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The COVID-19 pandemic has made well-fitting face masks a critical piece of protective equipment for healthcare workers and civilians. While the importance of wearing face masks has been acknowledged, there remains a lack of understanding about the role of good fit in rendering protective equipment useful. In addition, supply chain constraints have caused some organizations to abandon traditional quantitative or/and qualitative fit testing, and instead, have implemented subjective fit checking. Our study seeks to quantitatively evaluate the level of fit offered by various types of masks, and most importantly, assess the accuracy of implementing fit checks by comparing fit check results to quantitative fit testing results. Seven participants first evaluated N95 and KN95 respirators by performing a fit check. Participants then underwent quantitative fit testing wearing five N95 respirators, a KN95 respirator, a surgical mask, and fabric masks. N95 respirators offered higher degrees of protection than the other categories of masks tested; however, it should be noted that most N95 respirators failed to fit the participants adequately. Fit check responses had poor correlation with quantitative fit factor scores. KN95, surgical, and fabric masks achieved low fit factor scores, with little protective difference recorded between respiratory protection options. In addition, small facial differences were observed to have a significant impact on quantitative fit. Fit is critical to the level of protection offered by respirators. For an N95 respirator to provide the promised protection, it must fit the participant. Performing a fit check via NHS self-assessment guidelines was an unreliable way of determining fit.

Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.

ObjectiveWe examined the ability of fabrics which might be used to create home-made face masks to filter out ultrafine (0.02–0.1 µm) particles at the velocity of adult human coughing.MethodsTwenty commonly available fabrics and materials were evaluated for their ability to reduce air concentrations of ultrafine particles at coughing face velocities. Further assessment was made on the filtration ability of selected fabrics while damp and of fabric combinations which might be used to construct home-made masks.ResultsSingle fabric layers blocked a range of ultrafine particles. When fabrics were layered, a higher percentage of ultrafine particles were filtered. The average filtration efficiency of single layer fabrics and of layered combination was found to be 35% and 45%, respectively. Non-woven fusible interfacing, when combined with other fabrics, could add up to 11% additional filtration efficiency. However, fabric and fabric combinations were more difficult to breathe through than N95 masks.ConclusionsThe current coronavirus pandemic has left many communities without access to N95 face masks. Our findings suggest that face masks made from layered common fabric can help filter ultrafine particles and provide some protection for the wearer when commercial face masks are unavailable.

The plant pathogen Sclerotinia sclerotiorum can cause serious losses on lettuce crops worldwide and as for most other susceptible crops, control relies on the application of fungicides, which target airborne ascospores. However, the efficacy of this approach depends on accurate timing of these sprays, which could be improved by an understanding of the environmental conditions that are conducive to infection. A mathematical model for S. sclerotiorum infection and disease development on lettuce is presented here for the first time, based on quantifying the effects of temperature, relative humidity (RH) and ascospore density in multiple controlled environment experiments. It was observed that disease can develop on lettuce plants inoculated with dry ascospores in the absence of apparent leaf wetness (required for spore germination). To explain this, the model conceptualises an infection court area containing microsites (in leaf axils and close to the stem base) where conditions are conducive to infection, the size of which is modified by ambient RH. The model indicated that minimum, maximum and optimum temperatures for ascospore germination were 0.0, 29.9 and 21.7°C respectively and that maximum rates of disease development occurred at spore densities >87 spores cm-2. Disease development was much more rapid at 80-100% RH at 20°C, compared to 50-70% RH and resulted in a greater proportion of lettuce plants infected. Disease development was also more rapid at 15-27°C compared to 5-10°C (85% RH). The model was validated by a further series of independent controlled environment experiments where both RH and temperature were varied and generally simulated the pattern of disease development well. The implications of the results in terms of Sclerotinia disease forecasting are discussed.

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

During the course of the COVID-19 pandemic, there have been suggestions that various techniques could be employed to improve the fit and, therefore, the effectiveness of face masks. It is well recognized that improving fit tends to improve mask effectiveness, but whether these fit modifiers are reliable remains unexplored. In this study, we assess a range of common \"fit hacks\" to determine their ability to improve mask performance. Between July and September 2020, qualitative fit testing was performed in an indoor living space. We used quantitative fit testing to assess the fit of both surgical masks and KN95 masks, with and without 'fit hacks', on four participants. Seven fit hacks were evaluated to assess impact on fit. Additionally, one participant applied each fit hack multiple times to assess how reliable hacks were when reapplied. A convenience of four participants took part in the study, three females and one male with a head circumference range of 54 to 60 centimetres. The use of pantyhose, tape, and rubber bands were effective for most participants. A pantyhose overlayer was observed to be the most effective hack. High degrees of variation were noted between participants. However, little variation was noted within participants, with hacks generally showing similar benefit each time they were applied on a single participant. An inspection of the fit hacks once applied showed that individual facial features may have a significant impact on fit, especially the nose bridge. Fit hacks can be used to effectively improve the fit of surgical and KN95 masks, enhancing the protection provided to the wearer. However, many of the most effective hacks are very uncomfortable and unlikely to be tolerated for extended periods of time. The development of effective fit-improvement solutions remains a critical issue in need of further development.

