Computed Tomography Imaging Features of Pulmonary Sequestration

Background: Pulmonary sequestration (PS), generally diagnosed using computed tomography pulmonary angiography (CTPA), is a rare congenital developmental malformation of the lung that is characterized by nonfunctional lung tissue, independent of the normal lung tissue. This paper summarizes the imaging features of the supplying arteries and draining vessels in patients with PS with an aim to assist in timely clinical diagnosis and operation guidance. Materials and Methods: A total of 55 patients with PS diagnosed using CTPA from multiple clinical centers were retrospectively analyzed. Data included demographic characteristics, imaging features, disease location, isolation type, and the features of supplying and draining vessels, as shown on CTPA images. Results: Of the 55 patients reviewed, 3 (5.45%) were children, 3 (5.45%) were adolescents, and 49 (89.09%) were adults; the mean age was 44 years. Fifty-four (98.18%) patients had intralobar sequestration and one (1.82%) had extralobar sequestration. PS was noted 3.5 times more frequently in the left lower lobe than in the right lower lobe. For the supplying arteries, 47 (85.45%) were derived from the descending thoracic aorta, 1 (1.82%) from the abdominal aorta, 7 (12.73%) from the celiac axis, and 1 (1.82%) from the bronchial artery. The draining vessels were the pulmonary veins in 49 patients (89.09%), the umbilical vein in 1 (1.82%), the venae intercostal in 1 (1.82%), the pulmonary arteries in 11 (20.00%), and the vessels were not shown on the images in 2 patients (3.64%). Conclusion: Clinical presentations of PS are non-specific and can be easily missed or misdiagnosed. However, CTPA can help to improve the diagnostic accuracy and identify the supplying arteries and draining vessels, which significantly contribute to surgical planning

DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular, given a medical image, DeltaNet employs three steps to generate a report: 1) first retrieving related medical reports, i.e., the historical reports from the same or similar patients; 2) then comparing retrieved images and current image to find the differences; 3) finally generating a new report to accommodate identified differences based on the conditional report. We evaluate DeltaNet on a COVID-19 dataset, where DeltaNet outperforms state-of-the-art approaches. Besides COVID-19, the proposed DeltaNet can be applied to other diseases as well. We validate its generalization capabilities on the public IU-Xray and MIMIC-CXR datasets for chest-related diseases. Code is available at \url{https://github.com/LX-doctorAI1/DeltaNet}

Subsynchronous oscillation analysis method based on broad learning system

This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO

Coordinated optimization control strategy of hydropower and thermal power AGC units

With the rapid increase of penetration level of new energy power generation, the construction of auxiliary service market is facing new challenges. The stricter requirements of the power plant unit AGC are proposed by the power system. How to coordinate and control the output of hydropower and thermal power units to increase the use of hydropower while meeting the requirements of AGC regulation is a problem to be solved. Under the condition of interconnection of units in the whole network, an optimal control strategy for joint commissioning of hydropower and thermal power plants that meets the performance conditions of ‘two rules’ is proposed in this paper. The strategy establishes the connection between hydropower and thermal power units. Combined with the output characteristics and economic characteristics of hydropower units and thermal power units, a coordinated optimization control strategy model of hydropower and thermal power AGC units suitable for the ’two rules’ assessment rules is proposed for each objective and constraint condition in the joint scheduling of hydropower and thermal power. The optimal mathematical model with multiple objectives is constructed to realize the distribution of AGC among different types of power plants and units, which can give full play to the regulation ability of hydropower and thermal power. The proposed strategy achieves the efficient use of clean energy and reduces the grid’s overall operating costs to achieve energy conservation and emission reduction. Simulation results verify the superiority of the proposed strategy

Ln(III)-MOFs (Ln = Tb, Eu, Dy, and Sm) based on triazole carboxylic ligand with carboxylate and nitrogen donors with applications as chemical sensors and magnetic materials

<p>Using a triazole carboxylic ligand (H₂L = 4-(1<i>H</i>-1,2,4-triazol-1-yl) isophthalic acid), four water-stable lanthanide metal–organic frameworks (Ln(III)-MOFs) (<b>1-Ln</b>, Ln(III) = Tb, Eu, Dy, and Sm), [Ln(L)(HL)(H₂O)₂], where the deprotonated H₂L ligands have two different coordination modes: L²⁻ and HL⁻ [(a): η²μ₂χ², η²μ₁χ²; (b): η²μ₁χ²], have been synthesized by solvothermal reaction and characterized by elemental analysis, FT-IR spectroscopy, powder X-ray diffraction (PXRD), and thermogravimetric analysis (TGA). Single-crystal X-ray diffraction analyses show that Ln(III)-MOFs are isostructural with 2D-layered structures with uncoordinated carboxylic and triazole groups. The luminescent properties of <b>1-Tb</b> in aqueous solution containing different cationic solutions and small organic solvents have been explored under ultraviolet irradiation at room temperature. The high quenching constant <i>K</i>_<i>SV</i> values and low detection limits indicate that <b>1-Tb</b> exhibits extremely high detection sensitivity and selectivity toward Fe³⁺ ions and nitrobenzene; <b>1-Tb</b> can keep its original network and be reused after the sensing experiments, which provide us with an optical material for detecting Fe³⁺ ions and nitrobenzene. Magnetic studies show that antiferromagnetic exchange interactions exist between Dy(III) ions in <b>1-Dy</b>.</p