Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

抑郁情绪对阿尔茨海默病患者认知功能的影响

目的:探讨抑郁情绪对阿尔茨海默病(Alzheimer's disease, AD)患者各认知领域功能的影响。方法:对2021年6月至2023年6月就诊于日照市人民医院神经内科门诊及记忆门诊的62例AD轻、中度患者进行详细的神经心理量表评估,采用简易智能状态检查量表(MMSE)、蒙特利尔认知评估量表(MoCA)评价AD轻、中度患者的整体认知功能,并分别对记忆、语言、注意力、视空间、执行功能等5个不同维度的单认知领域功能进行评估,采用汉密尔顿抑郁量表(HAMD)评价患者抑郁状况,HAMD≤7分为AD不伴发抑郁组,HAMD> 7分为AD伴发抑郁组,比较两组总体认知功能及各认知领域功能的情况。结果:62例AD患者中,有28例合并抑郁状态,占比45.16%,AD患者HAMD评分与DST-顺背呈负相关,与TMT-B和Stroop色词测验-时间呈正相关(P <0.05)。合并抑郁的AD患者与不合并抑郁的AD患者DST-顺背、TMT-B和Stroop色词测验-时间得分差异有统计学意义(P <0.05)。结论:抑郁会影响AD患者的注意力、执行功能等认知功能。

A coupled cryogenic thermo-hydro-mechanical model for frozen medium:Theory and implementation in FDEM

This paper presents the development of a coupled modeling approach to simulate cryogenic thermo-hydro-mechanical(THM)processes associated with a freezing medium,which is then implemented in the combined finite-discrete element method code(FDEM)for multi-physics simulation.The governing equations are deduced based on energy and mass conservation,and static equilibrium equations,considering water/ice phase change,where the strong couplings between multi-fields are supplemented by critical coupling parameters(e.g.unfrozen water content,permeability,and thermal conductivity).The proposed model is validated against laboratory and field experiments.Results show that the cryogenic THM model can well predict the evolution of strongly coupled processes observed in frozen media(e.g.heat transfer,water migration,and frost heave deformation),while also capturing,as emergent properties of the model,important phenomena(e.g.latent heat,cryogenic suction,ice expansion and distinct three-zone distribution)caused by water/ice phase change at laboratory and field scales,which are difficult to be all revealed by existing THM models.The novel modeling framework presents a gateway to further understanding and predicting the multi-physical coupling behavior of frozen media in cold regions.

Straw mulching alters the composition and loss of dissolved organic matter in farmland surface runoff by inhibiting the fragmentation of soil small macroaggregates

Straw mulching is a widespread practice for reducing the soil carbon loss caused by erosion.However,the effects of straw mulching on dissolved organic matter(DOM)runoff loss from black soil are not well studied.How straw mulching affects the composition and loss of runoff DOM by changing soil aggregates remains largely unclear.Here,a straw mulching treatment was compared to a no mulching treatment(as a control)on sloping farmland with black soil erosion in Northeast China.We divided the soil into large macroaggregates(>2 mm),small macroaggregates(0.25-2 mm),and microaggregates(<0.25 mm).After five rain events,the effects of straw mulching on the concentration(characterized by dissolved organic carbon(DoC)and composition(analyzed by fluorescence spectroscopy)of runoff and soil aggregate DOM were studied.The results showed that straw mulching reduced the runoff amount by 54.7%.Therefore,although straw mulching increased the average DOc concentration in runoff,it reduced the total runoff DOM loss by 48.3%.The composition of runoff DOM is similar to that of soil,as both contain humic-like acid and protein-like components.With straw mulching treatment,the protein-like components in small macroaggregates accumulated and the protein-like components in runoff declined with rain events.Fluorescence spectroscopy technology may help in understanding the hydrological paths of rain events by capturing the dynamic changes of runoff and soil DOM characteristics.A variation partitioning analysis(VPA)indicated that the DOM concentration and composition of microaggregates explained 68.2%of the change in runoff DOM from no mulching plots,while the change in runoff DOM from straw mulching plots was dominated by small macroaggregates at a rate of 55.1%.Taken together,our results demonstrated that straw mulching reduces the fragmentation of small macroaggregates and the loss of microaggregates,thus effecting DOM compositions in soil and reducing the DOM loss in runoff.These results provide a theoretical basis for reducing carbon loss in sloping farmland.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

