Development of Long-Range,Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests

Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption optimization.Unlike the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.

The Effect of Water Flow Velocity on Heat Collection Performance of Active Heat Storage and Release System for Solar Greenhouses

In order to explore the influence of water velocity on the heat collection performance of the active heat storage and release system for solar greenhouses,six different flow rates were selected for treatment in this experiment.The comprehensive heat transfer coefficient of the active heat storage and release system at the heat collection stage was calculated by measuring the indoor solar radiation intensity,indoor air temperature and measured water tank temperature.The prediction model of water temperature in the heat collection stage was established,and the initial value of water temperature and the comprehensive heat transfer coefficient were input through MATLAB software.The simulated value of water temperature was compared with the measured value and the results showed that the best heat transfer effect could be achieved when the water flow speed was 1.0 m3h-1.The average relative error between the simulated water tank temperature and the measured value is 2.70-6.91%.The results indicate that the model is established correctly,and the variation trend of water temperature can be predicted according to the model in the heat collection stage.

新兴可穿戴生理感知技术助力急性高原病的监测和辅助诊疗

急性高原病是高原特发病之一,其主要症状包括心慌、胸闷、头痛、乏力等,重症患者还会出现高原脑水肿和高原肺水肿,严重影响我国高原地区的国防安全与经济建设。大量研究证明了血氧饱和度、心率、睡眠和血压等生理参数对急性高原病的研究价值,日益成熟的可穿戴生理感知设备已能实现上述参数的实时监测。本文以可穿戴生理感知技术对急性高原病的实时监测和辅助决策作为研究对象,对国内外研究现状进行综述,并结合新兴医学数据挖掘方法,对基于实时数据的急性高原病诊断和预测做出未来发展趋势的讨论。

Transport and behavior of marine oil spill containing polycyclic aromatic hydrocarbons in mesocosm experiments

Polycyclic aromatic hydrocarbons(PAHs)are one of the most important groups in oil,and re sponsible for major toxic and/or carcinogenic impact on humans and wildlife.It is important to understand the behavior of PAHs in marine environment after an oil-spill incident.However,interaction between petroleum PAHs and microbial communities in a marine environment remains unclear.Therefore,a series of mesocosm experiments were conducted.in which water-accommodated fraction(WAF)of oil was generated to simulate an oil-spill scenario and to analyze the transport and behavior of marine oil spill containing PAHs with and without dispersants.Results indicate that the application of dispersant could increase the concentration of total PAHs in water column due mainly to significant increase in the concentration of highmolecular weight(HMW)PAHs at a lower removal rate.At the end of the 7-day experiment,significant amount of HMW PAHs were accumulated in sediment.In general,the application of dispersant did not increase the sediment uptake of PAHs but increased the PAHs concentration in water column.

Event-Triggered Moving Horizon Pose Estimation for Spacecraft Systems

An event-triggered moving horizon estimation strategy is proposed for spacecraft pose estimation.The error dual quaternion is used to describe the system state and construct the spacecraft attitude-orbit coupled model.In order to reduce the energy consumption on spacecraft,an event-triggered moving horizon estimator(MHE)is designed for real-time pose estimation with limited communication resources.The model mismatch caused by event-triggered is finally solved by solving the cost function of the min-max optimization problem.The system simulation model is built in Matlab/Simulink,and the spacecraft pose estimation simulation is carried out.The numerical results demonstrate that the designed estimator could ensure the estimation effect and save spacecraft communication and computing resources effectively.

Prediction of Lifespan of Tropical Cyclones Landing at China in Spring:A Case Study of Northward-moving-cluster Tracks

The recurving cluster of tropical cyclones(TCs)landing at China in spring has the characteristics of high frequency,strong intensity,severe influence and long lifespan,a better understanding of tropical cyclone tracks and their underlying mechanisms and more accurate prediction are thus of great value for the prevention and mitigation of TC-related disasters.Based on the best track dataset of typhoon during 1951-2018 from Shanghai Typhoon Institute(STI)of Chinese Meteorological Administration,the tracks of tropical cyclones landfalling over the Chinese coast during 1951-2018 are grouped into three clusters through finite mixture model.The ENSO index,PDO index and the 74 circulation indices of the National Climate Center are analyzed by the classification and regression tree(CART)algorithm for the first TC cluster(the northward-moving cluster).The prediction model based on the results of CART for spring track lifespan is built by a random sampling of the data during the 50 years(about 75%)as a model of the training set,and the training accuracy is 82.46%.The 17-year(about 25%)remaining data are used for testing,with a prediction accuracy of 75%.This study suggests that the finite mixture model algorithm produces a better classification of the northward-going-cluster tracks of TC landfalling over China.In addition,the CART algorithm,which is used for the classification of track lifespan prediction model,not only shows high accuracy,but also the results are easily explained and understood.It provides a novel idea for forecasting the lifespan of tropical cyclones landing at China.

