Evaluation of Technical Education by Using a Modern Structured MOODLE Laboratory Course, in Relation to Recent Data

E-learning platforms support education systems worldwide, transferring theoretical knowledge as well as soft skills. In the present study high-school pupils’, and adult students’ opinions were evaluated through a modern structured MOODLE interactive course, designed for the needs of the laboratory course “Automotive Systems”. The study concerns Greek secondary vocational education pupils aged 18 and vocational training adult students aged 20 to 50 years. The multistage, equal size simple random cluster sample was used as a sampling method. Pupils and adult students of each cluster completed structured 10-question questionnaires both before and after attending the course. A total of 120 questionnaires were collected. In general, our findings disclosed that the majority of pupils and adult students had significantly improved their knowledge and skills from using MOODLE. They reported strengthening conventional teaching, using the new MOODLE technology. The satisfaction indices improved quite, with the differences in their mean values being statistically significant.

航天器v_(∞)转移轨道的模型和解析法

假定A和B是围绕一个引力中心按照开普勒轨道运行的卫星,针对航天器从A出发转移至B的轨道确定问题,文章提出了一个新的模型,称为v∞转移轨道(v∞-transfer-orbit,VTO).VTO选取航天器飞离A的时刻t0和逃逸速度大小v∞作为设计参数,求解抵达B的开普勒轨道.根据A和B运行轨道的空间位置关系,VTO分为A/B异面、A/B共面和A/B重合3种情况,同时存在3类解:General-VTO、Backflip-VTO和Resonant-VTO.文章建立了统一的VTO解析方法,即将航天器抵达B的位置约束分解为轨道约束和时间约束,根据轨道约束推导航天器轨道关于单个变量的解析式,根据时间约束建立该变量的一元方程,从而将原问题转化为一元方程寻根问题.首先,依次针对A/B异面、A/B共面和A/B重合情况构建了General-VTO的一元寻根方程,详细介绍了一元方程的寻根区间,并给出了一种基于三次样条插值的快速寻根算法;然后,构建了Backflip-VTO的一元寻根方程,在分析一元方程函数单调性、极值点和拐点的基础上给出了一元方程寻根区间和寻根方法;之后,构建了Resonant-VTO的直接解析式.最后,给出算例并重点说明VTO多解性.

Cohort study on the treatment of BRAF V600E mutant metastatic colorectal cancer with integrated Chinese and western medicine

BACKGROUND Patients with BRAF V600E mutant metastatic colorectal cancer(mCRC)have a low incidence rate,poor biological activity,suboptimal response to conventional treatments,and a poor prognosis.In the previous cohort study on mCRC conducted by our team,it was observed that integrated Chinese and Western medicine treatment could significantly prolong the overall survival(OS)of patients with colorectal cancer.Therefore,we further explored the survival benefits in the population with BRAF V600E mutant mCRC.AIM To evaluate the efficacy of integrated Chinese and Western medicine in the treatment of BRAF V600E mutant metastatic colorectal cancer.METHODS A cohort study was conducted on patients with BRAF V600E mutant metastatic colorectal cancer admitted to Xiyuan Hospital of China Academy of Chinese Medical Sciences and Traditional Chinese Medicine Hospital of Xinjiang Uygur Autonomous Region from January 2016 to December 2022.The patients were divided into two cohorts.RESULTS A total of 34 cases were included,with 23 in Chinese-Western medicine cohort(cohort A)and 11 in Western medicine cohort(cohort B).The median overall survival was 19.9 months in cohort A and 14.2 months in cohort B,with a statistically significant difference(P=0.038,hazard ratio=0.46).The 1-3-year survival rates were 95.65%(22/23),39.13%(9/23),and 26.09%(6/23)in cohort A,and 63.64%(7/11),18.18%(2/11),and 9.09%(1/11)in cohort B,respectively.Subgroup analysis showed statistically significant differences in median OS between the two cohorts in the right colon,liver metastasis,chemotherapy,and first-line treatment subgroups(P<0.05).CONCLUSION Integrated Chinese and Western medicine can prolong the survival and reduce the risk of death in patients with BRAF V600E mutant metastatic colorectal cancer,with more pronounced benefits observed in patients with right colon involvement,liver metastasis,combined chemotherapy,and first-line treatment.

