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A study on the use of a machine learning algorithm for the level 1 trigger decision in the Jiangmen Underground Neutrino Observatory (JUNO) experiment is presented. JUNO is a medium baseline neutrino experiment under construction in China, with the main goal of determining the neutrino mass hierarchy. A large liquid scintillator (LS) volume will detect the electron antineutrinos issued from nuclear reactors. The LS detector is instrumented by around 20 000 large photomultiplier tubes (PMTs). The hit information from each PMT will be collected into a central trigger unit for the level 1 trigger decision. The current trigger algorithm used to select a neutrino signal event is based on a fast vertex reconstruction. We propose to study an alternative level 1 (L1) trigger in order to achieve a similar performance as the vertex fitting trigger but with fewer logic resources by using a firmware implemented machine learning model at the L1 trigger level. We treat the trigger decision as a classification problem and train a multilayer perceptron (MLP) model to distinguish the signal events with energy higher than a certain threshold from noise events. We use JUNO software to generate datasets that include 100k physics events with noise and 100k pure noise events coming from PMT dark noise. For events with energy higher than 100 keV, the L1 trigger based on the converged MLP model can achieve an efficiency of higher than 99%. After the training performed on simulations, we successfully implemented the trained model into a Kintex 7 field-programmable gate array (FPGA). We present the technical details of the neural network development and training, as well as its implementation in the hardware with the FPGA programming. Finally, the performance of the L1 trigger MLP implementation is discussed. [ABSTRACT FROM AUTHOR]

Background: Aflatoxin (AF) ingestion through contaminated foodstuffs causes at least 250 000 deaths every year from hepatocellular carcinoma in China and sub-Saharan Africa. Objective: The main objective of the study was to determine the aflatoxin levels of oils in South Gondar, Ethiopia, and oils purchased from retail markets in Guangzhou, China. Methods: We used a rapid, sensitive, and selective HPLC-tandem mass spectrometry (MS/MS) method for the determination of aflatoxins in edible oils from China and Ethiopia using immunoaffinity column cleaning. Results: The level of contamination for Ethiopian oils ranged between 0.07 and 145.59 µg/kg for total aflatoxins. Of the 27 edible oil samples from Guangzhou, China, the total concentration of aflatoxins (AFB1 + AFB2 + AFG1 + AFG2) ranged between 0.03 and 2.23 µg/kg. Conclusions: The study concluded that the peanut oils from Ethiopia were contaminated with aflatoxins higher than the allowable limit set by many countries while the oils from China were safe for human consumption. Highlights: We first describe an HPLC-MS/MS method for the determination of aflatoxins in 48 edible oil samples from China and Ethiopia using immunoaffinity column cleaning. This is the first preliminary study done on Ethiopian edible oils, giving policy-makers and future researchers baseline data. It is also used to assess the aflatoxin levels of the Chinese edible oils from Guangzhou. Therefore, conducting a comparative study points out the severity of the problem and helps to formulate a new national standard for policy-makers, making this study imperative. [ABSTRACT FROM AUTHOR]

Background Cervical cancer screening reduced cancer morbidity and mortality in developed countries. Health education interventions are expected to enhance screening and early detection. Thus, this review is aimed to see the effectiveness of the interventions in cervical cancer screening uptake. Methods Online databases (PubMed/MEDLINE/PubMed Central, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science and Google Scholar) were searched for all published eligible research articles in the past 12 years (as of January 2005–2017). A total of 17 research articles were included. The interventions were classified as 'individual level', 'community level' and 'cultural sensitive educations' which contains various interventions in their content as compared with usual care. A quality coding system was assessed using Cochrane checklists and rated by each researcher independently and the average score was given accordingly. This study was registered in PROSPERO 2017: CRD42017060405. Results The review dovetailed 17 studies. Ten studies (58.82%) were conducted in the United States, three in Iran (17.65%) and one each in Malaysia, China, Cameroon and Nigeria (23.53%). Almost all levels of the interventions boosted the screening uptake and the Pap test. However, the individual level health education interventions were prioritized in many of the studies. Conclusion The review indicated that health education interventions have immense contributions in boosting the screening uptake. However, the effectiveness varies with study setting, populations and the way of delivery. Therefore, the limited quality of the studies indicated that further research is required to develop a simple and effective intervention to boost cervical cancer screening uptake. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) is a critical air pollutant affecting health and the environment. However, existing monitoring station networks often fail to adequately capture the regional distribution of NO 2 , indicating a need for enhanced sampling strategies. This study focuses on Southwest Fujian in China and introduces a high-precision NO 2 background map to delineate spatial stratified heterogeneity. Subsequently, the Mean of Surface with Non-homogeneity (MSN) method was employed to optimize the design of the NO 2 monitoring network, proposing the addition of 125 new stations. The Bayesian Kriging analysis, utilized to evaluate the optimized network, resulted in a coefficient of determination (R2) of 0.87, a mean absolute percentage error (MAPE) of 9.61%, a mean absolute error (MAE) of 2.75 μg/m3 and a root mean square error (RMSE) of 2.47 μg/m3. The improved accuracy and efficiency of the NO 2 monitoring network were validated against the background map. This research underscores the effectiveness of integrating precise mapping techniques with strategic network optimization for superior environmental monitoring outcomes. • NO 2 s Monitoring stations deployment based on background map is vital to assessing health exposure. • Our method uses a NO 2 background map for spatial variations and stratified area stratification. • Improved NO 2 Monitoring stations sampling efficiency through accurate stratification of spatial heterogeneity. [ABSTRACT FROM AUTHOR]

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics. The protocol has been employed for 10 months during the mass production and validation of all the electronics that will be installed in JUNO. A total number of 6950 electronic boards were tested with an acceptance yield of 99.1 %. [ABSTRACT FROM AUTHOR]

Cost-effective, representative and spatial coverage sampling designs are required to monitor the effects of potentially toxic elements (PTEs) in the soil. This study aims to evaluate the minimum sample sizes and placement of soil sampling designs to monitor and characterize the spatial variation of the PTEs (Cu, Zn, Cd, Cr, Pb, and Ni) in the soils. However, there is no standardized approach for evaluating the optimum soil sample size and monitoring location because of the spatial heterogeneity of PTEs in the soil. As a result, three broad techniques were applied. The first step was to use Global Moran's I and q-statistic values to describe the variability of soil PTEs and select appropriate evaluation methods. Second, using simple random sampling (SRS), ordinary kriging (OK), and Mean of Surface with Non-homogeneity (MSN), we estimated and evaluated soil PTEs in the current soil sampling schemes. Finally, MSN and spatial simulated annealing (SSA) optimization techniques were used to assess the required sample sizes and placements in the existing designs. Method performance was evaluated using a standard error (SE) and a relative standard error of the mean (RSE). Except for Zn and Cd, all PTEs tested showed heterogeneous distributions over the area. The MSN lowered the predicted SE by 79-86 % compared with SRS. The OK approach also outperformed the SRS method regarding mean estimated values of soil PTEs by 42-57 %. After SSA refined the initial design, the predicted SE by MSN of Cr and Zn was lowered by 13 % and 39 %, respectively. The MSN was effective with small sample sizes, reducing sample sizes and surveying costs by 39 % after SSA optimized the existing sample numbers. Thus, integrating various sampling strategies may be efficient for building optimal sample designs to monitor PTEs in the soil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

High concentrations of potentially toxic elements (PTE) create global environmental stress due to the crucial threat of their impacts on the environment and human health. Therefore, determining the concentration levels of PTE and improving their prediction accuracy by sampling optimization strategy is necessary for making sustainable environmental decisions. The concentrations of five PTEs (Pb, Cd, Cr, Cu, and Zn) were compared with reference values for Shanghai and China. The prediction of PTE in soil was undertaken using a geostatistical and spatial simulated annealing algorithm. Compared to Shanghai's background values, the five PTE mean concentrations are much higher, except for Cd and Cr. However, all measured values exceeded the reference values for China. Pb, Cu, and Zn levels were 1.45, 1.20, and 1.56 times the background value of Shanghai, respectively, and 1.57, 1.66, 1.91 times the background values in China, respectively. The optimization approach resulted in an increased prediction accuracy (22.4% higher) for non-sampled locations compared to the initial sampling design. The higher concentration of PTE compared to background values indicates a soil pollution issue in the study area. The optimization approach allows a soil pollution map to be generated without deleting or adding additional monitoring points. This approach is also crucial for filling the sampling strategy gap.