Improving Transferable Targeted Adversarial Attack for Object Detection Using RCEN Framework and Logit Loss Optimization

Object detection finds wide application in various sectors,including autonomous driving,industry,and healthcare.Recent studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial examples.This not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing systems.Most existing adversarial attack strategies focus primarily on image classification problems,failing to fully exploit the unique characteristics of object detectionmodels,thus resulting in widespread deficiencies in their transferability.Furthermore,previous research has predominantly concentrated on the transferability issues of non-targeted attacks,whereas enhancing the transferability of targeted adversarial examples presents even greater challenges.Traditional attack techniques typically employ cross-entropy as a loss measure,iteratively adjusting adversarial examples to match target categories.However,their inherent limitations restrict their broad applicability and transferability across different models.To address the aforementioned challenges,this study proposes a novel targeted adversarial attack method aimed at enhancing the transferability of adversarial samples across object detection models.Within the framework of iterative attacks,we devise a new objective function designed to mitigate consistency issues arising from cumulative noise and to enhance the separation between target and non-target categories(logit margin).Secondly,a data augmentation framework incorporating random erasing and color transformations is introduced into targeted adversarial attacks.This enhances the diversity of gradients,preventing overfitting to white-box models.Lastly,perturbations are applied only within the specified object’s bounding box to reduce the perturbation range,enhancing attack stealthiness.Experiments were conducted on the Microsoft Common Objects in Context(MS COCO)dataset using You Only Look Once version 3(YOLOv3),You Only Look Once version 8(YOLOv8),Faster Region-based Convolutional Neural Networks(Faster R-CNN),and RetinaNet.The results demonstrate a significant advantage of the proposed method in black-box settings.Among these,the success rate of RetinaNet transfer attacks reached a maximum of 82.59%.

Insights into Euphorbia diversity: Probing the contrasts between Euphorbia fischeriana Steud and Euphorbia ebracteolata Hayata

In traditional Chinese medicine(TCM),Euphorbia fischeriana Steud(E.fischeriana)and Euphorbia ebracteolata Hayata(E.ebracteolata),commonly referred to as“Langdu”,are widely extensively utilized for treating lymphatic tuberculosis and ringworm[1].Both plant species are perennial herbaceous plants mainly distributed in northeastern China,Mongolia,Russia(Siberia),and Republic of Korea[2].There have been many reports on the chemical constituents and pharmacological effects of the two plant species,which has made more and more researchers realize that there may be differences between E.fischeriana and E.ebracteolata.In some cases,long-term improper use of herbal medicines can even lead to life-threatening conditions[3,4].Therefore,it is essential to employ an effective technology to differentiate between these two plants based on their chemical constituents and biological activities,so as to reduce the harm caused by the mixing and misuse of medicinal materials.Therefore,the present paper describes a study of the differences between E.ebracteolata and E.fischeriana,using untargeted plant metabolomics and biological activity evaluations.This study aims to provide valuable insight into their equivalence and potential interchangeability in TCM and clinical medication.

Unmanned aerial vehicles towards future Industrial Internet:Roles and opportunities

Unmanned Aerial Vehicles(UAVs)are gaining increasing attention in many fields,such as military,logistics,and hazardous site mapping.Utilizing UAVs to assist communications is one of the promising applications and research directions.The future Industrial Internet places higher demands on communication quality.The easy deployment,dynamic mobility,and low cost of UAVs make them a viable tool for wireless communication in the Industrial Internet.Therefore,UAVs are considered as an integral part of Industry 4.0.In this article,three typical use cases of UAVs-assisted communications in Industrial Internet are first summarized.Then,the state-of-the-art technologies for drone-assisted communication in support of the Industrial Internet are presented.According to the current research,it can be assumed that UAV-assisted communication can support the future Industrial Internet to a certain extent.Finally,the potential research directions and open challenges in UAV-assisted communications in the upcoming future Industrial Internet are discussed.

