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【Objective】Due to its comb filter characteristics, Sampled optical Fiber Bragg Grating (SFBG) has attracted wide-spread attention and become a new focus in the research of optical fiber grating technology. The research work on designing optical filters with high channel numbers, flat tops, and narrow pass bands is of great importance in practical situation. Consequently, an Multi-Phase Shift Interpolated Sampling optical Fiber Grating (MPS-ISFBG) filter is proposed.【Methods】ISFBG has a large number of reflection channels and can introduce multiple flat top narrow transmission channels by inserting multi-phase shift. By optimizing the location of the two π phase-shift distributions of the inserted ISFBGs, an optical filters with 41 transmission channels was designed which covers the C-band with a channel interval of 100 GHz. Each channel has a flat-top response, a narrow 3 dB bandwidth (

According to the global cancer statistics in 2022 updated by the International Agency for Research on Cancer (IARC), there were nearly 20 million new cases of cancer and 9.7 million deaths. Lung cancer was the most commonly diagnosed cancer, accounting for nearly 2.5 million new cases (12.4% of all global cancers), followed by female breast cancer (11.6%), colorectal cancer (9.6%), prostate cancer (7.3%), and stomach cancer (4.9%). Lung cancer was also the leading cause of cancer deaths, with an estimated 1.8 million deaths (18.7%), followed by colorectal cancer (9.3%), liver cancer (7.8%), female breast cancer (6.9%), and stomach cancer (6.8%). Population-based projections suggest that the number of new cancer cases will reach 35 million by 2050. Increasing the investment in prevention and control measures targeting key cancer risk factors, including smoking, obesity, and infections, could save many lives globally and bring significant economic and social returns to countries in the coming decades.

Abstract: 68Ga is one of the most clinically valuable metal positron isotopes. Producing 68Ga through a 68Ge/68Ga generator is a relatively convenient way, while the parent nuclide 68Ge is mainly produced by cyclotrons. One of the most commonly used production methods is to obtain 68Ge through proton irradiation of Ga-Ni alloy targets. Accurately simulating the 68Ge yield is of great significance for Ga-Ni alloy target preparation, cyclotron irradiation plan making and production preparation. This paper proposes a method for calculating the theoretical yield of 68Ge produced by cyclotrons based on the Monte Carlo method. The energy loss and 68Ge theoretical yield of proton beam bombardment of Ga-Ni alloy targets under different conditions are calculated. The relevant results can provide reference for Ga-Ni alloy thickness design under different energy proton beam irradiation conditions. The experimental results match well with the calculated results. Finally, the reliability of the calculation results was verified through the cyclotron irradiation experiment, indicating that the theoretical calculation is instructive to the experimental results.

Machine learning methods have been widely used in the investigations of the complex plasmas. In this paper, we demonstrate that the unsupervised convolutional neural network can be applied to obtain the melting line in the two-dimensional complex plasmas based on the Langevin dynamics simulation results. The training samples do not need to be labeled. The resulting melting line coincides with those obtained by the analysis of hexatic order parameter and supervised machine learning method.

● An improved wash-off model integrated with rainfall pollution and SCS-CN is presented.● Nash-Sutcliffe efficiency coefficients of the enhanced model increased by 2%, 8%, 3% for chemical oxygen demand, total N, total P and 100% for NH4+-N.● Two pollution modes dominated by land and rainfall pollutant were identified.● Refined modeling indicated 12% runoff within 15 min includes 80% to 90% the pollutant load. The growing need to mitigate rainfall-runoff pollution, especially first flush, calls for accurate quantification of pollution load and the refined understanding of its spatial-temporal variation. The wash-off model has advantages in modeling rainfall-runoff pollution due to the inclusion of two key physical processes, build-up and wash-off. However, this disregards pollution load from wet precipitation and the relationship between rainfall and runoff, leading to uncertainties in model outputs. This study integrated the Soil Conservation Service curve number (SCS-CN) into the wash-off model and added pollutant load from wet precipitation to enhance the rainfall-runoff pollution modeling. The enhanced wash-off model was validated in a typical rural-residential area. The results showed that the model performed better than the established wash-off model and the commonly-used event mean concentrations method, and identified two different modes of pollution characteristics dominated by land pollution and rainfall pollution, respectively. In addition, the model simulated more accurate pollutant concentrations at high-temporal-resolution. From this, it was found that 12% of the total runoff contained 80% to 95% of the total load for chemical oxygen demand, total N, and total P, whereas it contained only 15% of the total load for NH4+-N. The enhanced model can provide deeper insights into non-point pollution mitigation.

● Erhai Lake basin faces the duel challenge of enhancing water protection and increasing farmer income.● A new framework indicates the key strategies for tackling the multiple challenges of agricultural green development in Erhai.● The needs for interdisciplinary research innovation and smallholder enabled technology transformation identified.● Building trust and partnerships between farmers, citizens, local government, industry and extension services should be prioritized.● Agricultural green development in Erhai can serve as a model for other high-altitude lake basins. Pollution of high-altitude lake basins by agriculture and rural activities, and the control of this pollution, have received increasing attention from academic research and government policy in China. Series of restrictions and regulations have been implemented to protect the surface water quality. These restrictions and regulations have greatly impacted and transformed the agricultural systems and rural livelihoods surrounding these lake basins. Using Erhai Lake basin in Yunnan Province as a case study, three main challenges were identified for concurrently decreasing pollution in the lake and increasing farmer income. It is contended that scientifically-sound environmental protection policies and agricultural green development practices are key to reversing the current situation. This will help to protect the lake from pollution while smallholder farmers will be able to produce healthy food in an environmentally sustainable manner, and with a fair remuneration for all the services farmers provide to the society.

In view of the low accuracy of objective automatic evaluation of fabric sewing flatness, an objective evaluation model of fabric sewing flatness based on feature parameters and deep belief network (DBN) is proposed. Firstly, image preprocessing including image grayscale processing, median filter denoising, and histogram equalization enhancement was performed on the image of the fabric sewing sample to extract the texture features of the fabric, so as to obtain a fabric feature image with higher quality and facilitate subsequent processing. Then the gray level co-occurrence matrix was constructed, and the four key feature parameters of energy, entropy, contrast and correlation of fabric image were extracted at 0°, 45°, 90° and 135°. On this basis, an automatic evaluation model of fabric sewing flatness based on DBN was constructed, and the model was trained using sewing images. Last it was verified by the extracted fabric sewing images. The results show that the evaluation accuracy of the model reaches 98.74%. Compared with the two methods of multiple linear regression model and BP network model, the proposed evaluation method can effectively evaluate the flatness of sewing objectively and provide a theoretical basis for the quality control of fabric garment appearance.

BackgroundClusterin, a glycoprotein implicated in Alzheimer’s disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals.MethodsWe gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities.ResultsPathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN