A Review of Forest Resources and Forest Biodiversity Evaluation System in China

China is a country rich in diverse forest ecosystems due to the large span of the country, complex topography, and multiple climate regimes. In this paper, the basic information of forest resources in China was briefly introduced and the current state in the measurements of forest biodiversity and the establishment of forest biodiversity index systems in related studies were reviewed. The results showed that a lot of studies on forest biodiversity have been conducted mostly at landscape or stand level in China and the commonly used biodiversity indicators were identified and compared. Several comprehensive forest biodiversity index systems were proposed. However, there are still some problems during the construction of forest biodiversity assessment system. Due to the late establishment of biodiversity monitoring system in China, the availability of data that could be included in a forest biodiversity index system is limited, which hurdles the precise assessment of forest biodiversity. It is suggested to develop long-term monitoring stations and keep data recording consistently. Concerns should also be given to the construction of the framework of the forest biodiversity index system and the determination of the indicators’ weight. The results will provide reference for the establishment of national or regional forest biodiversity evaluation indicator systems in China

Relationship between adherence to anti-diabetic medication and depression among patients with diabetes mellitus in three selected Chinese hospitals

Purpose: To determine the relationship between adherence to anti-diabetic medication and depression among patients with diabetes mellitus in three hospitals in Chinese.Methods: This research utilized a quantitative and descriptive design, and included 200 diabetic patients who fulfilled the inclusion criteria for recruitment through a convenient sampling technique. The study applied Beck’s depression inventory II scale for assessment of depression, and a questionnaire for adherence to anti-diabetic medication.Results: A total of 64 (32 %) participants had diabetes for 6 to 10 years. There was a high level of adherence to anti-diabetic medication in 96 patients (48 %); 74 participants (37 %) had moderate adherence to anti-diabetic medication, while 30 patients (15 %) had low adherence. A majority of the patients (181, 90.5 %) had no depression. Six (6) patients (3 %) had mild mood disturbance, 2 patients (1 %) had borderline clinical depression, while 11patients (5.5 %) experienced moderate depression.Adherence to anti-diabetic medication was not associated with depression (p = 0.068). However, depression was associated with age ˃ 50 years (p = 0.041), female sex (p = 0.043), long duration of illness (> 5-years) (p = 0.048), and presence of one or more comorbidities (p = 0.049).Conclusion: There was no association between adherence to anti-diabetic medication and depression among diabetic patients. Keywords: Adherence to anti-diabetic medication, Beck’s depression inventory II, Depression, Diabetes mellitu

Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings

Optimum Design for Mechanical Structures and Material Properties of the Dual-Elbow-Bar Mechanism

According to the kinematic equation of the die-cutting machine with the dual-elbow-bar mechanism, the angular acceleration curve figure can be obtained exactly through the analysis of MATLAB program when the die-cutting machine runs at the highest speed (6000 r/h). The material properties of the cam driving mechanism are analyzed by QFD (Quality Function Deployment) method, and the optimum design project is presented based on conjugate cam driving mechanism. The reverse design and the outline of conjugate cam mechanism are obtained by MATLAB program. The kinematical form of optimized design conjugate cam driving mechanism is simulated and analyzed by ADAMS software. The results show that the optimum design mechanism could raise the highest speed of the die-cutting machine up to 7500 r/h and improve the overall performance of the machine

Polyneuropathy as Novel Initial Manifestation in a Case of “Nonsecretory” POEMS Syndrome with Sjögren’s Syndrome

POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy, and skin changes) is a paraneoplastic syndrome driven by plasma cell dyscrasias. We report a patient with novel initial manifestation of polyneuropathy, which was considered due to Sjögren’s syndrome but with poor response to methylprednisolone (120 mg/d) and intravenous immunoglobulin (IVIg). Further investigation by imaging tests and following biopsy eventually confirmed the diagnosis of POEMS syndrome secondary to solitary plasmocytoma. To our knowledge, this is the first reported case of POEMS syndrome with Sjögren’s syndrome occurring in the absence of a peripheral monoclonal gammopathy, highlighting the diagnostic challenges posed by this disease and reviewing the diagnostic role of (18) F-FDG PET/CT in POEMS syndrome

Hyperspectral Nondestructive Detection of Maturity of Preserved Eggs Using Deep Learning Combined with Two-Dimensional Correction Spectral Image

In this study, hyperspectral imaging was used for nondestructive detection of preserved eggs at different maturity levels during the pickling period. First, the optimal waveband was determined based on the one-dimensional spectra and two-dimensional correlation spectra in the time-series mode, separately. Then, the modeling effects of traditional machine learning and the improved ResNet20_SE model in the optimal waveband were compared, and the results showed that the improved ResNet20_SE model was better; the overall recognition accuracy was 97.29% for the synchronous spectral dataset, and the average detection speed for a single image was 24.62 ms. Finally, the better synchronous spectral dataset ResNet20_SE model was applied to the hyperspectral pixel spectral image to calculate the value of each pixel point, and a pseudo-color technique was used for the visual detection of the spatial distribution of preserved egg maturity during the pickling process. The results of this study showed that hyperspectral imaging combined with deep learning is useful for nondestructive detection of preserved egg maturity during curing, which can lay a theoretical foundation for high-throughput online sorting of preserved egg maturity in the future