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Purpose: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI. Methods: We collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin's concordance correlation coefficient (CCC). Results: A total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80–0.82, PPV of 84.5–86.1%, and sensitivity of 77.6–81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland–Altman plots revealed minor tumor volume differences with 0.22–1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively. Conclusion: The nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images. [ABSTRACT FROM AUTHOR]

Rice production is the agricultural activity with the highest energy consumption and carbon emission intensity. Water and fertilizer management constitutes an important part of energy input for rice production and a key factor affecting greenhouse gas emissions from paddy fields. Water–fertilizer integration management (AIM) is an automated water and fertilizer management system for large-scale rice production, which can effectively save water and fertilizer resources. At present, the energy utilization and environmental impact of AIM in rice production are not clear. To clarify whether AIM is a water and fertilizer management measure that combines energy conservation and carbon emission reduction, a comparative study between the widely used farmers' enhanced water and fertilizer management (FEM) in China and AIM was conducted in this paper. Field experiments were conducted to evaluate the rice yield, carbon emission, energy utilization, and economic benefits of the two management methods. The results showed that AIM reduced water and fertilizer inputs, energy inputs, and economic costs by 12.18–28.57%, compared to FEM. The energy utilization efficiency, energy profitability, and energy productivity under AIM were improved by 11.30–12.61%. CH4 and N2O emissions and carbon footprint were reduced by 20.79%, 6.51%, and 16.39%, respectively. Compared with FEM, AIM can effectively improve the utilization efficiency of water and fertilizer resources and reduce carbon emissions. This study presents a mechanized water and fertilizer management approach suitable for large-scale rice production systems in China. By analyzing rice yield, resource utilization efficiency, and environmental benefits, AIM can serve as a crucial management strategy for enhancing productivity, economic returns, and environmental conservation within profitable rice production systems. In the future, further investigation into the impact of AIM on the microbial mechanisms underlying rice yield formation and greenhouse gas emissions is warranted. [ABSTRACT FROM AUTHOR]

In the context of China's 2020 dual carbon goals of peak CO2 emissions by 2030 and carbon neutrality by 2060, the security of multi-energy systems is increasingly challenged as clean energy continues to be supplied to the system. This paper proposes a risk assessment and enhancement strategy for distributed energy stations (DESs) based on a security boundary. First, based on the coupling relationship between different energy sources and combining the mutual support relationships between different pieces of equipment, a security boundary for DESs was constructed. Second, based on the characteristics of different sources of equipment failure, the real-time failure probabilities of equipment and pipelines were calculated in order to obtain the security risks of DES operation states based on the security boundary. Finally, for equipment and pipelines at high risk, an economic security enhancement strategy is proposed, and the Pareto solution set is solved using a multi-objective algorithm. The analysis shows that the proposed method can effectively quantify the security risks of energy systems in real time, and the proposed enhancement strategy takes into account both economics and system security. [ABSTRACT FROM AUTHOR]

Background: Orbital decompression is an important surgical procedure for treatment of Graves' ophthalmopathy (GO), especially in women. It is reasonable for balanced orbital decompression of the lateral and medial wall. Various surgical approaches, including endoscopic transnasal surgery for medial wall and eye-side skin incision surgery for lateral wall, are being used nowadays, but many of them lack the validity, safety, or cosmetic effect. Patients and methods: Endoscopic orbital decompression of lateral wall through hairline approach and decompression of medial wall via endoscopic transnasal surgery was done to achieve a balanced orbital decompression, aiming to improve the appearance of proptosis and create conditions for possible strabismus and eyelid surgery afterward. From January 29, 2016 to February 14, 2017, this surgery was performed on 41 orbits in 38 patients with GO, all of which were at inactive stage of disease. Just before surgery and at least 3 months after surgery, Hertel's ophthalmostatometer and computed tomography (CT) were used to check proptosis and questionnaires of GO quality of life (QOL) were completed. Findings: The postoperative retroversion of eyeball was 4.18±1.11 mm (Hertel's ophthalmostatometer) and 4.17±1.14 mm (CT method). The patients' QOL was significantly improved, especially the change in appearance without facial scar. The only postoperative complication was local soft tissue depression at temporal region. Obvious depression occurred in four cases (9.76%), which can be repaired by autologous fat filling. Interpretation: This surgery is effective, safe, and cosmetic. Effective balanced orbital decompression can be achieved by using this original and innovative surgery method. The whole manipulation is safe and controllable under endoscope. The postoperative scar of endoscopic surgery through hairline approach is covered by hair and the anatomic structure of anterior orbit is not impacted. [ABSTRACT FROM AUTHOR]