485 research outputs found


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    Currently, for tunnels, the design centerline and design cross-section with timestamps are used for dynamic three-dimensional (3D) modeling. However, thisapproach cannot correctly reflect some qualities of tunneling or some special cases,such as landslips. Therefore, a dynamic 3D model of a tunnel based onspatiotemporal data from survey cross-sections is proposed in this paper. Thismodel can not only playback the excavation process but also reflect qualities of aproject typically missed. In this paper, a new conceptual model for dynamic 3Dmodeling of tunneling survey data is introduced. Some specific solutions areproposed using key corresponding technologies for coordinate transformation of cross-sections from linear engineering coordinates to global projection coordinates,data structure of files and database, and dynamic 3D modeling. A 3D tunnel TINmodel was proposed using the optimized minimum direction angle algorithm. Thelast section implements the construction of a survey data collection, acquisition, anddynamic simulation system, which verifies the feasibility and practicality of thismodeling method

    PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network

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    Positron emission tomography (PET) serves as an essential tool for diagnosis of encephalopathy and brain science research. However, it suffers from the limited choice of tracers. Nowadays, with the wide application of PET imaging in neuropsychiatric treatment, 6-18F-fluoro-3, 4-dihydroxy-L-phenylalanine (DOPA) has been found to be more effective than 18F-labeled fluorine-2-deoxyglucose (FDG) in the field. Nevertheless, due to the complexity of its preparation and other limitations, DOPA is far less widely used than FDG. To address this issue, a tracer conversion invertible neural network (TC-INN) for image projection is developed to map FDG images to DOPA images through deep learning. More diagnostic information is obtained by generating PET images from FDG to DOPA. Specifically, the proposed TC-INN consists of two separate phases, one for training traceable data, the other for rebuilding new data. The reference DOPA PET image is used as a learning target for the corresponding network during the training process of tracer conversion. Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data. Notably, the reversible model employs variable enhancement technique to achieve better power generation. Moreover, image registration needs to be performed before training due to the angular deviation of the acquired FDG and DOPA data information. Experimental results exhibited excellent generation capability in mapping between FDG and DOPA, suggesting that PET tracer conversion has great potential in the case of limited tracer applications

    DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

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    We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://github.com/FudanDISC/DISC-LawLLM

    Depression and anxiety in cervical degenerative disc disease: Who are susceptible?

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    BackgroundPre-operative depression and anxiety are associated with poorer patient-reported outcomes following cervical spine surgery. Identification of and interventions for these disorders are key to preventing related negative effects. However, most spine surgeons do not routinely evaluate mental health disorders. Few studies have investigated which patients with cervical degenerative disc diseases (CDDD) are susceptible to depression and anxiety.ObjectiveTo determine the factors associated with depression and anxiety in patients with CDDD.MethodsThree hundred twelve patients with CDDD were recruited in this cross-sectional case-control study. Patients underwent a structured interview to acquire demographic and clinical characteristic information, which included the Neck Disability Index (NDI), modified Japanese Orthopedic Association (mJOA), and Visual Analog Scale (VAS) for neck/arm pain. Depression and anxiety were evaluated using the Zung Self-Rating Depression and Anxiety Scales. Univariate and multivariate logistic regression analyses were used to identify factors associated with depression and anxiety.ResultsOf all patients, 102 (32.7%) had depression and 92 (29.5%) had anxiety. Two hundred six (66.0%) patients with neither depression nor anxiety were defined as the control group. Univariate analysis indicated that gender, educational level, occupation type, Charlson comorbidity index, symptom duration, symptomatology, surgery history, NDI, mJOA, VAS-neck, and VAS-arm scores were associated with depression and anxiety (except for symptom duration for anxiety). Multivariate logistic regression analysis indicated that females [odds ratio (OR) 1.81, 95% confidence interval (CI) 1.01–3.23], physical work (OR 2.06, 95% CI 1.16–3.65), poor mJOA score (ORmoderate 2.67, 95% CI 1.40–5.07; ORsevere 7.63, 95% CI 3.85–15.11), and high VAS-neck score (OR 1.24, 95% CI 1.11–1.39) were independent risk factors for depression. Physical work (OR 1.84, 95% CI 1.01–3.35), poor mJOA score (ORmoderate 2.66, 95% CI 1.33–5.33; ORsevere 9.26, 95% CI 4.52–18.99), and high VAS-neck score (OR 1.34, 95% CI 1.19–1.51) were independent risk factors for anxiety.ConclusionApproximately one-third of patients with CDDD had depression or anxiety. Patients who engaged in heavy work and had severe symptoms (poor mJOA and high VAS-neck scores) are susceptible to depression and anxiety. Additionally, female patients are susceptible to depression. Our findings may help identify CDDD patients with depression and anxiety in clinical practice

    Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease

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    Rationale and introductionIt is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Materials and methodsPatients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827–0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816–0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720–0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages

    Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

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    Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

    Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks