A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single crystals.The carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of picoseconds.On the other hand,the observed spin dynamics indicate a longer time of about 120 ps.To further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Refinement of saliva microRNA biomarkers for sports-related concussion

Background:Recognizing sport-related concussion(SRC)is challenging and relies heavily on subjective symptom reports.An objective,biological marker could improve recognition and understanding of SRC.There is emerging evidence that salivary micro-ribonucleic acids(miRNAs)may serve as biomarkers of concussion;however,it remains unclear whether concussion-related miRNAs are impacted by exercise.We sought to determine whether40 miRNAs previously implicated in concussion pathophysiology were affected by participation in a variety of contact and non-contact sports.Our goal was to refine a miRNA-based tool capable of identifying athletes with SRC without the confounding effects of exercise.Methods:This case-control study harmonized data from concussed and non-concussed athletes recruited across 10 sites.Levels of salivary miRNAs within 455 samples from 314 individuals were measured with RNA sequencing.Within-subjects testing was used to identify and exclude miRNAs that changed with either(a)a single episode of exercise(166 samples from 83 individuals)or(b)season-long participation in contact sports(212 samples from 106 individuals).The miRNAs that were not impacted by exercise were interrogated for SRC diagnostic utility using logistic regression(172 samples from 75 concussed and 97 non-concussed individuals).Results:Two miRNAs(miR-532-5p and miR-182-5p)decreased(adjusted p<0.05)after a single episode of exercise,and 1 miRNA(miR-4510)increased only after contact sports participation.Twenty-three miRNAs changed at the end of a contact sports season.Two of these miRNAs(miR-26b-3p and miR-29c-3p)were associated(R>0.50;adjusted p<0.05)with the number of head impacts sustained in a single football practice.Among the 15 miRNAs not confounded by exercise or season-long contact sports participation,11 demonstrated a significant difference(adjusted p<0.05)between concussed and non-concussed participants,and 6 displayed moderate ability(area under curve>0.70)to identify concussion.A single ratio(miR-27a-5p/miR-30a-3p)displayed the highest accuracy(AUC=0.810,sensitivity=82.4%,specificity=73.3%)for differentiating concussed and non-concussed participants.Accuracy did not differ between participants with SRC and non-SRC(z=0.5,p=0.60).Conclusion:Salivary miRNA levels may accurately identify SRC when not confounded by exercise.Refinement of this approach in a large cohort of athletes could eventually lead to a non-invasive,sideline adjunct for SRC assessment.

基于等高线形状分析的LIDAR建筑物提取

基于等高线形状分析,提出了一种新的LIDAR(机载激光雷达)建筑物提取方法.首先利用LIDAR数据生成DSM(数字地面模型)等高线,然后根据等高线形状特征参数,从DSM等高线中提取由建筑物点云形成的等高线(建筑物等高线).最后,根据拓扑关系简化建筑物等高线,再利用建筑物轮廓线相互垂直或平行的特点,对建筑物等高线进行规则化,提取建筑物.实验结果表明,即使在地形起伏的情况下,该方法仍可提取出形状复杂的建筑物,准确率达80%以上.

基于等高线的表面估计滤波方法

提出一种基于等高线的滤波方法,它先由LIDAR数据生成数字表面模型,并内插出等高线,再根据DSM等高线的特征,如闭合性、首尾点距离、等高线的长度及等高线间距离等,通过设定阈值自动提取出属于自然地面的等高线线段,以获得初始的自然地面点,然后内插生成初始数字地面模型,最后使用迭代逼近法生成最终的(精确的)数字地面模型,即比较初始DTM与DSM,差值小于预设阈值的点视为DTM点,而差值大于预设阈值的点则标记为无数据点,最后,这些无数据点由选择的DTM点内插出。通过与现有表面估计的滤波方法的对比实验以及所提取地物轮廓线与航片的叠加对比试验,证明新方法可适用于地表起伏较大的地形,地物提取精度高、计算量小、效率高。