基于改进YOLO v7轻量化模型的自然果园环境下苹果识别方法

针对自然果园环境下苹果果实识别中,传统的目标检测算法往往很难在检测模型的检测精度、速度和轻量化方面实现平衡,提出了一种基于改进YOLO v7的轻量化苹果检测模型。首先,引入部分卷积(Partial convolution, PConv)替换多分支堆叠模块中的部分常规卷积进行轻量化改进,以降低模型的参数量和计算量;其次,添加轻量化的高效通道注意力(Efficient channel attention, ECA)模块以提高网络的特征提取能力,改善复杂环境下遮挡目标的错检漏检问题;在模型训练过程中采用基于麻雀搜索算法(Sparrow search algorithm, SSA)的学习率优化策略来进一步提高模型的检测精度。试验结果显示:相比于YOLO v7原始模型,改进后模型的精确率、召回率和平均精度分别提高4.15、0.38、1.39个百分点,其参数量和计算量分别降低22.93%和27.41%,在GPU和CPU上检测单幅图像的平均用时分别减少0.003 s和0.014 s。结果表明,改进后的模型可以实时准确地识别复杂果园环境中的苹果,模型参数量和计算量较小,适合部署于苹果采摘机器人的嵌入式设备上,为实现苹果的无人化智能采摘奠定了基础。

基于改进DeepLabv3+的轻量化作物杂草识别方法

为在存储资源与计算能力有限的设备上实现田间作物和杂草的识别,本文提出一种基于改进DeepLabv3+的轻量化语义分割网络。首先,以MobileNet v2作为DeepLabv3+的特征提取骨干网络,提出双分支残差模块替换倒残差模块,并删除后两层卷积以降低模型参数量。其次,在空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块中引入分组逐点卷积,使用深度扩张卷积替换标准卷积,并将卷积后的特征图进行多尺度特征融合增强对作物和杂草深层特征的提取能力。最后,将原有的非线性激活函数替换为Leaky ReLU激活函数来提升分割精度。实验结果表明:改进后网络的mIOU达到86.75%,参数量仅为0.69M,FPS达到了98,与原始DeepLabv3+以及3个典型轻量化语义分割网络的相比,参数量最小,在对比的轻量化网络中具有最高的分割精度。

基于ⅢF A/V规范和Avalon系统的大学图书馆视听数据库建设研究

随着中国网络基础设施的不断改善,视听媒体在年轻一代中非常流行,给以文本资源为主的图书馆带来了挑战。本研究旨在探究国内外大学图书馆视听资源数据库建设的现状,借鉴ⅢF规范在图像资源管理方面的成功经验和各种视听保存社区的实践,提出基于ⅢF A/V规范与开源软件的中国大学图书馆视听资源管理方法。通过分析华东师范大学图书馆在视听资源保存、流媒体发布、时间轴气泡注释、转录、视听结构化和开放共享方面的实践,进行实证研究。

基于改进轻量化YOLO v5n的番茄叶片病害识别方法

针对现有番茄叶片病害识别存在背景复杂、识别准确率低、模型参数量大、计算量大以及难以部署至移动设备或嵌入式设备等问题,提出一种改进的轻量化YOLO v5n的番茄叶片病害识别方法。首先收集细菌性斑疹病、早疫病、晚疫病、叶霉病、斑枯病、褐斑病等6种常见番茄叶片病害图像以及番茄健康叶片图像,对图像进行镜像翻转、高斯模糊等数据增强方式增加样本多样性,提升模型识别和泛化能力。接着在YOLO v5n网络基础上,选择采用轻量化的C3Ghost模块替换C3模块以压缩卷积过程中的计算量、模型权重和大小,同时在颈部网络中融合轻量级卷积技术GSConv和VOV-GSCSP模块,在增强特征提取能力的同时降低模型参数量。最后引入PAGCP算法对改进后的模型进行全局通道剪枝压缩参数量并减少训练开销。试验结果表明,改进后的YOLO v5n平均精度均值达到99.0%,参数量减少66.67%,计算量降低了2.6 G,模型权重压缩了2.23 MB。本研究提出的番茄叶片病害识别方法在降低了模型大小、参数量、计算量的同时仍保持较高的识别精度,为移动设备上实现番茄叶片病害识别提供技术参考。

罗汉果苷V调控高糖状态巨噬细胞M1极化促进骨髓间充质干细胞的成骨分化

背景:糖尿病微环境会造成巨噬细胞过度M1极化,这种高糖炎症状态会抑制骨髓间充质干细胞的成骨分化,从而影响糖尿病骨缺损的愈合。研究表明罗汉果苷Ⅴ具有抗炎、抗氧化、降血糖的作用,但其能否调节高糖炎症状态下巨噬细胞M1极化及骨髓间充质干细胞的成骨分化尚不清楚。目的:探讨罗汉果苷Ⅴ在高糖炎症状态下调节巨噬细胞M1型极化对骨髓间充质干细胞成骨分化的影响。方法:构建糖尿病C57BL/6小鼠模型,从正常和糖尿病小鼠分离骨髓来源巨噬细胞,分别培养于低糖和高糖培养基。使用脂多糖和干扰素γ作为炎症刺激诱导骨髓来源巨噬细胞的M1型极化,同时以160,320,640μmol/L罗汉果苷Ⅴ干预,用流式细胞术检测F4/80^(+)CD86^(+)细胞比例,qRT-PCR检测诱导型一氧化氮合酶、白细胞介素1β、白细胞介素6的mRNA表达水平,ELISA检测骨髓来源巨噬细胞上清液中肿瘤坏死因子α水平。分离C57BL/6小鼠骨髓间充质干细胞,分别使用低糖或高糖成骨诱导液诱导成骨分化,添加M1型巨噬细胞条件培养基作为炎症刺激,以及320μmol/L罗汉果苷Ⅴ干预,成骨诱导14 d后采用qRT-PCR检测碱性磷酸酶、Runt相关因子2、骨钙素、骨桥蛋白的mRNA表达水平,成骨诱导21 d后进行茜素红染色及定量分析。结果与结论:①流式细胞术结果显示320,640μmol/L罗汉果苷Ⅴ组的F4/80^(+)CD86^(+)细胞比例明显低于高糖炎症对照组(P<0.05);②qRT-PCR结果显示160,320,640μmol/L罗汉果苷Ⅴ组的诱导型一氧化氮合酶、白细胞介素6的mRNA相对表达量较高糖炎症对照组显著降低(P<0.05),320,640μmol/L罗汉果苷Ⅴ组白细胞介素1β的mRNA相对表达量较高糖炎症对照组显著降低(P<0.05);③ELISA结果显示160,320,640μmol/L罗汉果苷Ⅴ组的肿瘤坏死因子α分泌水平较高糖炎症对照组显著降低(P<0.05);④320μmol/L罗汉果苷Ⅴ干预后,高糖炎症状态下骨髓间充质干细胞的钙盐沉积增加(P<0.05),且碱性磷酸酶、Runt相关因子2和骨桥蛋白的mRNA相对表达量增加(P<0.05)。结果表明,罗汉果苷Ⅴ可通过抑制高糖炎症状态下骨髓来源巨噬细胞的M1型极化及炎症因子表达,促进骨髓间充质干细胞的成骨分化。

基于YOLO v7的海量烟支外观缺陷快速自动标注方法

图像标注作为监督式机器学习的关键环节,在处理海量的烟支缺陷数据时,传统的人工标注方法由于耗时长和主观性强等缺点显得不够高效。针对烟支缺陷检测领域中大量图像数据的自动化标注挑战,该文以YOLO v7作为基线网络,并进行一系列经验性的改进,以解决传统人工标注过程中存在的高成本和低效率问题。通过对YOLO v7的结构进行创新性调整,如合并neck层和head层,并引入Rep VGG结构,实现烟支图像的高效自动标注。实验结果表明,改进后的YOLO v7和YOLO v7-tiny在真实烟支数据集上的标注错误率分别为7.3%和6.56%,其中YOLO v7-tiny展现最快的标注速度。这项研究不仅在提高标注效率和准确性方面取得显著进步,还为烟支缺陷检测领域提供一种经济高效的自动化处理方案。

基于改进SVIT算法的非侵入式居民负荷监测方法

针对居民侧异常用电负荷对用户用电安全有重大影响的问题,提出了一种基于改进SVIT的非侵入式负荷监测方法。该方法从用户总线处对居民家庭用电负荷投切时的V-I数据进行特征提取;对提取出的V-I轨迹特征进行二进制化处理并绘制成V-I轨迹图像;利用改进的与三重注意力机制(Triplet Attention)相结合的SVIT的特征提取网络对其进行特征提取与映射;在此基础上,将处理后生成的新的特征向量进行聚类形成特征检索数据库,以识别出不在该特征检索数据库中的用电器V-I轨迹样本。通过利用PLAID数据集进行仿真实验并分析,验证了模型的有效性以及算法的优越性。

Testing and Analysis of VoIPv6 (Voice over Internet Protocol V6) Performance Using FreeBSD

This study focuses on testing and quality measurement and analysis of VoIPv6 performance. A client, server codes were developed using FreeBSD. This is a step before analyzing the Architectures of VoIPv6 in the current internet in order for it to cope with IPv6 traffic transmission requirements in general and specifically voice traffic, which is being attracting the efforts of research, bodes currently. These tests were conducted in the application level without looking into the network level of the network. VoIPv6 performance tests were conducted in the current tunneled and native IPv6 aiming for better end-to-end VoIPv6 performance. The results obtained in this study were shown in deferent codec's for different bit rates in Kilo bits per second, which act as an indicator for the better performance of G.711 compared with the rest of the tested codes.

Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead.However,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and fatalities.In this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation.This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety.See-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of sight.To address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both cars.The server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front car.Our see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other vehicles.Our network was trained and tested on the Cityscape dataset using semantic segmentation.This transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has obscured.For our findings,we have achieved 97.1% F1-score.The article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

Characteristics and mechanisms of strain waves generated in rock by cylindrical explosive charges

A superposing principle, by suitably adding the strain waves from a number of concentrated explosive charges to approximate the waves generated by a cylindrical charge based on the strain wave of a point or small spherical explosive charge generated in rock, is used to further study the triggering time of strain gauges installed in radial direction at same distances but different positions surrounding a cylindrical explosive charge in rock. The duration of the first compression phase and peak value of strain wave, and furthermore, their differences are analyzed and some explanations are given. Besides that, the gauge orientation in which the maximum peak value occurs is also discussed. At last, the effect of velocity of detonation(V.O.D.) of a cylindrical explosive charge on the strain waves generated in the surrounding rock is taken as key research and the pattern of peak amplitude of a strain wave varies with the V.O.D. is likely to have been found.