Progress and Challenges of Reference Standard and Its New Form: Digital Reference Standard

Number and quantities of reference standards (RS) needed for the quality control of drugs are increasing, bringing great pressure to the calibration and using company. This manuscript summarized the four generations for the development of RS including physical RS, paper atlas, substitute RS and electronic databases. The advantages and disadvantages of each generation were summarized. The concept of digital RS (DRS) was proposed based on this, and summed up the definition, advantages, and technical architecture of DRS. The 10 characteristics of five aspects of the DRS were discussed including digital, multi-dimension, big data, cloud computing, internet, internet of things, sharing, multi-terminal, intelligence, and compliance certification. Then, the necessity of its formation and application in the medicine holistic quality control of internet plus era was discussed in this manuscript.

Development of a Rehabilitation Instrument of Neurological Training for Cervical Spine

Objective: To develop a kind self active resistance movement training equipment for cervical spine, so as to restore the dynamic balance of the front and back muscle strength of the cervical spine in order to correct the abnormal physiological radian of the cervical spine, thereby relieving the clinical symptoms. Methods: The air spring with adjustable strength was used to provide the power of resistance active movement, and the neurological training cervical audio-visual synthesis training software was used to automatically guide patients to perform resistance active movement training of the cervical spine. Thirty two patients with cervical spondylosis were enrolled for treatment, and SPSS26 was used to statistically analyze the VAS, Lovett muscle strength, head and neck range of motion and C2-3 vertebral body curvature before and after the treatment. Result: There were significant differences in posterior cervical muscle strength (Lovett), dizziness, shoulder and neck pain (VAS) and C2-3 vertebral curvature before and after training in 32 patients (p˂0.001). Conclusion: The application of neurological training cervical vertebra self rehabilitation training instrument can effectively relieve clinical symptoms, restore head and neck range of motion, improve nape muscle strength, restore the dynamic balance of front and rear cervical muscle strength, and also can correct the abnormal physiological radian of the cervical vertebra.

下一代风险评估在化妆品原料安全性评价的应用研究进展

下一代风险评估(Next Generation Risk Assessment,NGRA)是在传统的风险评估基础上发展出的一套以暴露为导向、以假设为驱动的新式风险评估框架体系。随着科学技术的发展、3R原则的推广,化妆品安全性评价和风险评估方法也在不断进步。国外已有一些采用NGRA开展化妆品原料风险评估的探索性案例研究,这些案例分析展示了NGRA的评估过程,有助于提高对NGRA原则的理解。文章通过对国际化妆品监管合作组织(ICCR)提出的NGRA原则、框架体系、评估工具以及国外应用NGRA开展化妆品原料安全性评价的案例分析等内容进行系统性的梳理和介绍,提出我国化妆品原料安全性评价面临的挑战,希望为提高我国化妆品技术支撑能力提供研究思路和技术储备。

Apelin-13通过调节YAP入核抵抗缺氧诱导的视网膜Müller细胞凋亡

目的:探讨Apelin-13在缺氧诱导的视网膜Müller细胞凋亡中的作用及机制。方法:以视网膜Müller细胞为研究对象,实验分为对照组、缺氧组和实验组,对照组细胞培养于正常环境中,缺氧组细胞培养于缺氧环境中,实验组细胞培养于缺氧环境中并以Apelin-13(1μmol/L)处理,MTT法检测细胞活力变化,结晶紫染色法观察细胞形态,免疫荧光染色法观察细胞GFAP和YAP的表达情况,TUNEL染色检测细胞凋亡并计算凋亡指数,蛋白印记实验检测p-LATS1、p-YAP、LATS1及YAP蛋白表达变化。结果:提取分离的Müller细胞传代后贴壁生长,细胞呈长梭形、多角形、圆形等形态,细胞质丰富,细胞核呈圆形,细胞GFAP表达阳性。0.1、1、10μmol/L Apelin-13处理显著抑制缺氧诱导的Müller细胞活力下降(P<0.05或P<0.01)。与对照组相比,缺氧组细胞凋亡指数明显升高(P<0.01);而与缺氧组相比,实验组凋亡指数明显降低(P<0.01)。对照组和缺氧组细胞p-LATS1和p-YAP蛋白表达无明显差异;与缺氧组相比,实验组细胞p-LATS1和p-YAP蛋白表达明显降低(P<0.01)。对照组和缺氧组细胞核YAP蛋白表达无明显差异;与缺氧组相比,实验组YAP细胞核表达明显升高(P<0.01)。结论:Apelin-13能抵抗缺氧诱导的视网膜Müller细胞凋亡,该作用可能与调节YAP入核有关。

