.

A Step-by-Step Guide to Writing a Scientific Review Article

Scientific review articles are comprehensive, focused reviews of the scientific literature written by subject matter experts. The task of writing a scientific review article can seem overwhelming; however, it can be managed by using an organized approach and devoting sufficient time to the process. The process involves selecting a topic about which the authors are knowledgeable and enthusiastic, conducting a literature search and critical analysis of the literature, and writing the article, which is composed of an abstract, introduction, body, and conclusion, with accompanying tables and figures. This article, which focuses on the narrative or traditional literature review, is intended to serve as a guide with practical steps for new writers. Tips for success are also discussed, including selecting a focused topic, maintaining objectivity and balance while writing, avoiding tedious data presentation in a laundry list format, moving from descriptions of the literature to critical analysis, avoiding simplistic conclusions, and budgeting time for the overall process.

Assessing Spatial User Experience for Design Guidelines: A Comparative Study of Outpatient Waiting Rooms With Conventional Versus Modern Features

OBJECTIVES

Identify waiting room design features that are most appreciated by outpatients and their companions in conventional and modern waiting rooms. Evaluate if end users evaluate the environment differently from experts and in what aspects. Provide evidence-based design guidelines that orient designers and healthcare managers.

BACKGROUND

Built environments are relevant in patients' evaluation of overall healthcare service. For outpatients, waiting frequently consumes the largest amount of time; thus, waiting room interior design has great potential to enhance their experience.

METHODS

This study compares perceptions of two types of waiting rooms-conventional and modern-based on the spatial user experience (SUE) model. In the first stage of the study, we compared user evaluations of conventional waiting rooms (n = 137) and modern waiting rooms (n = 426) with respect to the eight SUE model dimensions using multigroup structural equation modeling. In the second stage, an expert ergonomist and two professional interior designers assessed both types of waiting rooms.

RESULTS

Results showed that modern waiting rooms were perceived to be significantly better in all SUE dimensions. We also found experts' evaluations were overall consistent with users' perceptions. Discrepancies were only found in temperature perception, signage evaluation, and spatial appreciation.

CONCLUSIONS

Participants valued modern style waiting room features such as good quality signage, use of armchairs and sofas, a controlled environment, and decoration. We suggest involving end users in the design process to respond to their needs and promote a positive experience. Finally, we provide easy-to-adopt design guidelines to improve patients' waiting room experience.

Electrocoagulation treatment of furniture industry wastewater

Electrocoagulation was investigated as a method for treating wastewater containing polyvinyl acetate (PVAc) from the furniture industry. The study evaluated the evolution of iron concentration and passivation during the treatment process. Laboratory-scale experiments were conducted to evaluate the effects of inter-electrode distance (d), current density, and mode on treatment performance. Three values of d (0.3, 0.6, and 0.9 cm) were studied and found to have no significant effect on performance. However, lower d values resulted in reduced energy consumption due to a decrease in applied voltage. Three values of current density (132, 158, and 197 A m^-2) were studied under two current modes, Direct Current (DC) and Alternating Pulsed Current (APC). The best treatment performance for DC occurred under 158 A m^-2 (the treated wastewater was characterized by pH = 4.59 ± 0.02, conductivity = 996 ± 21 μS cm^-1, COD = 1940 ± 55 mgO₂ L^-1, TSS = 105 ± 14 mg L^-1, and Fe = 50.39 ± 1.87 mgFe L^-1). For APC, the best performance was achieved under 197 A m^-2 (the treated wastewater was characterized by pH = 6.33 ± 0.06, conductivity = 988 ± 17 μS cm^-1, COD = 1942 ± 312 mgO₂ L^-1, TSS = 199 ± 55 mg L^-1, and Fe = 44.68 ± 4.60 mgFe L^-1). Despite the promising results, treatment performance was insufficient to meet the legal requirements for water discharge. APC was found to be a more economically viable approach, as it reduced anode wear, electrode passivation, and energy consumption. The quantity of iron released increased with d, and the effect of current density on iron concentration was found to be non-linear. However, applying APC reduced the iron content for all tested current densities. The tests showed that EC was effective in removing chemical oxygen demand (COD) and total suspended solids (TSS), achieving removal efficiencies above 92% and 99%, respectively. However, the studied treatment procedures were insufficient to meet the EU legal requirements for water discharge. Therefore, the obtained wastewater should undergo a post-treatment process.