Systematic evaluation of IgG responses to SARS-CoV-2 spike protein-derived peptides for monitoring COVID-19 patients

Serological tests play an essential role in monitoring and combating the COVID-19 pandemic.Recombinant spike protein(S protein),especially the S1 protein,is one of the major reagents used for serological tests.However,the high cost of S protein production and possible cross-reactivity with other human coronaviruses pose unavoidable challenges.By taking advantage of a peptide microarray with full spike protein coverage,we analyzed 2,434 sera from 858 COVID-19 patients,63 asymptomatic patients and 610 controls collected from multiple clinical centers.Based on the results,we identified several S protein-derived 12-mer peptides that have high diagnostic performance.In particular,for monitoring the IgG response,one peptide(aa 1148-1159 or S2-78)exhibited a sensitivity(95.5%,95%CI 93.7-96.9%)and specificity(96.7%,95%CI 94.8-98.0%)comparable to those of the S1 protein for the detection of both symptomatic and asymptomatic COVID-19 cases.Furthermore,the diagnostic performance of the S2-78(aa 1148-1159)IgG was successfully validated by ELISA in an independent sample cohort.A panel of four peptides,S1-93(aa 553-564),S1-97(aa 577-588),S1-101(aa 601-612)and S1-105(aa 625-636),that likely will avoid potential cross-reactivity with sera from patients infected by other coronaviruses was constructed.The peptides identified in this study may be applied independently or in combination with the S1 protein for accurate,affordable,and accessible COVID-19 diagnosis.

Establishment and application of national reference panels for SARS-CoV-2 antigen detection kit

To develop a national reference panel for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)antigen detection kit and establish a quality standard.The cultures of SARS-CoV-2 and other pathogens were collected to establish a national reference panel for SARS-CoV-2 antigen detection.The stability and homogeneity of the reference panel were evaluated.Based on World Health Organization(WHO)guidance and nucleic acid quantitative results,a quality standard reference panel was established.Currently,three generations of SARS-CoV-2 antigen national reference materials with batch numbers 370095–202001,370095–202202,and 370095–202203 have been successfully established.These national reference panels comprised 8 positive samples,20 negative samples,1 repetitive sample,and 1 lower detection limit sample.The stability and homogeneity of the reference panel meet the requirements.The quality standards are as follows:the positive and negative coincidence rates are 8/8 and 20/20,respectively.The 10 test results of the medium and low-concentration repetitive reference materials should be positive,and the color rendering should be uniform(or the coefficient of variance should not be higher than 20.0%).The lower detection limit should be at least 5×105 U/mL(equivalent to copies/mL),and higher concentrations above the lower detection limit must be positive.A national reference panel for the SARS-CoV-2 antigen detection kit has been established.As the standard of SARS-CoV-2 antigen reagents,the reference panel has played a crucial role in the pre-marketing quality evaluation and post-marketing quality supervision in China.

Effects and regulation mechanisms of post-sinter annealing treatment on magnetic properties and microstructures of Nd-Fe-B sintered magnets with varying boron contents

In traditional Nd-Fe-B-based sintered magnets,the composition,role of each element and microstruc-tures have been extensively investigated globally since they were invented in 1983.However,the effects of boron(B)content and post-sinter annealing(PSA)on the microstructure and magnetic properties have been least studied so far and the relative mechanisms are not yet clear.In this paper,we investigated the influence of B on the magnetic performance and microstructure of Nd-Fe-B sintered magnets origi-nally containing copper(Cu),gallium(Ga)and titanium(Ti).It is shown that the intrinsic coercivity has a substantial increment of 2.86 kOe and the remanence has a slight reduction of 0.16 kGs when B content is reduced from 0.980 wt.%to 0.900 wt.%.Moreover,there is a coercivity increment of 27.3%and 65.3%for samples with 0.980 wt.%and 0.900 wt.%B content after PSA,respectively.It is shown that the im-pacts of B content and PSA are significant and their regulation mechanisms are worthwhile to be studied systematically.Furthermore,it is revealed by microstructural analysis that high coercivity of the sample with 0.900 wt.%B after PSA results from the uniform distribution of Ga,Cu,Nd,and the formation of RE 6(Fe,M)14(RE=Pr,Nd,M=Cu,Ga)compound in triple junction phases.The dilution of Fe content in grain boundary phases(GB phases)also plays an important role.It is found out that decrease of the re-manence is mainly due to reduction of the matrix phase and c-axis alignment degree.In this study,we explored a new path to develop Nd-Fe-B-based sintered magnets with high comprehensive properties by novel approaches through varying B content,PSA technique and co-adding trace elements.