Abstract Quality problem or issue A number of challenges have been identified with current risk assessment practice in hospitals, including: a lack of consultation with a sufficiently wide group of stakeholders; a lack of consistency and transparency; and insufficient risk assessment guidance. Consequently, risk assessment may not be fully effective as a means to ensure safety. Initial assessment We used a V system developmental model, in conjunction with mixed methods, including interviews and document analysis to identify user needs and requirements. Choice of solution One way to address current challenges is through providing good guidance on the fundamental aspects of risk assessment. We designed a risk assessment framework, comprising: a risk assessment model that depicts the main risk assessment steps; risk assessment explanation cards that provide prompts to help apply each step; and a risk assessment form that helps to systematize the risk assessment and document the findings. Implementation We conducted multiple group discussions to pilot the framework through the use of a representative scenario and used our findings for the user evaluation. Evaluation User evaluation was conducted with 10 participants through interviews and showed promising results. Lessons learned While the framework was recommended for use in practice, it was also proposed that it be adopted as a training tool. With its use in risk assessment, we anticipate that risk assessments would lead to more effective decisions being made and more appropriate actions being taken to minimize risks. Consequently, the quality and safety of care delivered could be improved.

ObjectivesTo systematically review the evidence base for a systems approach to healthcare design, delivery or improvement.DesignSystematic review with meta-analyses.MethodsIncluded were studies in any patients, in any healthcare setting where a systems approach was compared with usual care which reported quantitative results for any outcomes for both groups. We searched Medline, Embase, HMIC, Health Business Elite, Web of Science, Scopus, PsycINFO and CINAHL from inception to 28 May 2019 for relevant studies. These were screened, and data extracted independently and in duplicate. Study outcomes were stratified by study design and whether they reported patient and/or service outcomes. Meta-analysis was conducted with Revman software V.5.3 using ORs—heterogeneity was assessed using I2 statistics.ResultsOf 11 405 records 35 studies were included, of which 28 (80%) were before-and-after design only, five were both before-and-after and concurrent design, and two were randomised controlled trials (RCTs). There was heterogeneity of interventions and wide variation in reported outcome types. Almost all results showed health improvement where systems approaches were used. Study quality varied widely. Exploratory meta-analysis of these suggested favourable effects on both patient outcomes (n=14, OR=0.52 (95% CI 0.38 to 0.71) I2=91%), and service outcomes (n=18, OR=0.40 (95% CI 0.31 to 0.52) I2=97%).ConclusionsThis study suggests that a systems approaches to healthcare design and delivery results in a statistically significant improvement to both patient and service outcomes. However, better quality studies, particularly RCTs are needed.PROSPERO registration numberCRD42017065920.

Continuous changes in society and the need for sustainable development demand updates in designing better built environments to respond to the variety of user needs. Notwithstanding the growing interest of research and the introduction of guidelines and standards on inclusion, diversity, equity, and accessibility, there are still several limitations in effectively and efficiently embedding such principles for the design of buildings and neighborhoods. Previous research demonstrated the critical need for innovative tools and methods to support professionals in designing responsive, inclusive spaces for an extended range of users. This article reports the results of a pilot study using the new IDEA Audit Tool for assessing how inclusion, diversity, equity, and accessibility are perceived by building occupants in a specific facility. The analysis of significant data provided by this study shows the challenges and highlights the benefits of the tool, including fostering an evidence-based decision-making process, speeding up the prioritization of critical design improvements, demonstrated through a six-month trial with a London-based inclusive design firm. The research-driven outcomes showcase the huge potential that the tool offers to improve the company strategy while evolving towards more inclusive, accessible spaces that foster diversity and equity, and has the potential to be replicated in several fields of action to raise awareness and improve the application of IDEA principles in all phases of the design process.

Co-design in healthcare settings requires teams to utilize each other's knowledge effectively, but practical guidance and simple methods for observing collaboration are often lacking. We tested whether a lightweight AI assistant that guides the process-and automatically logs who speaks, when, and how work progresses-can make teamwork easier to manage and easier to track. Six four-person teams completed the same five-phase session. The assistant nudged timing, turn-taking, and artifact hand-offs; all interactions were recorded in a shared workspace. We assessed usability and acceptance, expert-rated product quality (technical performance), perceived team performance, and self-rated technical contribution, and we summarized basic log signals of participation and pacing (e.g., turn-taking balance, average turn duration). Analyses were descriptive. All teams finished the protocol with complete logs. Outcomes were favorable (expert ratings averaged 4.18/5; perceived performance 6.14/7; self-rated contribution 4.08/5). Teams with more balanced participation and clearer pacing tended to report better performance, whereas simply having more turns did not. A process-guiding AI assistant can quantify teamwork behaviors as markers of collective intelligence and support reflection in everyday clinical co-design; future work will examine the generalizability of these findings across different sites.