Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Double-face intelligent hole position planning method for precision blasting in roadways using a computer-controlled drill jumbo

To solve the uneven burden of same-type holes reducing the blasting efficiency due to the limitation of drilling equipment,we need a double-face program-controlled planning method for hole position parameters used on a computer-controlled drilling jumbo.The cross-section splits into even and uneven areas.It also considers the uneven burden at the hole’s entrance and bottom.In the uneven area,various qualifying factors are made to optimize the hole spacing and maximize the burden uniformity,combined with the features of the area edges and gridbased segmentation methods.The hole position coordinates and angles in the even area are derived using recursion and iteration algorithms.As a case,this method presents all holes in a 4.8 m wide and 3.6 m high cross-section.Compared with the design produced by the drawing method,our planning in the uneven area improved the standard deviation of the hole burden by 40%.The improved hole layout facilitates the evolution of precise,efficient,and intelligent blasting in underground mines.

HY-PC:Enabling Consistent Positioning and Communication Using Visible Light

Visible light(VL)plays an important role in achieving high-precision positioning and low bit error radio(BER)data communication.However,most VL-based systems can not achieve positioning and communication,simultaneously.There are two problems:1)the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance,2)in the hybrid systems,the lost data bits in the inter-frame gap(IFG)are hard to recover,which affects positioning and communication performance.Therefore,in this article,we propose a novel VL-based hybrid positioning and communication system,named HY-PC system,to solve the above problems.First,we propose the robust T-W mapping for recognizing specific Light Emitting Diodes(LEDs),which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication rate.Furthermore,we also propose the novel linear block coding and bit interleaving mechanism,which can recover the lost data bits in the IFG and improve data communication performance.Finally,we use commercial off-the-shelf devices to implement our HY-PC system,extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication,simultaneously.

Dynamic Visible Light Positioning Based on Enhanced Visual Target Tracking

In visible light positioning systems,some scholars have proposed target tracking algorithms to balance the relationship among positioning accuracy,real-time performance,and robustness.However,there are still two problems:(1)When the captured LED disappears and the uncertain LED reappears,existing tracking algorithms may recognize the landmark in error;(2)The receiver is not always able to achieve positioning under various moving statuses.In this paper,we propose an enhanced visual target tracking algorithm to solve the above problems.First,we design the lightweight recognition/demodulation mechanism,which combines Kalman filtering with simple image preprocessing to quickly track and accurately demodulate the landmark.Then,we use the Gaussian mixture model and the LED color feature to enable the system to achieve positioning,when the receiver is under various moving statuses.Experimental results show that our system can achieve high-precision dynamic positioning and improve the system’s comprehensive performance.

MBB-IoT:Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

Distributed Denial of Service(DDoS)attacks have always been a major concern in the security field.With the release of malware source codes such as BASHLITE and Mirai,Internet of Things(IoT)devices have become the new source of DDoS attacks against many Internet applications.Although there are many datasets in the field of IoT intrusion detection,such as Bot-IoT,ConstrainedApplication Protocol–Denial of Service(CoAPDoS),and LATAM-DDoS-IoT(some of the names of DDoS datasets),which mainly focus on DDoS attacks,the datasets describing new IoT DDoS attack scenarios are extremely rare,and only N-BaIoT and IoT-23 datasets used IoT devices as DDoS attackers in the construction process,while they did not use Internet applications as victims either.To supplement the description of the new trend of DDoS attacks in the dataset,we built an IoT environment with mainstream DDoS attack tools such as Mirai and BASHLITE being used to infect IoT devices and implement DDoS attacks against WEB servers.Then,data aggregated into a dataset namedMBB-IoTwere captured atWEBservers and IoT nodes.After the MBB-IoT dataset was split into a training set and a test set,it was applied to the training and testing of the Random Forests classification algorithm.The multi-class classification metrics were good and all above 90%.Secondly,in a cross-evaluation experiment based on Support Vector Machine(SVM),Light Gradient Boosting Machine(LightGBM),and Long Short Term Memory networks(LSTM)classification algorithms,the training set and test set were derived from different datasets(MBB-IoT or IoT-23),and the test performance is better when MBB-IoT is used as the training set.

An Intelligent Algorithm for Solving Weapon-Target Assignment Problem:DDPG-DNPE Algorithm

Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.

甲磺酸阿帕替尼治疗二线化疗失败上皮性卵巢癌的疗效分析

目的:探讨甲磺酸阿帕替尼治疗二线化疗失败上皮性卵巢癌(epithelial ovarian cancer,EOC)患者的疗效与安全性。方法:收集2017年9月至2018年11月46例天津医科大学肿瘤医院收治的二线化疗失败的复发性EOC患者的临床资料,分析甲磺酸阿帕替尼的疗效及治疗相关的不良事件,并评估其安全性。结果:46例患者的中位随访时间为12个月,中位总生存(overall survival,OS)时间为6(2~15)个月。客观缓解率(objective response rate,ORR)为26.1%(12/46),疾病控制率(disease control rate,DCR)为86.9%(40/46)。46例患者中30例(65.2%)出现不良反应,主要为1~2级,最常见的治疗相关不良反应为高血压占39.1%(18/46),手足皮肤综合征占30.4%(14/46),仅1例患者出现3级治疗相关性高血压,所有患者的1、2级不良事件均能迅速缓解,且甲磺酸阿帕替尼治疗后患者耐受性良好。结论:甲磺酸阿帕替尼对于二线化疗失败的EOC患者可能是一种安全且有效的治疗选择,但仍需开展大规模的临床试验进一步研究。

乳腺癌患者就业准备度和重返工作岗位现况及其相关因素研究

目的旨在调查女性乳腺癌患者的就业准备度和重返工作岗位现况,并分析其相关因素。方法本研究采用混合研究方法。2018年12月—2019年6月在湖南省某医院乳腺科共招募乳腺癌患者300例,最终有192例乳腺癌患者参与量性研究。采用患者一般情况问卷、认知症状量表(Cognitive Symptom Checklist,CSC)、患者健康问卷(the nine-item Patient Health Questionnaire,PHQ-9),简易疲劳量表(Brief Fatigue Inventory,BFI)、工作能力指数(Work Ability Index,WAI)量表和林氏就业准备(Lam Assessment of Employment Readiness,LASER)量表对乳腺癌患者进行调查。其中返回工作岗位的41例乳腺癌患者参与质性研究,通过一组开放式问题对其进行访谈,收集重返工作岗位的相关因素。结果结果显示,41例(21.35%)乳腺癌患者在治疗后重返工作岗位。乳腺癌患者CSC得分为9.00(6.00,15.25)分,WAI的得分为5.00(3.50,9.75)分,BFI总分为26.00(14.75,42.00)分,PHQ-9总分为8.00(5.25,17.00)分,LASER总分为(50.35±11.90)分。多元回归分析显示,癌症晚期、认知受损、抑郁、疲劳和工作能力水平低是乳腺癌患者工作准备情况的影响因素(P<0.05)。质性研究结果提炼出两个方面主题:促进乳腺癌患者重返工作岗位的因素包括较高的职业技能水平、更有力的社会支持和灵活的工作时间安排;乳腺癌患者重返工作岗位的障碍因素包括压力、对工作技能缺乏信心、抑郁和身体疲劳。结论乳腺癌患者的就业准备度水平较低。护士和其他医疗保健提供者可协助制订相关干预措施,以提高乳腺癌患者的就业准备度水平并最终实现重返工作岗位。

Investigation of Movement and Deposition Behaviors of Solid Particles in Hydraulic Water Reservoir via the CFD‑DEM Coupling Method

Solid contamination existing as solid particles in power fluid transmission systems may lead to transmission performance reduction,system failures,and component damage.The hydraulic reservoir will deposit the contamination and store hydraulic fluid.To investigate its purification ability for solid contamination,experiments and simulations for the motion and deposition status of the typical hydraulic system particles are carried out to reveal the interaction of particles and fluid in hydraulic water reservoirs.The results show that the CFD-DEM coupling method could predict the accurate deposition position of iron particles and sand particles when ignoring the small-scale turbulence effect in the flow field.Besides,the particle motion traces and deposition patterns in the reservoir illustrate that the flow development on the bottom surface results in the particles turning,and particles tend to settle in the low flow energy position.The motion of particles is also linked to particles Stokes number,and the same-size sand particles are easily driven by the fluid.The contribution of this paper could provide a guide for predicting the particle motion and deposition pattern in the hydraulic reservoir.

Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network:A Large Multicenter Study

Background:Current studies have confirmed that fetal congenital heart diseases(CHDs)are caused by various factors.However,the quantitative risk of CHD is not clear given the combined effects of multiple factors.Objective:This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios(RRs).Methods:Pregnant women who underwent fetal echocardiography(N=16,086 including 3,312 with CHD fetuses)were analyzed.Twenty-six maternal and fetal factors were obtained.A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to find the environmental factors that directly and indirectly associated with outcome,and the probability of fetal CHD in the two groups is predicted through a junction tree reasoning algorithm,so as to obtain RR for fetal CHD under different exposure factor combinations.Taking into account the effect of gestational week on the accuracy of model prediction,we conducted sensitivity analysis on gestational week groups.Results:The single-factor analysis showed that the RRs for the numbers of births,spontaneous abortions,and parental smoking were 1.50,1.38,and 1.11(P<0.001),respectively.The risk gradually increased with the synergistic effect of ranging from one to more environmental factors above.The risk was higher among subjects with five synergistic factors,including the number of births,upper respiratory tract infection during early pregnancy,anemia,and mental stress as well as a history of spontaneous abortions or parental smoking,than in those with less than 5 factors(RR=2.62 or 2.28,P<0.001).This result was consistent across the participants grouped by GWs.Conclusion:We identified six factors that were directly associated with fetal CHD.A higher number of these factors led to a higher risk of CHD.These findings suggest that it is important to strengthen healthcare and prenatal counseling for women with these factors.

Early Identification and Diagnosis of Adrenal Crisis after Retroperitoneal Laparoscopic Unilateral Adrenalectomy

The occurrence of adrenal crisis after retroperitoneal laparoscopic unilateral adrenalectomy is usually concealed.If not timely diagnosis and treatment,it may cause shock,and even lead to death.It is very difficult to distinguish the clinical manifestations of adrenal crisis from nausea,vomiting,fatigue,gas separation from the lower diaphragm,abdominal pain,hypotension,hypertension,fever and hypothermia after operation.This makes it very difficult to identify and diagnose adrenal crisis early.This article mainly discusses the early recognition,diagnosis and treatment of adrenal crisis after unilateral adrenalectomy by retroperitoneoscope.

Analysis and Defense of Network Attacking Based on the Linux Server

The kernel of the Linux server is analyzed to find out the main cause of the server’s denial of service when it is attacked. In the kernel, when the connection request information memory is full, the new connection request is discarded. Therefore, the printk function was used to alert the kernel output log when the memory was full, the processing of discarding the connection request in the kernel was changed, and the function tcp_syn_flood_action was applied to full memory processing. In the function tcp_syn_flood_action, the free function was used to release the memory according to the condition, so that the new connection request has a storage space, thereby offering the server’s normal service. Finally, the proposed defense technology is verified to be effective.

Validation Research on the Application of Depthwise Separable Convolutional Al Facial Expression Recognition in Non-pharmacological Treatment of BPSD

One of the most obvious clinical reasons of dementia or The Behavioral and Psychological Symptoms of Dementia(BPSD)are the lack of emotional expression,the increased frequency of negative emotions,and the impermanence of emotions.Observing the reduction of BPSD in dementia through emotions can be considered effective and widely used in the field of non-pharmacological therapy.At present,this article will verify whether the image recognition artificial intelligence(AI)system can correctly reflect the emotional performance of the elderly with dementia through a questionnaire survey of three professional elderly nursing staff.The ANOVA(sig.=0.50)is used to determine that the judgment given by the nursing staff has no obvious deviation,and then Kendall's test(0.722**)and spearman's test(0.863**)are used to verify the judgment severity of the emotion recognition system and the nursing staff unanimously.This implies the usability of the tool.Additionally,it can be expected to be further applied in the research related to BPSD elderly emotion detection.

Multiple interface coupling on natural tourmaline enables high-efficiency removal of antibiotic:Superior property and mechanism

Reasonably designing highly active,environmentally friendly,and cost-effective catalysts for efficient elimination of pollutants from water is desirable but challenging.Herein,an efficient heterogeneous photo-Fenton catalyst tourmaline(TM)/tungsten oxide(WO_(3-x))(named TW10)containing tungsten/boron/iron(W/B/Fe)synergistic active centers and 90%of cheap natural tourmaline(TM)mineral rich in Fe and B elements.The TW10 catalyst can quickly activate peroxymonosulfate(PMS)to generate massive active free radicals,which may induce the rapid and efficient degradation of tetracycline(TC).The TW10/PMS/Visible light system can effectively degrade up to 98.7%of tetracycline(TC)in actual waters(i.e.seawater,Yellow River,and Yangtze River water),and the catalytic degradation rates reach 1.65,5.569,and 2.38 times higher than those of TM,WO_(3-x),and commercial P25(Degussa,Germany),respectively.In addition,the catalyst can be recycled and reused multiple times.Electron spin resonance spectroscopy(EPR),X-ray photoelectron spectroscopy(XPS),and liquid chromatograph-mass spectrometer(LC-MS)analyses confirm that the synergistic catalytic effect of W/B/Fe sites on the TW10 catalyst accelerates the electron transfer between Fe(Ⅱ)and Fe(Ⅲ),as well as between W(Ⅴ)and W(Ⅵ),and thus promotes the rapid degradation of TC.The catalytic reaction mechanism and degradation pathway of TC were explored.This work provides a feasible route for the design and development of new eco-friendly and efficient catalyst.

