Design and high-power testing of offline conditioning cavity for CiADS RFQ high-power coupler

To validate the design rationality of the power coupler for the RFQ cavity and minimize cavity contamination,we designed a low-loss offline conditioning cavity and conducted high-power testing.This offline cavity features two coupling ports and two tuners,operating at a frequency of 162.5 MHz with a tuning range of 3.2 MHz.Adjusting the installation angle of the coupling ring and the insertion depth of the tuner helps minimize cavity losses.We performed electromagnetic structural and multiphysics simulations,revealing a minimal theoretical power loss of 4.3%.However,when the cavity frequency varied by110 kHz,theoretical power losses increased to10%,necessitating constant tuner adjustments during conditioning.Multiphysics simulations indicated that increased cavity temperature did not affect frequency variation.Upon completion of the offline high-power conditioning platform,we measured the transmission performance,revealing a power loss of 6.3%,exceeding the theoretical calculation.Conditioning utilized efficient automatic range scanning and standing wave resonant methods.To fully condition the power coupler,a 15°phase difference between two standing wave points in the condition-ing system was necessary.Notably,the maximum continuous wave power surpassed 20 kW,exceeding the expected target.

An Investigation on the Application of Online and Offline Hybrid Teaching in Local Anatomy Course of Acupuncture and Moxibustion Specialty

The purpose of this paper is to explore the application of online and offline blended teaching in local anatomy courses of acupuncture specialty.It introduces the concept and characteristics of blended teaching mode and analyzes the respective advantages of online and offline teaching as well as the advantages and application prospects of blended teaching mode.The teaching status quo of local anatomy courses in acupuncture and moxibustion is analyzed,pointing out the problems of the traditional teaching mode and the application prospect of the blended teaching mode.In the practical part,the preparation and design of online teaching resources,the design of offline practical teaching sessions,and the evaluation methods of teaching effect are introduced in detail.This study aims to provide new teaching modes and ideas for teaching acupuncture and moxibustion and promote the improvement of teaching quality.

Research on the Blended Online and Offline Teaching Mode of Ideological and Political Courses in Universities

With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.

10层陆面过程模式及其Offline独立试验

利用西伯利亚地区的一个试验点资料和1998年中国淮河流域试验(HUBEX)的加密观测资料,对一个新发展的陆面过程模式进行了模拟检验。西伯利亚地区的单点试验表明,不同时间间隔的边界强迫对地表吸收的净短波辐射和释放的潜热影响较大。淮河流域的模拟结果表明,模式能够较好地模拟出我国夏季半湿润地区陆面特征量的变化趋势。由于模式模拟的地温偏低、净短波辐射偏小,所以模拟的感热和潜热值偏小。对该模式在淮河流域的植被、土壤等参数的合理选取可能会提高模式的模拟效果。

在线课程背景下高职英语online+offline教学模式的应用研究

通过对当前英语教学中存在问题的统计分析,依托在线开放课程平台,利用英语课前、课上和课后教学实践活动验证英语online+offline教学新模式下的教学设计、教学组织形式、教学方法手段、考核评价方式等对英语教学效果的提升作用。

基于Online to Offline的高职航海技术专业现代学徒制教学生态构建

以高职航海技术专业为例,分析现代学徒制培养模式下的瓶颈问题,探究引入O2O(Online to Offline)教学方式,在线上线下形成学生、学校和企业共同参与的工学结合、产教融合的教学生态,提高航海技术专业学徒制学生的自主学习能力、专业技能和综合素质,培养满足社会和航运企业需求的航海人才。

基于在线开放课程平台的高职英语“online+offline”学习方式研究

随着教育信息化理念指导各学科教学的不断深入,有关信息化课堂教学中学生学习方式的研究成为不可忽视的关注点。基于在线开放课程信息化平台对英语教学及学习的支持,以建构主义学习理论为指导,探讨现代信息环境下英语主要教学环节与学习方式的关系,阐释通过自主学习、合作学习等方式在英语"online+offline"课堂教学中有效开展学习活动,提升学生学习效率,为英语课堂教学改革提供参考。

A Model for Regional Energy Utilization by Offline Heat Transport System and Distributed Energy Systems—Case Study in a Smart Community, Japan

Under the Kyoto Protocol,Japanwas supposed to reduce six percent of the green house gas (GHG) emission in 2012. However, until the year 2010, the statistics suggested that the GHG emission increased 4.2%. What is more challenge is, afterFukushimacrisis, without the nuclear energy,Japanmay produce about 15 percent more GHG emissions than1990 inthis fiscal year. It still has to struggle to meet the target set by Kyoto Protocol. The demonstration area of “smart community” suggests Japanese exploration for new low carbon strategies. The study proposed a demand side response energy system, a dynamic tree-like hierarchical model for smart community. The model not only conveyed the concept of smart grid, but also built up a smart heat energy supply chain by offline heat transport system. Further, this model promoted a collaborative energy utilization mode between the industrial sector and the civil sector. In addition, the research chose the smart community inKitakyushuas case study and executed the model. The simulation and the analysis of the model not only evaluate the environmental effect of different technologies but also suggest that the smart community inJapanhas the potential but not easy to achieve the target, cut down 50% of the CO2 emission.