城市大比例尺真正射影像阴影与遮挡问题的研究

为解决城市大比例尺正射影像生成的问题,从真正射影像的关键技术入手,着重研究其中的阴影与遮挡问题并提出相应解决方法。将这些方法应用在美国丹佛城区的真正射影像生成的项目中,取得了令人满意的效果。

城市不同人群HIV高危行为对比分析——以昆明市为例

该文围绕经济赤贫、社会孤立和社会控制状态以及个体特征等对不同人群HIV高危行为的影响因素,以及高危行为的具体表现,设计问卷指标;对昆明市城镇150个HIV感染者、吸毒人群、流动人口和常住居民进行深入问卷调查,分析结果显示了不同人群HIV高危行为的差异,并对此作出了解释。

基于DLT算法的微型无人机(MUAV)视频影像的几何纠正方法

高精度真正射影像在灾害监测、紧急救援与反恐决策等应用领域有着广泛的应用前景。利用微型低空无人飞机获取高精度的视频影像流,对影像流进行重采样,借助直接线性变换(DLT)方法,以GPS、INS集成系统获取的摄像机外方位元素为初始值进行内方位元素解算,以此进行视频影像分割后单幅影像的几何纠正,给出了纠正影像的拼接结果。研究表明,在DEM/DSM、DOQQ的精度与视频影像精度匹配条件下,可获得高精度(亚米级)的真正射影像。

利用Avenue程序快速加载ArcView主题图例方法的研究

利用ArcView系统自身提供的图例加载方法完成视图中主题图例的加载需要经过多个步骤。针对Arc-View系统以上繁琐的加载过程,本文探讨了一种更为有效的方法,即利用ArcView提供的二次开发环境——Avenue语言,通过编写Avenue程序代码,实现主题图例的快速加载。通过建立Avenue程序与图形用户界面(GUI)工具按钮的"链接",从而实现视图View中主题图例加载操作的简单化。

吉兰泰盐湖盆地土壤重金属铬、汞、砷分布的多方法评价

选择西北旱区吉兰泰盐湖盆地为研究对象,以随机布点法对表土层(0~10 cm)、心土层(50 cm)、底土层(100 cm)的重金属铬(Cr)、汞(Hg)、砷(As)以及主要化学成分进行空间采样测定,揭示Cr、Hg、As的空间分布特征,研究确定其土壤环境背景值,并以此背景值为评价基准,利用单因子污染指数、地累积指数、生态风险指数、内梅罗综合污染指数、潜在生态风险综合指数、污染负荷指数等六种常用方法对表土进行评价及对比分析。结果表明:盐湖盆地表层土壤中Cr、Hg、As总体具有相似的空间分布特征;盐湖盆地土壤Cr、Hg、As背景值分别为27.89 mg·kg–1、0.039 mg·kg–1、12.83 mg·kg–1,对比宁夏、新疆等相关地区,盆地Cr背景值较低,Hg背景值较高,As元素相近;以背景值为基准进行污染评价,盆地表层土壤重金属污染顺序依次为Hg、Cr、As,简单指数法呈现Cr、Hg、As整体处于轻微污染状态,Hg存在的生态风险较Cr、As大,类似的,综合指数法表明盆地整体上处于轻微污染状态,综合生态风险相对较大;单因子污染指数法的土壤污染等级评价结果大于综合污染指数法;三种综合指数法均与三种简单指数法对Hg的评价结果具有显著相关性,表明盐湖盆地表层土壤中Cr、Hg、As形成受到成土母质、天然水文地球化学作用和气候特征条件等综合作用的影响,局部受到人类活动的扰动。