Alkali-resistant NO_(x) reduction over FeVO_(4)/TiO_(2)catalysts via regulating the electron transfer between Fe and V

The presence of alkali metals in exhaust gas from stationary resources causes a grand challenge for the practical application of selective catalytic reduction(SCR)of NO_(x) with NH_(3).Here,alkali-resistant NO_(x) reduction has been successfully implemented via tailoring the electron transfer over Fe and V species on FeVO_(4)/TiO_(2)catalysts.The strong interaction between Fe and V induced electron transfer from V to Fe and strengthened the adsorption and activation of NH_(3)and NO over active VO_(x) sites.In the presence of K_(2)O,the strong electron withdrawing effect of Fe offset the electron donating effect of K on the VO_(x) species,thus protecting the active species VO_(x) to maintain the NO_(x) reduction ability.The enhanced adsorption and activation of NH_(3) allowed SCR reaction to proceed via E-R mechanism even after K_(2)O poisoning.This work elucidated the electronic effects on the alkali metals resistance of traditional ferric vanadate SCR catalysts and provided a promising strategy to design SCR catalysts with superior alkali resistance.

中国土壤动物多样性监测网络建设、进展与展望

土壤动物作为陆地生态系统的重要组成部分,调控着生态系统结构和功能的完整性,在生物多样性维持、能量流动和物质循环等方面发挥着关键作用。在当前气候变化和人类活动的持续影响下,土壤动物多样性锐减,使部分土壤动物面临灭绝风险,需要尽快重视土壤动物多样性的监测、保护和恢复工作。基于此,2016年土壤动物多样性监测网络作为中国生物多样性监测与研究网络(Sino BON)的重要组成部分正式建立,初步形成了包含森林、农田、海岛和荒漠生态系统的土壤动物多样性监测网络体系,为推动和完善生物多样性监测的标准化、数据共享和多尺度生物多样性变化评估奠定了良好的基础。本文概述了21世纪以来全球土壤动物多样性监测网络的发端与形成,简介了我国土壤动物多样性监测网络的建立和目标,整理了土壤动物多样性监测网络建立以来的代表性研究进展,主要介绍了国家尺度土壤动物标本的收集与鉴定、典型山地垂直自然带监测、大型固定样地动态监测及长期控制实验样地的土壤动物多样性监测,旨在总结我国土壤动物多样性监测工作的进展与不足,展望未来的研究方向,为加强土壤动物多样性的多尺度空间格局及其维持机制、土壤动物多样性的保护和资源合理利用提供基础数据支撑。

Comparative risk of acute kidney injury among cancer patients treated with immune checkpoint inhibitors

With the development and introduction of immune checkpoint inhibitors(ICIs)in cancer patients,immune-related side effects have increasingly attracted attention.However,the risks of immune-related renal toxicity are poorly characterized.In this study,we performed a network meta-analysis(NMA)of ICI-related randomized clinical trials(RCTs)to elucidate the comparative risk of acute kidney injury(AKI)in cancer patients receiving different ICIs.We also sought to identify other factors potentially affecting the risk of AKI.PubMed and EMBASE were searched for peer-reviewed trial reports published between January 2000 and May 2021.Eligible studies were RCTs studying ICIs in cancer patients and reporting AKI data.We performed a frequentist NMA to evaluate the risk ratios for grade 1-5 and grade 3-5 AKI between the treatment groups.We also assessed the absolute incidence of AKI in the ICI-containing arm using traditional direct meta-analysis.Once significant heterogeneity was detected in a traditional direct meta-analysis,multivariable meta-regression analysis was applied to identify factors that significantly affected the absolute incidence of AKI.A total of 85 RCTs were included in this study.In the NMA for the risk of grade 1-5 and 3-5 AKI,ipilimumab showed a significantly higher risk than avelumab and durvalumab,whereas 1 mg/kg nivolumab plus 3 mg/kg ipilimumab(N1I3)showed a significantly higher risk than other groups.In terms of treatment ranking,durvalumab±low-dose tremelimumab and avelumab were consistently among the top three safest treatments for grade 1-5 or 3-5 AKI,whereas N1I3,ipilimumab and tremelimumab were consistently among the top three treatments with the highest risk for grade 1-5 or 3-5 AKI.Compared with other cancers,renal cell carcinoma and urothelial carcinoma showed a significantly higher risk of AKI.The incidence of AKI was significantly higher with ICI+chemotherapy than with ICI monotherapy.In this NMA involving largescale up-to-date ICI trials,we demonstrated the comparative safety of existing ICI drugs for grade 1-5 and grade 3-5 AKI.Based on data from the ICI arms of these trials,we also revealed several potential risk factors for immune-related AKI,including tumor type and treatment paradigm.