Offline Urdu Nastaleeq Optical Character Recognition Based on Stacked Denoising Autoencoder

Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.

Offline two-dimensional liquid chromatography coupled with ion mobility-quadrupole time-of-flight mass spectrometry enabling fourdimensional separation and characterization of the multicomponents from white ginseng and red ginseng

Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification.A dimension-enhanced strategy,by offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry(2 D-LC/IM-QTOF-MS)enabling four-dimensional separations(2 D-LC,IM,and MS),is proposed.In combination with in-house database-driven automated peak annotation,this strategy was utilized to characterize ginsenosides simultaneously from white ginseng(WG)and red ginseng(RG).An offline 2 DLC system configuring an Xbridge Amide column and an HSS T3 column showed orthogonality 0.76 in the resolution of ginsenosides.Ginsenoside analysis was performed by data-independent high-definition MSE(HDMSE)in the negative ESI mode on a Vion?IMS-QTOF hybrid high-resolution mass spectrometer,which could better resolve ginsenosides than MSEand directly give the CCS information.An in-house ginsenoside database recording 504 known ginsenosides and 58 reference compounds,was established to assist the identification of ginsenosides.Streamlined workflows,by applying UNIFI?to automatedly annotate the HDMSEdata,were proposed.We could separate and characterize 323 ginsenosides(including 286 from WG and 306 from RG),and 125 thereof may have not been isolated from the Panax genus.The established 2 D-LC/IM-QTOF-HDMSEapproach could also act as a magnifier to probe differentiated components between WG and RG.Compared with conventional approaches,this dimensionenhanced strategy could better resolve coeluting herbal components and more efficiently,more reliably identify the multicomponents,which,we believe,offers more possibilities for the systematic exposure and confirmative identification of plant metabolites.

Improved Generalized Predictive Control Algorithm with Offline and Online Identification and Its Application to Fixed Bed Reactor

An improved generalized predictive control algorithm is presented in thispaper by incorporating offline identification into online identification. Unlike the existinggeneralized predictive control algorithms, the proposed approach divides parameters of a predictivemodel into the time invariant and time-varying ones, which are treated respectively by offline andonline identification algorithms. Therefore, both the reliability and accuracy of the predictivemodel are improved. Two simulation examples of control of a fixed bed reactor show that this newalgorithm is not only reliable and stable in the case of uncertainties and abnormal disturbances,but also adaptable to slow time varying processes.

Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing

In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.

OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Preventive online and offline health management intervention in polycystic ovary syndrome

BACKGROUND Polycystic ovary syndrome(PCOS) is characterized by hyperandrogenism, hyperinsulinemia, ovarian polycystic changes, and irregular ovulation, often occurring in women of childbearing age for whom it can be a cause of infertility. Hypothalamus-pituitary-ovarian axis dysregulation is important in the pathogenesis of PCOS and the associated chronic excess of sex hormones can lead to cardiovascular and cerebrovascular diseases, diabetes, and malignancies such as endometrial cancer, and breast cancer. At present, most scholars agree that lifestyle interventions in conjunction with drug treatment can help PCOS patients achieve their goals of successful pregnancy and childbirth.AIM To investigate the clinical effect of an online and offline(O2O) preventive health management model on PCOS with kidney deficiency and phlegm dampness.METHODS A total of 82 patients with PCOS of the kidney deficiency and phlegm dampness type who were admitted to Beijing Luhe Hospital Affiliated to Capital Medical University from April 2019 to June 2020 were randomly divided into two groups. The treatment group was treated with oral Diane-35 for 3 mo and received preventive O2O medical health management for 6 mo(including eating and living, exercise, drug management). The control group was treated with oral Diane-35 for 3 mo and completed outpatient health education. The traditional Chinese medicine(TCM) syndrome score, acne score, hair score, sex hormone level and clinical effects were compared between the two groups before and after the intervention. RESULTS After treatment, the TCM syndrome score, acne score, and serum luteinizing hormone/follicle stimulating hormone level were significantly lower in the treatment group than in the control group(P < 0.05). After 3 mo of treatment, the TCM syndrome curative effect index in the treatment group was 97.30% compared to 54.05% in the control group(P < 0.05), whereas the total treatment effect in the treatment group was 91.89%, compared to 54.05% in the control group(P < 0.05).CONCLUSION An integrated therapeutic approach incorporating medication, TCM methods and social media is more effective than standard treatment for PCOS.

