INVESTIGATION OF MICRO-BLOGGING MARKETING STRATEGY OF FASHION BRAND : VIA BIG DATA AND MACHINE LEARNING METHODOLOGY

Ruirui Zhang,Shan Xue,Leslie Davis Burns 글로벌지식마케팅경영학회 2014 Global Marketing Conference Vol.2014 No.2

In this paper, researchers investigated current fashion brands’ social media micro-blogging marketing strategy and consumers’ word of mouth reactions. More than 5,000 of the micro-blogs posted by fashion brands and 143,000 of customers’ comments were analysed in this study. Researchers firstly investigated the overall micro-blogging marketing structure for each fashion brand and compared them. Then researchers identified the type of expression pattern of each fashion brand currently in the micro-blogging context, negative or positive sentiments. Researchers found that fashion brands are using different micro-blogging marketing strategies. Forever21 used 61% micro-blogs for customer communication. H&M posted very diverse micro-blogs content to their official account. Their main micro-blogs were used for new product promotion (43%) and brand’s live event broadcasting (33%). Luxury brands, such as Burberry, more than 52% of micro-blogs posted last year were used for new product promotion and 36% of their micro-blogs contents included celebrities’ images of wearing Burberry product. Chanel used 60% of their micro-blogs to broadcast and introduce their brand events. There was no sale information posted on Chanel and Burberry’s micro-blogs account. Through sentiment analysis, researchers also found the brands have very positive electronic word of mouth (e-WOM). Particular, luxury fashion brands are having better e-WOM than fast fashion brands.

Fabricating patterned microstructures by embedded droplet printing on immiscible deformable surfaces

Ruirui Zhang,Lehua Qi,Hongcheng Lian,Jun Luo 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.105 No.-

The deposition of droplets inside the deformable surfaces has attracted researchers for decades due to itsapplication in direct-writing microporous polymer architectures, printing embedded flexible wires, patterningfunctional nanoparticles, etc. Herein, a patterned microstructure method based on droplets,named as the embedded droplet printing (EDP), is proposed. The experiments were conducted at aWeber number of 5.49–17.7 to explore the impact outcome, spreading laws and embedded morphologyof the droplets. The rebound of droplets impacting the viscous surface was suppressed under appropriateconditions. The spreading factor of the droplet impacting on the high viscous surface followed the powerlaw d*/ t a. However, the exponent a was observed to be in the range 0.042–0.031, much smaller than thereported values (0.1–1), which could be explained by the Oh number. The diameters of droplets wrappedwith viscous PDMS were only about 1/6th as compared to the spreading on the surface of PDMS precuredfor 30 min. In particular, a domain map was plotted in which patterns of solid bracts, plates and coffeerings were printed. Overall, EDP is a promising candidate to tailor the size, depth and morphology of dropletsfor preparing the patterned microstructures inside the soft materials.

INVESTIGATION OF MICRO-BLOGGING MARKETING STRATEGY OF FASHION BRAND: VIA BIG DATA AND MACHINE LEARNING METHODOLOGY

Ruirui Zhang,Shan Xue,Leslie Davis Burns 글로벌지식마케팅경영학회 2014 Global Marketing Conference Vol.2014 No.7

In this paper, researchers investigated current fashion brands’ social media micro-blogging marketing strategy and consumers’ word of mouth reactions. More than 5,000 of the micro-blogs posted by fashion brands and 143,000 of customers’ comments were analysed in this study. Researchers firstly investigated the overall micro-blogging marketing structure for each fashion brand and compared them. Then researchers identified the type of expression pattern of each fashion brand currently in the micro-blogging context, negative or positive sentiments. Researchers found that fashion brands are using different micro-blogging marketing strategies. Forever21 used 61% micro-blogs for customer communication. H&M posted very diverse micro-blogs content to their official account. Their main micro-blogs were used for new product promotion (43%) and brand’s live event broadcasting (33%). Luxury brands, such as Burberry, more than 52% of micro-blogs posted last year were used for new product promotion and 36% of their micro-blogs contents included celebrities’ images of wearing Burberry product. Chanel used 60% of their micro-blogs to broadcast and introduce their brand events. There was no sale information posted on Chanel and Burberry’s micro-blogs account. Through sentiment analysis, researchers also found the brands have very positive electronic word of mouth (e-WOM). Particular, luxury fashion brands are having better e-WOM than fast fashion brands.