Prescribing physical activity to prevent and manage gestational diabetes

Gestational diabetes mellitus(GDM)is the most prevalent metabolic disorder during pregnancy.Women diagnosed with GDM have a substantially greater risk of developing type 2 diabetes within 5-10 years after delivery,and the risk is increased by excess body weight.Uncontrolled hyperglycemia during pregnancy is potentially harmful to both mother and fetus,resulting in a greater need for Caesarian-section deliveries,delivery of larger infants with more excess body fat,a greater risk of infant death and stillbirth,and an elevated risk of infant hypoglycemia immediately after birth.Fortunately,engaging in physical activity prior to and during pregnancy may lower the risk of developing GDM.Pregnant women should also be advised how to safely increase their physical activity during pregnancy and the postpartum period.An initial approach to becoming more physically active can simply be to encourage women to incorporate more unstructured physical activity into daily living,both before and during pregnancy.Giving women an appropriate exercise prescription can encourage them to participate in physical activity safely and effectively throughout pregnancy to prevent and/or manage GDM.Engaging in 30 min of moderate intensity physical activity on most,if not all,days of the week has been adopted as a recommendation for all pregnant women.

Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

数字建筑模型生成大比例尺真正射影像方法

为解决城市大比例尺正射影像生成中出现的遮挡和阴影等问题,提出基于CSG数字建筑模型生成大比例尺真正射影像的方法,应用在美国丹佛城区的真正射影像生成的项目中,取得了令人满意的效果.

Health-related fitness knowledge growth in middle school years:Individual-and school-level correlates

Background:Health-related fitness knowledge(HRFK)has been an essential concept for many health and physical education programs.There has been limited understanding and longitudinal investigation on HRFK growth.This longitudinal study examined HRFK growth and its individual-and school-level correlates in middle school years under 1 curriculum condition:Five for Life.Methods:Participants were 12,044 students from 47 middle schools.Data were collected at both individual/participant and school/institution levels.Individual-level variables included gender,grade,and HRFK test scores.School-level variables included percentage of students receiving free and reduced meals(FARM),student-to-faculty ratio for physical education,and school academic performance(SAP).We used hierarchical linear modeling to examine HRFK 3-year growth in relation to individual-and school-level correlates.Results:The average HRFK score at 6th grade for females was 42.81%±1.32%.The predicted HRFK growth was 17.06%±1.02%per year,holding other factors constant.A 1-standard deviation increase in FARM correlated with a 14.68%-point decrease in predicted test score(p=0.02).A 1-standard deviation increase in SAP was associated with an 11.90%-point increase in HRFK score.Males had a significantly lower growth rate than females during the middle school years(0.78%/year,p=0.02).Conclusion:The result showed that both individual-and school-level variables such as gender,FARM,and SAP influenced HRFK growth.Educators should heed gender differences in growth curves and recognize the correlates of school-level variables.

Tumor ablation with nanosecond pulsed electric fields

Liver cancer is one of the most malignant cancers. It is reported that 600 000 patients died from liver cancer every year. [1,2] Hepatocellular carcinoma (HCC) is a particular problem because symptoms are not evident until the disease has progressed and hepatitis B, which is prominent specific regions of Asia, is a common precursor of the disease. There are many liver cancer

Interactive Image Enhancement by Fuzzy Relaxation

In this paper, an interactive image enhancement （HE） technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.

Influences of landform as a confounding variable on SOM-NDVI association in semiarid Ordos Plateau

Soil organic matter （SOM） plays an important role in maintaining vegetation cover and thus mitigating land erosion of fragile terrestrial ecosystems such as in the Northern Ordos Plateau of China （NOPC）. However, little information is available on whether and how SOM varies spatially as an intrinsic characteristic of landform in NOPC. The objective of this study was to examine the spatial associations of SOM with landform and vegetation cover. The study was conducted in a 23,000-km2 area within NOPC because this area has landforms of mobile dunes （MD）, flat dunes （FD）, grassy sandy land （GSL）, flat sandy bedrocks （FSB）, and swamps and salt lakes （SW）, which are typical landforms in semiarid ecosystems. SOM was determined using a standard laboratory analysis method for 5 cm topsoil samples collected at 72 locations across the study area. In addition, the 250 m Multitem- poral Moderate Resolution Imaging Spectroradiometer （MODIS） imageries taken in the period from August 2006 to August 2010 were used to extract Normalized Difference Vegetation Index （NDVI） which in turn was used as the surrogate of vegetation cover. Classic and geostatistical methods were used to compare SOM concentration across different landforms. The results indicated that an area with a greater value for NDVI （i.e. better vegetation cover） tended to have a higher SOM concentration regardless of the landform types. However, the association between SOM and NDVI varied from one landform to another. The SW and GSL had a highest SOM concentration, while MD had a lowest concentration. For the study area as a whole and the FD, GSL, and MD, SOM was found to be the sole function of NDVI, whereas, for the FSB, SOM was influenced by several intrinsic variables, namely ground surface altitude, slope, and aspect, as well as NDVI. SOM for the SW landform was found to be a function of NDVI. Furthermore, SOM and NDVI exhibited a consistent spatial pattern of increasing from north to south and from west to east. The highest SOM concentration of 3.5% occurred along an east-westward belt, which is adjacent to water pathways, in the mid part of the study area.