Teaching Model Integrating Online and Offline in Real Time ——An Exploration of New Teaching Mode for Regular Epidemic Prevention and Control

During epidemic,students in medium-risk or high-risk areas are unable to return to school on time.In response to this new challenge,there is an urgent need to create a new teaching mode to offer on-line courses to those absent from the offline classes,and we propose a model integrating online and offline teaching.It is based on“dual-camera”method,which allows off-campus students to virtually build up a physical classroom scenario on campus through computers and mobile phones.Using this model,students can participate in class remotely.In order to enhance the engagement of off-campus online students,emphasis is placed on interactive teaching.Teachers are required to design their teaching in advance and to work in collaboration with multiple departments,then using information technology and suitable teaching methods to enable students to participate in physical classroom teaching.This model has been tested in practice and has been successful in meeting the challenges.Finally,4 areas for improvement and refinement are identified.

基于MOOC构建高职院校“Online To Offline”教学模式研究

文章对比了x MOOC和c MOOC两种MOOC形式,分析了讲授型、任务驱动式、支架式以及协作研究式四种信息化教学模式,结合"95后"高职院校学生的特点,提出了基于MOOC构建高职院校"Online To Offline"教学模式,并结合专业知识、专业技能和职业素质对O2O教学模式进行了介绍。

Antecedents of Social Network Trust in SNS Usage: The Moderating Role of Offline Familiarity

With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor contributing to online purchasing behavior. This article explored several factors in social media trust and verified the moderating role of offline familiarity by using relevant research on online trust in conjunction with a structure equation model. The results show that independent variables such as reputation, SNS interaction, information quality, reciprocity, satisfaction and shared values have a positive influence on trust, whereas perceived similarity does not, and information quality is the most important factor. In addition, offline familiarity significantly moderates the relations between information quality, reciprocity, reputation, shared values and social media trust. This information is important to assist companies in developing an effective social network marketing strategy.

Wire Bonding Using Offline Programming Method

Manual process of creating bonding diagram is known to be time consuming and error prone. In comparison, offline programming (OLP) provides a much more viable option to reduce the wire bonding creation time and error. OLP is available in two versions, i.e., vendor specific OLP and direct integration offline pro- gramming (Di-OLP). Both versions utilize the bonding diagram and computer aided design data to speed up bonding program creation. However, the newly proposed Di-OLP is more flexible as it can be used to create bonding program for multiple machine platforms in microelectronics industry. Some special features of Di-OLP method are presented. The application of generic OLP however, is applicable to machines that recognize ASCII text file. The user needs to know the data format accepted by machine and convert the data accordingly to suit its application for different machine platforms. Di-OLP is also a practical method to replace the time consuming manual method in production line.

O2O＇s construction based on the research of Business model withintegration of online and offline channel

With the rapid development of information technology, it appears that a single online channel or a single offline is not able to meet the consumers＇ shopping needs, the enterprises will have a better development only by making the online and offline channels be in fusion and promote each other. This paper will combine the general business modes with corporate channel integration pattern, build out different forms of the market mode including market, enterprise and customers basing on different influence level, and analyze out the enterprise how to implement the business mode of integration basing on the influence factors under the background of different businessmodes, then apply the corresponding O2Oprocess to the mode, so as to broaden the enterprise selling channels for a better development.

Reference Selection for Offline Hybrid Siamese Signature Verification Systems

This paper presents an off-line handwritten signature verification system based on the Siamese network,where a hybrid architecture is used.The Residual neural Network(ResNet)is used to realize a powerful feature extraction model such that Writer Independent(WI)features can be effectively learned.A single-layer Siamese Neural Network(NN)is used to realize a Writer Dependent(WD)classifier such that the storage space can be minimized.For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively,we propose a method of selecting a reference signature as one of the inputs for the Siamese network.To take full advantage of the reference signature,we modify the conventional contrastive loss function to enhance the accuracy.By using the proposed techniques,the accuracy of the system can be increased by 5.9%.Based on the GPDS signature dataset,the proposed system is able to achieve an accuracy of 94.61%which is better than the accuracy achieved by the current state-of-the-art work.