Preoperative MRI Features Associated With Axillary Nodal Burden and Disease-Free Survival in Patients With Early-Stage Breast Cancer

Zhang Junjie,Yin Zhi,Zhang Jianxin,Song Ruirui,Cui Yanfen,Yang Xiaotang 대한영상의학회 2024 Korean Journal of Radiology Vol.25 No.9

Objective: To investigate the potential association among preoperative breast MRI features, axillary nodal burden (ANB), and disease-free survival (DFS) in patients with early-stage breast cancer. Materials and Methods: We retrospectively reviewed 297 patients with early-stage breast cancer (cT1-2N0M0) who underwent preoperative MRI between December 2016 and December 2018. Based on the number of positive axillary lymph nodes (LNs) determined by postoperative pathology, the patients were divided into high nodal burden (HNB; ≥3 positive LNs) and non-HNB (<3 positive LNs) groups. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors associated with ANB. Predictive efficacy was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Univariable and multivariable Cox proportional hazards regression analyses were performed to determine preoperative features associated with DFS. Results: We included 47 and 250 patients in the HNB and non-HNB groups, respectively. Multivariable logistic regression analysis revealed that multifocality/multicentricity (adjusted odds ratio [OR] = 3.905, 95% confidence interval [CI]: 1.685– 9.051, P = 0.001) and peritumoral edema (adjusted OR = 3.734, 95% CI: 1.644–8.479, P = 0.002) were independent risk factors for HNB. Combined peritumoral edema and multifocality/multicentricity achieved an AUC of 0.760 (95% CI: 0.707– 0.807) for predicting HNB, with a sensitivity and specificity of 83.0% and 63.2%, respectively. During the median follow-up period of 45 months (range, 5–61 months), 26 cases (8.75%) of breast cancer recurrence were observed. Multivariable Cox proportional hazards regression analysis indicated that younger age (adjusted hazard ratio [HR] = 3.166, 95% CI: 1.200–8.352, P = 0.021), larger tumor size (adjusted HR = 4.370, 95% CI: 1.671–11.428, P = 0.002), and multifocality/multicentricity (adjusted HR = 5.059, 95% CI: 2.166–11.818, P < 0.001) were independently associated with DFS. Conclusion: Preoperative breast MRI features may be associated with ANB and DFS in patients with early-stage breast cancer.

Direct printing of surface-embedded stretchable graphene patterns with strong adhesion on viscous substrates

Ruirui Zhang,Lehua Qi,Hongcheng Lian,Jun Luo 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.109 No.-

The graphene patterns with piezoresistance behaviors, originating from the structural deformation andband-structure shift under strain, represent an attractive characteristic for developing the small strain(<10%) sensors for wearable devices. However, the insufficient adhesion between the patterns and substrateshas significantly limited their utility and reliability. Here, the surface-embedded graphene patternswere directly deposited on polydimethylsiloxane (PDMS) surfaces without hydrophilic treatmentvia the embedded droplet printing (EDP). The viscous PDMS films instead of the solid ones were usedas substrates, and the graphene patterns were partly embedded onto the viscous PDMS surfaces. Toassess the adhesion performance, a series of tests were performed. In the bending and tensile tests,the patterns strongly adhered to the PDMS films. Further, the patterns had a favorable increase in resistancein the tensile strain range of 0–3.5%. The resistance of the surface-embedded graphene patternsexhibited a negligible change for over 3 min in the ultrasonic bath. Finally, the relative resistance R/R0remained constant after the first two adhesion tests using a 3M tape. The surface-embedded graphenepatterns exhibited a strong adhesion to the flexible/stretchable substrates, indicating the application prospectin the field of flexible devices.