Performance and Efficiency Simulation Study of a Smart-Grid Connected Photovoltaic System

The availability of non-renewable energy sources such as crude oil, natural gas, coal etc., is fast diminishing. So the renewable energy sources such as solar, hydropower, geothermal, wind, tidal energy, are gaining more and more importance. Many new developments to convert these renewable energy sources into usable forms are taking place. Most renewable energy sources are used to produce electricity. In this paper, a performance and efficiency simulation study of a smart-grid connected photovoltaic system using Chroma DC programmable power supply, AC programmable source and an Aurora Inverter is proposed. The simulation is performed in MATLAB environment where the Current-Voltage (I-V) and Power-Voltage (P-V) curves from the solar array simulator are generated and plotted. The proposed topology has been verified with satisfactory results. In addition, temperature and irradiance effects on I-V and P-V characteristic curves are verified. Also, the efficiency curves of the photovoltaic grid interface inverter are generated in the study. The MATLAB code developed in this paper is a valuable tool for design engineers comparing different inverters, calculating the optimum efficiency of a given inverter type.

Vaccines’ Safety and Effectiveness in the Midst of Covid-19 Mutations

We examined the coronavirus classification and evolution through its multiple mutations that have increased its transmissibility rate up to 70% globally, threatening to undermine the promise of a number of emerging vaccines that primarily focus on the immune detection of the Spike trimer. The safety and effectiveness of different vaccination methods are evaluated and compared, including the mRNA version, the Adenovirus DNA, Spike protein subunits, the deactivated virus genres, and the live attenuated coronavirus. Mutations have been long considered as random events, or mistakes during the viral RNA replication. Usually, what can go wrong will go wrong;therefore, repeated transformations lead to the extinction of a virus. On the contrary, the aggregate result of over 300,000 Covid-19 variants has expanded its transmissibility and infectiousness. Covid-19 mutations do not degrade the virus;they empower and facilitate its disguise to evade detection. Unlike other coronaviruses, Covid-19 amino acid switches do not reflect the random unfolding of errors that eventually eradicate the virus. Covid-19 appears to use mutations adaptively in the service of its survival and expansion. We cite evidence that Covid-19 inhibits the interferon type I production, compromising adaptive immunity from recognizing the virus. The deleterious consequences of the cytokine storm where the CD8+ killer cells injure the vital organs of the host may well be a Covid-19 manoeuvring to escape exposure. It is probable that evolution has programmed Covid-19 with an adeptness designed to debilitate key systemic defences to secure its subsistence. To date the infectiousness of the Covid-19 pandemic is exponentially increasing, denoting the possibility of an even more dangerously elusive, inconspicuous, and sophisticated version of the disease.

Avian hepatitis B viruses： Molecular and cellular biology, phylogenesis, and host tropism

The human hepatitis B virus （HBV） and the duck hepatitis B virus （DHBV） share several fundamental features. Both viruses have a partially double-stranded DNA genome that is replicated via a RNA intermediate and the coding open reading frames （ORFs） overlap extensively. In addition, the genomic and structural organization, as well as replication and biological characteristics, are very similar in both viruses. Host of the key features of hepadnaviral infection were first discovered in the DHBV model system and subsequently confirmed for HBV. There are, however, several differences between human HBV and DHBV. This review will focus on the molecular and cellular biology, evolution, and host adaptation of the avian hepatitis B viruses with particular emphasis on DHBV as a model system.

基于改进YOLO v5的皮蛋裂纹在线检测方法

为了解决裂纹皮蛋分选中存在的效率低、人力成本高等问题,提出了一种基于改进YOLO v5的皮蛋裂纹在线检测方法。使用EfficientViT网络替换主干特征提取网络,并采用迁移学习对网络进行训练,分别得到YOLO v5n_EfficientViTb0和YOLO v5s_EfficientViTb1两个模型。YOLO v5n_EfficientViTb0为轻量化模型,相较于改进前参数量减少14.8%,浮点数计算量减少26.8%;YOLO v5s_EfficientViTb1为高精度检测模型,平均精度均值为87.8%。采用GradCAM++对模型可视化分析,得出改进模型减少了对背景区域的关注度,证明了改进模型的有效性。设计了视频帧的目标框匹配算法,实现了视频中皮蛋的目标追踪,依据皮蛋的检测序列实现了对皮蛋的定位和裂纹与否的判别。轻量化模型的判别准确率为92.0%,高精度模型的判别准确率为94.3%。研究结果表明,改进得到的轻量化模型为运算能力较差的皮蛋裂纹在线检测装备提供了解决方案,改进得到的高精度模型为生产要求更高的皮蛋裂纹在线检测装备提供了技术支持。

