DORA
DORA (De Montfort Open Research Archive) is De Montfort University's research repository. It forms the primary public and institutional record of DMU research outputs. The breadth of research at DMU means that these outputs include articles, conference papers, books, book chapters, and other material available in a digital form. The record for each item contains descriptive information as well as, where possible, a version of the final research output. DORA also provides access to DMU PhD theses. This includes most PhD produced from 2009 onwards.
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Recent Submissions
Research on tomato disease image recognition method based on DeiT
(Elsevier, 2024-10-30) Sun, Changxia; Song, Zhengdao; Li, Yong; Liu, Qian; Si, Haiping; Yang, Yingjie; Cao, Qing
Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production.
Enhancing Clinical Trial Outcome Prediction with Artificial Intelligence: A Systematic Review
(Elsevier, 2025-03-15) Qian, Long; Lu, Xin; Haris, Parvez; Zhu, Jianyong; Li, Shuo; Yang, Yingjie
Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review synthesizes AI methodologies impacting clinical trial outcomes, focusing on clinical text embedding, trial multimodal learning, and prediction techniques, while addressing practical challenges and opportunities.
Comparing 17β‐estradiol and progesterone concentrations in young, physically active females: Insights from plasma versus serum analysis
(Wiley, 2025-03-12) Rowland, Samantha N.; Da Boit, Mariasole; Tan, Rachel; Heaney, Liam M.; Bailey, Stephen J.
Serum measurements of 17β-estradiol and progesterone are widely used to verify menstrual cycle status and confirm contraceptive use, often through commercially available immunoassay kits. However, no studies have investigated whether blood collection tube chemistries influence hormone concentrations in young females, despite assays permitting the use of different biofluids with similar reference ranges. In this study, venous blood was sampled from physically active females (n = 25) using Ethylenediaminetetraacetic acid (EDTA) and serum vacutainers, and 17β-estradiol and progesterone concentrations were measured using competitive immunoenzymatic assays. Median plasma concentrations of 17β-estradiol and progesterone were 44.2% (plasma 40.75 vs. serum 28.25 pg/ml) and 78.9% (plasma 1.70 vs. serum 0.95 ng/ml) higher than serum concentrations, respectively (P < 0.001 for both). Strong positive correlations were observed between plasma and serum concentrations for 17β-estradiol (r = 0.72; P < 0.001) and progesterone (r = 0.89; P < 0.001). The mean bias and limits of agreement for plasma versus serum were 12.5 pg/ml (−20.6 to 45.5 pg/ml) for 17β-estradiol and 1.01 ng/ml (−5.6 to 7.6 ng/ml) for progesterone. Ovarian hormone levels were consistently higher in EDTA plasma compared with serum, with these matrices not yielding statistically equivalent results. Despite these differences, the strong correlations and good agreement suggest that both matrices are suitable for biomarker analysis. Researchers using EDTA plasma should account for the higher hormone concentrations when applying inclusion or exclusion criteria, because adjustments might be necessary to ensure appropriate participant classification.
Portion Estimation, Satiety Perception and Energy Intake Following Different Breakfast Portion Sizes in Healthy Adults
(Wiley, 2025-02-05) Kwiecien, Kinga; Santos-Merx, Lourdes; Sahota, Tarsem; Coulthard, Helen; Da Boit, Mariasole
Expected satiety is a key element in predicting meal portion size and food consumption; however, how this can be affected by different breakfast portion sizes is unknown. The study examined the impact of different breakfast portions on satiety, portion size, and energy intake and comprised an online survey and an experimental intervention. Sixteen adults (9 women, BMI: 24.9 ± 4.3 kg/m2) rated images of three portion sizes (small, standard, large) of the same breakfast using an ordinal scale. Subsequently, they were asked to self-prepare and consume ad libitum the three breakfast portions in a randomised order on different days and to complete a food diary. Satiety and portion size perception were re-measured upon consumption of each breakfast. For both the visual image and breakfast consumption, the small breakfast portion was rated as the smallest and least filling, while the large portion was rated as the largest and most filling (p < 0.05). When consuming the small breakfast, participants reported being hungrier and less full between breakfast and lunch (p < 0.05) and had a higher energy intake from lunch onward, due to more snacking (p < 0.05). However, the total daily energy intake was not different among the three breakfast portion sizes. Individuals seemed accustomed to predicting satiety and portion size from images. The consumption of the small breakfast was judged as not filling enough and was accompanied by a higher energy intake via energy-dense snacks. Based on these preliminary findings, breakfast size reduction may lead to unhealthy compensatory energy intake by snacking on energy-dense foods.
Experimental design for a novel co-flow jet airfoil
(Springer, 2023-12-01) Jiang, Hao; Yao, Weigang; Xu, Min
The Co-flow Jet (CFJ) technology holds significant promise for enhancing aero-dynamic efficiency and furthering decarbonization in the evolving landscape of air transportation. The aim of this study is to empirically validate an optimized CFJ airfoil through low-speed wind tunnel experiments. The CFJ airfoil is structured in a tri-sectional design, consisting of one experimental segment and two stationary segments. A support rod penetrates the airfoil, fulfilling dual roles: it not only maintains the structural integrity of the overall model but also enables the direct measurement of aerodynamic forces on the test section of the CFJ air-foil within a two-dimensional wind tunnel. In parallel, the stationary segments are designed to effectively minimize the interference from the lateral tunnel walls. The experimental results are compared with numerical simulations, specifically focusing on aerodynamic parameters and flow field distribution. The findings reveal that the experimental framework employed is highly effective in characterizing the aerodynamic behavior of the CFJ airfoil, showing strong agreement with the simulation data.