Browsing by Author "Ding L"

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    Cooling demand reduction with nighttime natural ventilation to cool internal thermal mass under harmonic design-day weather conditions
    (Elsevier Ltd, 2025-02-01) Li M; Shen X; Wu W; Cetin K; Mcintyre F; Wang L; Ding L; Bishop D; Bellamy L; Liu M
    Cooling demand is steadily increasing across different climate zones due to global warming. A potential solution for cooling demand reduction is applying nighttime natural ventilation to cool internal thermal mass. However, a simplified and accurate modelling framework to assess the technique is still missing. The goal of the study is to build that framework integrated with a validated internal thermal mass model and apply the framework to quantify the cooling demand reduction potential in a space with different thermal mass and envelope configurations and in different climate zones. Results show that using Granite as internal thermal mass is three times more effective than concrete to reduce peak cooling load. Adding too much internal thermal mass can create adverse effects on cooling load reduction. The optimum thickness of internal thermal mass is between 28 and 45 mm. Envelope construction also has an influence on the performance of nighttime cooling. Applying the technique in buildings with lightweight structures reduces peak cooling load by 35.9% more than heavyweight structures. As heavyweight structures delay the release of the daily absorbed heat and cause higher indoor air temperatures at night. The two belts between the Tropic of Cancer and 60 degrees north latitude, and between the Tropic of Capricorn and 45 degrees south latitude are suitable for nighttime natural ventilation of internal thermal mass, achieving the annual cooling demand reduction above 1.25 kWh m−2. In Dessert climate zones, the technique exhibits an extraordinary potential to reduce cooling demand, up to 6.67 kWh m−2 per year.
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    Effect of Caffeinated Chewing Gum on Maximal Strength, Muscular Power, and Muscle Recruitment During Bench Press and Back Squat Exercises
    (MDPI (Basel, Switzerland), 2025-07-28) Ding L; Liu J; Ma Y; Lei T-H; Barnes M; Guo L; Chen B; Cao Y; Girard O
    Background/Objectives: This study aims to investigate the effects of caffeinated chewing gum on maximal strength, muscular power, and neural drive to the prime movers during bench press and back squat in resistance-trained men. Methods: Sixteen resistance-trained males participated in a double-blind, randomized trial, chewing either caffeinated gum (4 mg/kg) or placebo gum on two separate occasions, seven days apart. After chewing for 5 min, participants performed a maximal strength test followed by muscular power assessments at 25%, 50%, 75%, and 90% of their one-repetition maximum (1RM), completing with 3, 2, 1, and 1 repetition (s), respectively, for bench press and back squat. Surface electromyography data were recorded for each repetition. Results: Caffeinated gum did not significantly improve one-repetition maximum (1RM) for bench press (p > 0.05), but increased mean frequency (MF) and median frequency (MDF) in anterior deltoid, pectoralis major, and biceps brachii (all p < 0.05) compared to placebo. For back squat, 1RM increased with caffeinated gum, along with higher MF and MDF in vastus medialis (all p < 0.05). Caffeinated gum also improved mean and peak velocities, and mean and peak power outputs at 25–75% 1RM during the bench press (all p < 0.05), along with elevated MDF in pectoralis major and biceps brachii (all p < 0.05). Similar improvements were seen in mean and peak velocities during the back squat at 25–90% 1RM (all p < 0.05), along with higher MF and MDF in vastus medialis and increased normalized root mean square activity in gluteus maximus (all p < 0.05). Conclusions: Caffeinated chewing gum (4 mg/kg) enhanced muscular power (25–75% 1RM) in the bench press and improved maximal strength and muscular power (25–90% 1RM) in the back squat by increasing muscle recruitment in resistance-trained men.
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    Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification
    (Springer Nature Switzerland AG, 2025-05) Yang B; Ding L; Li J; Li Y; Qu G; Wang J; Wang Q; Liu B
    Digital medical imaging, particularly pathology images, is essential for cancer diagnosis but faces challenges in direct model training due to its super-resolution nature. Although weakly supervised learning has reduced the need for manual annotations, many multiple instance learning (MIL) methods struggle to effectively capture crucial spatial relationships in histopathological images. Existing methods incorporating positional information often overlook nuanced spatial correlations or use positional encoding strategies that do not fully capture the unique spatial dynamics of pathology images. To address this issue, we propose a new framework named TMIL (Transformer-based Multiple Instance Learning Network with 2D positional encoding), which leverages multiple instance learning for weakly supervised classification of histopathological images. TMIL incorporates a 2D positional encoding module, based on the Transformer, to model positional information and explore correlations between instances. Furthermore, TMIL divides histopathological images into pseudo-bags and trains patch-level feature vectors with deep metric learning to enhance classification performance. Finally, the proposed approach is evaluated on a public colorectal adenoma dataset. The experimental results show that TMIL outperforms existing MIL methods, achieving an AUC of 97.28% and an ACC of 95.19%. These findings suggest that TMIL’s integration of deep metric learning and positional encoding offers a promising approach for improving the efficiency and accuracy of pathology image analysis in cancer diagnosis.