Intrinsic Kinematics of the Tibiotalar and Subtalar Joints during Human Walking based on Dynamic Biplanar Fluoroscopy

Accurate knowledge of the kinematics of the in vivo Ankle Joint Complex(AJC)is critical for understanding the biomechanical function of the foot and assessing postoperative rehabilitation of ankle disorders,as well as an essential guide to the design of ankle–foot assistant devices.However,detailed analysis of the continuous 3D motion of the tibiotalar and subtalar joints during normal walking throughout the stance phase is still considered to be lacking.In this study,dynamic radiographs of the hindfoot were acquired from eight subjects during normal walking.Natural motions with six Degrees of Freedom(DOF)and the coupled patterns of the two joints were analyzed.It was found that the movements of the two joints were mostly in opposite directions(including rotation and translation),mainly in the early and late stages.There were significant differences in the Range of Motion(ROM)in Dorsiflexion/Plantarflexion(D/P),Inversion/Eversion(In/Ev),and Anterior–Posterior(AP)and Medial–Lateral(ML)translation of the tibiotalar and subtalar joints(p<0.05).Plantarflexion of the tibiotalar joint was coupled with eversion and posterior translation of the subtalar joint during the impact phase(R^(2)=0.87 and 0.86,respectively),and plantarflexion of the tibiotalar joint was coupled with inversion and anterior translation of the subtalar joint during the push-off phase(R^(2)=0.93 and 0.75,respectively).This coordinated coupled motion of the two joints may be a manifestation of the AJC to move flexibly while bearing weight and still have stability.

Deep-Learning-Assisted Underwater 3D Tactile Tensegrity

The growth of underwater robotic applications in ocean exploration and research has created an urgent need for effective tactile sensing.Here,we propose an underwater 3-dimensional tactile tensegrity(U3DTT)based on soft self-powered triboelectric nanogenerators and deep-learning-assisted data analytics.This device can measure and distinguish the magnitude,location,and orientation of perturbations in real time from both flow field and interaction with obstacles and provide collision protection for underwater vehicles operation.It is enabled by the structure that mimics terrestrial animals’musculoskeletal systems composed of both stiff bones and stretchable muscles.Moreover,when successfully integrated with underwater vehicles,the U3DTT shows advantages of multiple degrees of freedom in its shape modes,an ultrahigh sensitivity,and fast response times with a low cost and conformability.The real-time 3-dimensional pose of the U3DTT has been predicted with an average root-mean-square error of 0.76 in a water pool,indicating that this developed U3DTT is a promising technology in vehicles with tactile feedback.

Hydrothermally stable metal oxide-zeolite composite catalysts for low-temperature NO_(x) reduction with improved N_(2) selectivity

Development of hydrothermally stable,low-temperature catalysts for controlling nitrogen oxides emissions from mobile sources remains an urgent challenge.We have prepared a metal oxide-zeolite composite catalyst by depositing Mn active species on a mixture support of CeO_(2)/Al_(2)O_(3) and ZSM-5.This composite catalyst is hydrothermally stable and shows improved low-temperature SCR activity and significantly reduced N_(2)O formation than the corresponding metal oxide catalyst.Comparing with a Cu-CHA catalyst,the composite catalyst has a faster response to NH_(3) injection and less NH_(3) slip.Our characterization results reveal that such an oxide-zeolite composite catalyst contains more acidic sites and Mn^(3+)species as a result of oxide-zeolite interaction,and this interaction leads to the generation of more NH_(4)^(+)species bound to the Br?nsted acid sites and more reactive NOxspecies absorbed on the Mn sites.Herein,we report our mechanistic understanding of the oxide-zeolite composite catalyst and its molecular pathway for improving the low-temperature activity and N_(2) selectivity for NH_(3)-SCR reaction.Practically,this work may provide an alternative methodology for low-temperature NO_(x) control from diesel vehicles.