Functional Brain Network Learning Based on Spatial Similarity for Brain Disorders Identification

Functional brain network (FBN) measures based on functional magnetic resonance imaging (fMRI) data, has become important biomarkers for early diagnosis and prediction of clinical outcomes in neurological diseases, such as Alzheimer’s diseases (AD) and its prodromal state (i.e., Mild cognitive impairment, MCI). In the past decades, researchers have developed numbers of approaches for FBN estimation, including Pearson’s correction (PC), sparse representation (SR), and so on. Despite their popularity and wide applications in current studies, most of the approaches for FBN estimation only consider the dependency between the measured blood oxygen level dependent (BOLD) time series, but ignore the spatial relationships between pairs of brain regions. In practice, the strength of functional connection between brain regions will decrease as their distance increases. Inspired by this, we proposed a new approach for FBN estimation based on the assumption that the closer brain regions tend to share stronger relationships or similarities. To verify the effectiveness of the proposed method, we conduct experiments on a public dataset to identify the patients with MCIs from health controls (HCs) using the estimated FBNs. Experimental results demonstrate that the proposed approach yields statistically significant improvement in seven performance metrics over using the baseline methods.

基于平台数据的成人高等教育信息化改革的路径探索

互联网时代,在网络信息技术的加持下,线上学习平台迎来迅猛发展普及的契机,为成人学习者提供了巨大的资源、方便灵活的学习方式以及良好的交互性能,同时也衍生出海量化、多样化、层级复杂的学习数据信息。厘清和明确数据价值意蕴及关联关系,是成人高等教育发展进程面临的挑战和发展方向。充分挖掘、整合和分析平台数据资源,洞察数据蕴藏内涵,掌握数据演变规律,明晰映射交互关系,借力平台数据从适性教学、高效学习、科学管理、实效评价四个维度进行认知思考,助力优化教育教学,从而全面提升人才培养质量和水平。正确把握平台数据在数字化教育改革系统工程中的地位和作用,将是成人高等教育进一步拓展信息化发展空间和高质量内涵建设的突破口,是推动信息化向智慧化转型的新理念新思路新方法。

日本防晒产品的管理制度及对我国的启示

我国的化妆品类产品在日本按照化妆品和医药部外品进行分类管理。其中防晒产品是非常具有代表性的一类,既包括化妆品又包括医药部外品。通过对日本化妆品和医药部外品的法律法规及文献进行分析,研究和总结了产品分类、测试方法、原料管理、产品备案审批制度和标签管理等技术要求及监管要求;结合我国化妆品的监管实际情况,对两国的法规要求和管理现状进行了对比分析,为我国化妆品监管提供了参考,在我国化妆品行业蓬勃发展、监管法规逐步完善的现状下,进一步促进行业的高质量发展。