Designing for Family Engagement in Neonatal ICUs: How Is the Interior Design of Single-Family Rooms Supporting Family Behaviors, From Passive to Active?

PURPOSE

Understand how the interior design of single-family rooms (SFRs) in neonatal intensive care units (NICUs) can support family engagement behaviors.

BACKGROUND

Family members are integral contributors to infant care in NICUs, impacting infant development. While at the NICU, parents are encouraged to participate in a process called family engagement, where they are expected to move from passive to active caregivers, in preparation for their role after discharge. While family engagement is affected by the built environment, no studies have investigated this relationship in any depth. NICU settings morphed to involve families through the SFR design model, but the interior environment of SFRs have not been sufficiently explored as a resource to support specific family engagement behaviors.

METHODS

We interviewed family and staff and observed family engagement behaviors in SFRs at two NICUs. Behaviors were observed and described in terms of their location, number of people, and design elements involved. Built environment characteristics were collected through physical assessments, and interviews elicited participants' perceptions about design factors impacting family behaviors inside SFRs. Data analysis followed grounded theory segments and pattern matching.

RESULTS

Three behavioral patterns and five themes were identified showing how SFRs' private bathrooms, family storage, family zone partitions, positive distractions, and information boards can support families' home-like, educational, collaborative, and infant care behaviors.

CONCLUSIONS

The interior design of SFRs can be a resource to family engagement in the NICU. Future research should operationalize SFR features found in our study to measure and validate their impact on family engagement outcomes.

Analysis of the Hanging Actions and Operating Heights of Storage Furniture Suitable for the Elderly

The current functional scale design of storage furniture which the elderly use does not meet their needs, and unsuitable storage furniture may bring many physiological and psychological problems to their daily lives. The purpose of this study is to start with the hanging operation, to study the factors influencing the hanging operation heights of elderly people undergoing self-care in a standing posture and to determine the research methods to be used to study the appropriate hanging operation height of the elderly so as to provide data and theoretical support for the functional design scale of storage furniture suitable for the elderly. This study quantifies the situations of elderly people's hanging operation through an sEMG test performed on 18 elderly people at different hanging heights combined with a subjective evaluation before and after the operation and a curve fitting between the integrated sEMG indexes and the test heights. The test results show that the height of the elderly subjects had a significant effect on the hanging operation, and the main power muscles of the suspension operation were the anterior deltoid, upper trapezius and brachioradialis. Elderly people in different height groups had their own performance of the most comfortable hanging operation ranges. The suitable range for the hanging operation was 1536-1728 mm for seniors aged 60 or above whose heights were within the range of 1500-1799 mm, which could obtain a better action view and ensure the comfort of the operation. External hanging products, such as wardrobe hangers and hanging hooks, could also be determined according to this result.

A new consensus on reconciling fire safety with environmental & health impacts of chemical flame retardants

Flame retardants are chemical substances that are intended to mitigate fire safety risks posed by a range of goods including furniture, electronics, and building insulation. There are growing concerns about their effectiveness in ensuring fire safety and the potential harms they pose to human health and the environment. In response to these concerns, on 13 June 2022, a roundtable of experts was convened by the UKRI Six Clean Air Strategic Priorities Fund programme 7. The meeting produced a Consensus Statement that summarises the issues around the use of flame retardants, laying out a series of policy recommendations that should lead to more effective fire safety measures and reduce the human and environmental health risks posed by these potentially toxic chemicals.

A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection

Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively.

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

Accelerated transformation of plastic furniture into microplastics and nanoplastics by fire

Numerous plastic items are known to gradually degrade and release microplastics and nanoplastics under certain conditions, which can be significantly accelerated by fire combustion. Unfortunately there is a limited knowledge about this burning process because the characterisation on microplastics and nanoplastics is still a challenge. In this study, an outdoor plastic chair is subjected to a combustion process, the change in the surface functional groups (due to different degree of burning) and the release of microplastics and nanoplastics are investigated. During the combustion process, the plastic is molten, burned and deposited on solid surfaces including concrete, stone and glass. Scanning electron microscopy (SEM) results show that the peeling off the deposited plastic generates a large number of fragments. Through Raman imaging, these fragments are characterised as polypropylene (PP) microplastics and nanoplastics due to appearance of characteristic peaks. To further increase the sensitivity, several algorithms are tested and optimised, including logic-based, non-supervised principal component analysis (PCA)-based, algebra-based and their hybrids (to intentionally correct the non-supervised PCA) to enable the effective extraction of the key information towards plastics characterisation, particularly by distinguishing the signal from the background noise towards the visualisation of the different degrees of burning. Based on the findings from Raman imaging and SEM, it is estimated that tens of microplastics and nanoplastics are created per μm². Overall Raman imaging can be a suitable approach to characterise the microplastics and nanoplastics in a complex background, such as the fire-burned plastic items.

Evaluating Therapeutic Healthcare Environmental Criteria: Architectural Designers' Perspectives

This study presents architectural designers’ perception of the importance of healthcare environmental criteria in the implementation of user-centered, therapeutic hospital design. Architectural designers with over three years of professional experience (N = 182) in South Korea were surveyed using an empirical questionnaire. The extensive interviews of 15 hospital design experts followed to interpret the survey results and discuss the barriers and suggestions for the successful delivery of therapeutic healthcare design practice. Among the 27 variables selected from the preliminary literature review, factor analyses revealed seven important therapeutic environmental criteria (i.e., management, interior design, spatial quality, service, nature and rest, ambient indoor comfort, and social program and space; χ2 = 1783.088, df = 300, p < 0.001). Analyses of variance revealed the level of importance among these criteria related to respondents’ personal and professional characteristics. Significant differences were found for the variables from the management, interior design, and spatial quality factors in relation to the respondents sex and age. For the successful delivery of therapeutic healthcare design, the design experts highlighted the implementation of evidence-based design practice that integrates local and international knowledge from various hospital users and multi-disciplinary specialists participating in the healthcare design process.

Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform

A low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved.

The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks' detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.

Global research into the relationship between electronic waste and health over the last 10 years: A scientometric analysis

Introduction

The aims of this research were to conduct the first holistic and deep scientometric analysis of electronic waste and health and provide with the prediction of research trends and hot topics.

Method

A comprehensive literature search was conducted via the Web of Science Core collection databases on 26 August 2022 to identify all articles related to electronic waste and health. A total of 652 records have been extracted from the Web of Science after applying inclusion and exclusion criteria and were analyzed using bibliometrix software of R-package, VOSviewer, and CiteSpace, visualized by tables and diagrams.

Result

The number of publications and total citations had shown a general growth trend from 2012 to 2021, with an average annual growth rate of 23.74%. Mainland China was the significant nation with the greatest number of publications, citations, and international links. The journal publishing the most was "Science of the Total Environment" (n = 56). Huo X and Hu XJ were the top two author contributing to this field with the highest h-index (23). Over time, the focus in this field shifted to exposure to heavy metal, polychlorinated biphenyls, polybrominated biphenyl ethers, and poly- and perfluorinated alkyl substances from electronic waste, and managements, such as hydrometallurgy.

Discussion

By this scientometric analysis, we found that the most active country, journal, organization and author contributing to this filed, as well as high impact documents and references and research hotspots. Also, we found that the hotspots might be exposure to toxic substances from electronic waste procession, its impact on human health and relevant managements. And evironmentally friendly materials to replace heavy metal mate rials, and environmentally friendly and effective recycling methods of electronic waste need to be further studied.

Creating crime scene 3D model with body wear camera footage

Aim

The aim of this study is to develop a methodology for creating 3D images of crime scenes based on footage from cameras used by emergency services. To accomplish this, a research experiment was conducted, which consisted of re-enactment of a crime scene and simulation of the actions of the emergency team. The experiment did not illustrate a real case. The scenario was developed and dedicated for the purpose of the research.

Material and methods

The research material of this study consists of footage recorded in digital video format. The footage shows the course of a re-enacted intervention of emergency services at the crime scene. The re-enactment, which was a research experiment, was arranged under conditions close to real ones. The 3D model of the scene was created in three stages: video analysis and 3D reconstruction of the spatial position of the camera; 3D modelling of the figure of the participant with reconstruction of the position similar to the one in the recording; and 3D scanning of the scene of the simulated crime, assembly of individual elements, and scaling to real dimensions.