电动汽车800 V平台电驱动总成噪声分析研究

目前,主流的新能源汽车高压电气系统的电压范围一般为230~450 V,取中间值称之为400 V平台。伴随着快充及超充的应用,汽车高压电气系统电压范围达到550~930 V,取中间值称之为800 V平台。以某款前驱纯电动车型搭载800 V电驱动总成分别匹配400 V、800 V电池系统的噪声分析优化为背景,本文通过对主客观数据进行理论分析,并对电驱动总成本体以及整车传递路径进行优化,以改善车内噪声。这对后续整车搭载800 V平台电驱动总成的噪声、振动与声振粗糙度(NVH)性能改善具有重要的指导意义和工程价值。

基于改进COF-YOLO v8n的油茶果静态与动态检测计数方法

针对自然环境下油茶果存在严重遮挡、近景色、小目标等现象,使用YOLO网络存在检测精度低、漏检现象严重等问题,提出对YOLO v8n网络进行改进。首先使用MPDIOU作为YOLO v8n的损失函数,有效解决因为果实重叠导致的漏检问题;其次调整网络,向其中加入小目标检测层,使网络能够关注小目标油茶以及被树叶遮挡的油茶;最后使用SCConv作为特征提取网络,既能兼顾检测精度又能兼顾检测速度。改进COF-YOLO v8n网络精确率、召回率、平均精度均值分别达到97.7%、97%、99%,比未改进的YOLO v8n分别提高3.2、4.8、2.4个百分点,其中严重遮挡情况下油茶检测精确率、召回率、平均精度均值分别达到95.9%、95%、98.5%,分别比YOLO v8n提高4.0、9.1、4.6个百分点。因此改进后COF-YOLO v8n网络能够明显提高油茶在严重遮挡、近景色、小目标均存在情况下的识别精度,减小油茶的漏检。此外,模型能够实现动、静态输入条件下油茶果计数。动态计数借鉴DeepSORT算法的多目标跟踪思想,将改进后COF-YOLO v8n的识别输出作为DeepSORT的输入,实现油茶果实的追踪计数。所得改进模型具有很好的鲁棒性,且模型简单可以嵌入到边缘设备中,不仅可用于指导自动化采收,还可用于果园产量估计,为果园物流分配提供可靠借鉴。

基于CRV-YOLO的苹果中心花和边花识别方法

苹果树疏花是果园生产管理中的重要环节。准确高效地识别苹果中心花和边花,是研发智能疏花机器人的前提。针对苹果疏花作业中的实际需求,提出了一种基于CRV-YOLO的苹果中心花和边花识别方法。本文基于YOLO v5s模型进行了如下改进:将C-CoTCSP结构融入Backbone,更好地学习上下文信息并提高了模型特征提取能力,提高了模型对外形相似和位置关系不明显的中心花和边花的检测性能。在Backbone中添加改进RFB结构,扩大特征提取感受野并对分支贡献度进行加权,更好地利用了不同尺度特征。采用VariFocal Loss损失函数,提高了模型对遮挡等场景下难识别样本检测能力。在3个品种1837幅图像数据集上进行了实验,结果表明,CRV-YOLO的精确率、召回率和平均精度均值分别为95.6%、92.9%和96.9%,与原模型相比,分别提高3.7、4.3、3.9个百分点,模型受光照变化和苹果品种影响较小。与Faster R-CNN、SSD、YOLOX、YOLO v7模型相比,CRV-YOLO的精确率、平均精度均值、模型内存占用量和复杂度性能最优,召回率接近最优。研究成果可为苹果智能疏花提供技术支持。