脂肪因子Vaspin在非酒精性脂肪性肝病中的表达及其临床意义

目的初步探索Vaspin在非酒精性脂肪性肝病(NAFLD)肝脏组织中的表达及其临床意义。方法回顾分析2016年10月—2019年10月首都医科大学附属北京地坛医院收治的78例NAFLD超声引导下肝脏组织细针穿刺患者临床病理资料。采用HE染色和网织Gomori银染色观察肝脏组织的炎症程度和纤维化程度;免疫组织化学染色法检测Vaspin和转化生长因子-β(TGF-β)的表达;分析肝脏组织Vaspin、TGF-β的表达与肝脏组织炎症程度、纤维化程度、血清总胆固醇(TCHO)、甘油三酯(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、谷丙转氨酶(ALT)、谷草转氨酶(AST)、γ-谷氨酰基转移酶(GGT)、碱性磷酸酶(ALP)、总胆红素(TBIL)、直接胆红素(DBIL)、空腹血浆葡萄糖(FPG)、糖化血红蛋白(HbA1c)的相关性;采用Logistic回归模型分析NAFLD的影响因素。结果Vaspin表达分级与TG呈负相关(r_(s)=-0.279,P<0.05),与LDL-C、FPG呈正相关(r_(s)=0.352和0.312,均P<0.05)。TGF-β表达分级与ALP呈负相关(r_(s)=-0.225,P<0.05)。Logistic回归分析显示,Vaspin表达分级[OR=2.987(95%CI:1.065,8.379)]、ALT[OR=1.062(95%CI:1.014,1.112)]、TBIL[OR=1.502(95%CI:1.147,1.967)]、FPG[OR=13.111(95%CI:1.430,120.195)]是发生NAFLD的独立危险因素。结论肝脏组织Vaspin高表达提示NAFLD患者病情较重。

A holistic strategy for quality and safety control of traditional Chinese medicines by the “iVarious” standard system

An effective quality control system is the key to ensuring the quality, safety and efficacy of traditional Chinese medicines(TCMs). However, the current quality standard research lacks top-level design and systematic design,mostly based on specific technologies or evaluation methods. To resolve the challenges and questions of quality control of TCMs, a brand-new quality standard system, named "iVarious", was proposed. The system comprises eight elements in a modular format. Meaning of every element was specifically illustrated via corresponding research instances. Furthermore, frankincense study was taken as an example for demonstrating standards and research process, based on the "i Various" system. This system highlighted a holistic strategy for effectiveness,security, integrity and systematization of quality and safety control standards of TCMs. The establishment of"i Various" integrates multi-disciplinary technologies and progressive methods, basis elements and key points of standard construction. The system provides a novel idea and technological demonstration for regulation establishment of TCMs quality standards.

Effect and mechanism of octanol in cassiterite flotation using benzohydroxamic acid as collector

The effect of octanol in cassiterite flotation using benzohydroxamic acid (BHA) as a collector was investigated. Theadsorption mechanism of octanol and BHA on the surface of cassiterite was analyzed by adsorption experiments and infrared spectraanalysis. Micro-flotation results indicated that single octanol exhibited almost no collecting power to cassiterite over a wide pHrange. However, as an auxiliary collector, octanol could markedly decrease the consumption of collector BHA and keep the recoveryof cassiterite in high level. The results of adsorption experiments and infrared spectra demonstrated that single octanol was notadsorbed on the surface of cassiterite. It formed adsorption connected with BHA on the surface of cassiterite, and enhanced thehydrophobicity of cassiterite. Octanol promoted the adsorption amount of BHA on the cassiterite surface, and decreased theconsumption of BHA.

Neural stem cell transplantation in a double-layer collagen membrane with unequal pore sizes for spinal cord injury repair

A novel double-layer collagen membrane with unequal pore sizes in each layer was designed and tested in this study. The inner, loose layer has about 100-μm-diameter pores, while the outer, compact layer has about 10-μm-diameter pores. In a rat model of incomplete spinal cord injury, a large number of neural stem cells were seeded into the loose layer, which was then adhered to the injured side, and the compact layer was placed against the lateral side. The results showed that the transplantation of neural stem cells in a double-layer collagen membrane with unequal pore sizes promoted the differentiation of neural stem cells, attenuated the pathological lesion, and signiifcantly improved the motor function of the rats with incomplete spinal cord injuries. These experimental ifndings suggest that the transplantation of neural stem cells in a double-lay-er collagen membrane with unequal pore sizes is an effective therapeutic strategy to repair an injured spinal cord.