Results

The result (a 3D model) was presented in the form of a set of images: horizontal projections, vertical sections, and isometric and perspective views of the model. Technical data of the research equipment as well as other relevant information was presented in tables and diagrams.

Conclusions

This study demonstrated that graphic data obtained unintentionally and through alternative recording sources may significantly complement the data collected in the course of routine medico-legal and forensic activities. The use of cameras during the actions of rescue and emergency services allows us to obtain information of significant importance for medico-legal and forensic analyses. The footage from cameras of emergency services makes it possible to obtain a 3D image of the crime scene for further medico-legal and forensic analyses.

An investigation of universal design (UD) features in Indian household products

BACKGROUND

Universal design (UD) is a beneficial concept for better accessible design to improve easy approachability and industry-standard products. Specifically, Indian household products require UD features in domains such as bathroom and toilet, furniture, kitchen utilities, and home appliances. Among household product design in India, a lack of understanding of the product's universality might be a constraint for product designers. Also, there are no studies assessing the UD features of Indian household products.

OBJECTIVE

(1) To examine the UD feature of Indian household products against the seven principles of UD; (2) To determine the most lacking UD feature among Indian household products; and (3) To find out the Indian household categories (i.e., bathroom and toilet, furniture, kitchen utilities, and home appliances) which are most lacking in UD performance.

METHOD

The UD features were evaluated using a standardized questionnaire, which contains 29 questions on UD principles and general questions (gender, education level, age and house characteristics). Using statistical packages, the data were computed for mean and frequency distribution, as well as analyzed to achieve the objectives. The analysis of variance (ANOVA) was performed for comparative analyses.

RESULTS

The results indicate that the "flexibility in use" and "perceptible information" principles were lacking among the Indian household products. Also, bathroom and toilet and furniture household products were most lacking in UD performance.

CONCLUSION

The findings of this research will enlighten the insights into the usefulness, usability, safety, and marketability of Indian household products. In addition, they will be helpful in promoting UD features and obtaining financial benefits from the Indian market.

A novel Q-learning algorithm based on improved whale optimization algorithm for path planning

Q-learning is a classical reinforcement learning algorithm and one of the most important methods of mobile robot path planning without a prior environmental model. Nevertheless, Q-learning is too simple when initializing Q-table and wastes too much time in the exploration process, causing a slow convergence speed. This paper proposes a new Q-learning algorithm called the Paired Whale Optimization Q-learning Algorithm (PWOQLA) which includes four improvements. Firstly, to accelerate the convergence speed of Q-learning, a whale optimization algorithm is used to initialize the values of a Q-table. Before the exploration process, a Q-table which contains previous experience is learned to improve algorithm efficiency. Secondly, to improve the local exploitation capability of the whale optimization algorithm, a paired whale optimization algorithm is proposed in combination with a pairing strategy to speed up the search for prey. Thirdly, to improve the exploration efficiency of Q-learning and reduce the number of useless explorations, a new selective exploration strategy is introduced which considers the relationship between current position and target position. Fourthly, in order to balance the exploration and exploitation capabilities of Q-learning so that it focuses on exploration in the early stage and on exploitation in the later stage, a nonlinear function is designed which changes the value of ε in ε-greedy Q-learning dynamically based on the number of iterations. Comparing the performance of PWOQLA with other path planning algorithms, experimental results demonstrate that PWOQLA achieves a higher level of accuracy and a faster convergence speed than existing counterparts in mobile robot path planning. The code will be released at https://github.com/wanghanyu0526/improveQL.git.

Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems

Deep learning-based Human Activity Recognition (HAR) systems received a lot of interest for health monitoring and activity tracking on wearable devices. The availability of large and representative datasets is often a requirement for training accurate deep learning models. To keep private data on users' devices while utilizing them to train deep learning models on huge datasets, Federated Learning (FL) was introduced as an inherently private distributed training paradigm. However, standard FL (FedAvg) lacks the capability to train heterogeneous model architectures. In this paper, we propose Federated Learning via Augmented Knowledge Distillation (FedAKD) for distributed training of heterogeneous models. FedAKD is evaluated on two HAR datasets: A waist-mounted tabular HAR dataset and a wrist-mounted time-series HAR dataset. FedAKD is more flexible than standard federated learning (FedAvg) as it enables collaborative heterogeneous deep learning models with various learning capacities. In the considered FL experiments, the communication overhead under FedAKD is 200X less compared with FL methods that communicate models' gradients/weights. Relative to other model-agnostic FL methods, results show that FedAKD boosts performance gains of clients by up to 20 percent. Furthermore, FedAKD is shown to be relatively more robust under statistical heterogeneous scenarios.

An external focus of attention enhances table tennis backhand stroke accuracy in low-skilled players

The aim of the study was to determine the impact of internal and external (proximal and distal) attentional focus on table tennis backhand stroke accuracy in low-skilled players. Fifty-one undergraduate physical education (PE) students were randomly assigned to 3 groups: Group G1 (IF) was instructed to focus on the hand holding the paddle, Group G2 (EFP) was instructed to focus on the ball, while Group G3 (EFD) was instructed to focus on targets marked on the tennis table. The experimental groups followed identical instructions except for the instruction about the focus of attention. Participants were asked to score as many points as possible by hitting the ball inside the three smallest targets marked on the tennis table. They were required to do so using a backhand stroke. The practice session consisted of 45 trials in three blocks of backhand (15 trials at each target). A special scoring system was used to determine the accuracy of the strokes. One of the most important findings from the current research was that groups with an external focus of attention revealed significant improvements in accuracy in the post-test, while the group with an internal focus of attention achieved low training effects. No significant difference was observed between G2 (EFP) and G3 (EFD) in the delayed retention test, which indicates that proximal and distal attentional focus had similar effects on table tennis backhand stroke accuracy in low-skilled players.

HomeMonitor: An Enhanced Device Event Detection Method for Smart Home Environment

As more and more smart devices are deployed in homes, the communication between these smart home devices and elastic computing services may face some risks of privacy disclosure. Different device events (such as the camera on, video on, etc.) will generate different data traffic during communication. However, the current smart home system lacks monitoring of these device events, which may cause the disclosure of private data collected by these devices. In this paper, we present our device event monitor system, HomeMonitor. HomeMonitor runs in the OpenWRT system and supports complete event monitoring for smart home devices. HomeMoitor solves the problem that machine learning models for detecting device events do not scale flexibly. It uses the network packet size and the direction of the device event for unique identification during training. When detecting, it only needs to get the packet size and timestamp and then query the policy table for signature matching to control the device events. We evaluated the effectiveness of HomeMonitor, and the experiments show that the match rate of our method is 98.8%, the false positive rate is 1.8%, and the detection time is only 16.67% for PINBALL. The results mean that our method achieves the balance of applicable protocol scope, detection performance, and detection accuracy.

Prevalence of Musculoskeletal Disorders and Their Associated Risk Factors among Furniture Manufacturing Workers in Guangdong, China: A Cross-Sectional Study

OBJECTIVE

This study aims to investigate the one-year prevalence and the associated factors of work-related musculoskeletal disorders (WMSDs) among furniture manufacturing workers in Guangdong, China.

METHODS

A cross-sectional study of 4181 (2953 males and 1228 females) furniture manufacturing workers was conducted between September 2019 and December 2019. All information about WMSDs was collected by the electronic version of Chinese Musculoskeletal Questionnaires (CMQ). Descriptive statistics and a binary logistic regression model were used to interpret the data.

RESULT

The overall prevalence of WMSDs was 31.57%. The WMSD symptoms most commonly occurred in the neck (16.77%), followed by the shoulders (14.90%), ankles/feet (14.64%), hands/wrists (13.30%), upper back (11.48%), and lower back (10.95%). Multiple logistic regression analysis revealed that several individual, labor organization, and ergonomics-related factors conferred significant risks to WMSDs at different body sites.

CONCLUSIONS

WMSDs remain the major occupational health problem for furniture manufacturing workers. Hence, some effective and feasible protective measures for furniture manufacturing workers are required in order to alleviate the health burden caused by WMSDs.