运用Sensewear臂环和饮食日记加强青少年的能量平衡知识和积极性（英文）

目的:对青少年进行能量平衡教育对于有效控制体重至关重要。本研究使用Sensewear臂环和饮食日记来加强青少年的能量平衡知识和积极性。方法:共90名六年级学生被随机分为试验组(n=46)和对照组(n=44),前者连续7天佩戴Sensewear臂环并记饮食日记,后者没有任何行动。两组成员受试前后测量其能量平衡知识、情境兴趣及体重。跟踪记录试验组的动机成果、能量消耗和能量摄入。结果:随着时间的推移,受试者能量平衡知识显著增加,情境兴趣保持稳定(总体兴趣爱好除外),但这些变化没有给试验组带来益处。情境兴趣和动机努力与能量消耗、能量摄入和能量平衡相关。结论:通过Sensewear臂环和饮食日记能追踪能量消耗,对加强青少年的能量平衡知识有积极作用,但作用效果有限。将这两种方法作为教育手段,并结合专注、系统的教育方法有望提高青少年的能量平衡知识和积极性,并促进其形成能量平衡的生活方式。

Kinetic Modeling of Diagenesis of Eogene Lacustrine Sandstone Reservoirs in the Jianghan Basin, Southeastern China

In the Tuoshi oilfield, located in the Cenozoic Jianghan Basin of southeastern China, there have been found hydrocarbon reservoirs hosted in lacustrine sandstones of the Eogene Xingouzui Formation. The main diagenetic features identified in these sandstones include the dissolution of detrital K\|feldspar and albite grains, the precipitation of quartz as overgrowths and/or cements, and the precipitation and/or transformation of clay minerals. These diagenetic features were interpreted to have occurred in early, intermediate and late stages, based on the burial depth. The kinetics of fluid\|mineral reactions and the concentrations of aqueous species at each stage of diagenesis were simulated numerically for these lacustrine sandstones, using a quasi\|stationary state approximation that incorporates simultaneous chemical reactions in a time\|space continuum. During the early diagenetic stage, pore fluid was weakly acidic, which resulted in dissolution of K\|feldspar and albite and, therefore, led to the release of K\++, Na\++, Al\+ 3+ and SiO\- 2(aq) into the diagenetic fluid. The increased K\++, Na\++, Al\+ 3+ and SiO\- 2(aq) concentrations in the diagenetic fluid caused the precipitation of quartz, kaolinite and illite. At the beginning of the intermediate diagenetic stage the concentration of H\++ was built up due to the decomposition of organic matter, which was responsible for further dissolution of K\|feldspar and albite and precipitation of quartz, kaolinite, and illite. During the late diagenetic stage, the pore fluid was weakly alkaline, K\|feldspar became stable and was precipitated with quartz and clay minerals. When the burial depth was greater than 3000 m, the pore fluids became supersaturated with respect to albite, but undersaturated with respect to quartz, resulting in the precipitation of albite and the dissolution of quartz. The diagenetic reactions forecasted in the numerical modeling closely matched the diagenetic features identified by petrographic examination, and therefore, can help us to gain a better understanding of the diagenetic processes and associated porosity evolution in sandstone reservoirs .