A Feasibility Study of Nano-grained ZnO Piezoelectric Thin Film Fabrication

Zhang, Ruirui,Lee, Eun-Ju,Yoon, Gi-Wan The Korea Institute of Information and Commucation 2009 Journal of information and communication convergen Vol.7 No.4

C-axis-oriented ZnO thin films were successfully deposited on p-Si (100) in an RF magnetron sputtering system. Deposition conditions such as deposition power, working pressure, and oxygen gas ratio $O_2/(O_2+Ar)$ were varied. Crystalline structures of the deposited ZnO films were investigated by a scanning electron microscope (SEM) technique. Results show that the deposition parameters can have a strong impact on the preferred orientations and grain sizes of the deposited ZnO films.

Study of Danger-Theory-Based Intrusion Detection Technology in Virtual Machines of Cloud Computing Environment

Zhang, Ruirui,Xiao, Xin Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1

In existing cloud services, information security and privacy concerns have been worried, and have become one of the major factors that hinder the popularization and promotion of cloud computing. As the cloud computing infrastructure, the security of virtual machine systems is very important. This paper presents an immune-inspired intrusion detection model in virtual machines of cloud computing environment, denoted I-VMIDS, to ensure the safety of user-level applications in client virtual machines. The model extracts system call sequences of programs, abstracts them into antigens, fuses environmental information of client virtual machines into danger signals, and implements intrusion detection by immune mechanisms. The model is capable of detecting attacks on processes which are statically tampered, and is able to detect attacks on processes which are dynamically running. Therefore, the model supports high real time. During the detection process, the model introduces information monitoring mechanism to supervise intrusion detection program, which ensures the authenticity of the test data. Experimental results show that the model does not bring much spending to the virtual machine system, and achieves good detection performance. It is feasible to apply I-VMIDS to the cloud computing platform.

Study of Danger-Theory-Based Intrusion Detection Technology in Virtual Machines of Cloud Computing Environment

( Ruirui Zhang ),( Xin Xiao ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1

In existing cloud services, information security and privacy concerns have been worried, and have become one of the major factors that hinder the popularization and promotion of cloud computing. As the cloud computing infrastructure, the security of virtual machine systems is very important. This paper presents an immune-inspired intrusion detection model in virtual machines of cloud computing environment, denoted IVMIDS, to ensure the safety of user-level applications in client virtual machines. The model extracts system call sequences of programs, abstracts them into antigens, fuses environmental information of client virtual machines into danger signals, and implements intrusion detection by immune mechanisms. The model is capable of detecting attacks on processes which are statically tampered, and is able to detect attacks on processes which are dynamically running. Therefore, the model supports high real time. During the detection process, the model introduces information monitoring mechanism to supervise intrusion detection program, which ensures the authenticity of the test data. Experimental results show that the model does not bring much spending to the virtual machine system, and achieves good detection performance. It is feasible to apply IVMIDS to the cloud computing platform.

An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response

Zhang, Ruirui,Xiao, Xin Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.1

Although the research of immune-based anomaly detection technology has made some progress, there are still some defects which have not been solved, such as the loophole problem which leads to low detection rate and high false alarm rate, the exponential relationship between training cost of mature detectors and size of self-antigens. This paper proposed an intrusion detection method based on changes of antibody concentration in immune response to improve and solve existing problems of immune based anomaly detection technology. The method introduces blood relative and blood family to classify antibodies and antigens and simulate correlations between antibodies and antigens. Then, the method establishes dynamic evolution models of antigens and antibodies in intrusion detection. In addition, the method determines concentration changes of antibodies in the immune system drawing the experience of cloud model, and divides the risk levels to guide immune responses. Experimental results show that the method has better detection performance and adaptability than traditional methods.

An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response

Ruirui Zhang,Xin Xiao 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.1

Although the research of immune-based anomaly detection technology has made some progress, there are stillsome defects which have not been solved, such as the loophole problem which leads to low detection rate andhigh false alarm rate, the exponential relationship between training cost of mature detectors and size of selfantigens. This paper proposed an intrusion detection method based on changes of antibody concentration inimmune response to improve and solve existing problems of immune based anomaly detection technology. Themethod introduces blood relative and blood family to classify antibodies and antigens and simulate correlationsbetween antibodies and antigens. Then, the method establishes dynamic evolution models of antigens andantibodies in intrusion detection. In addition, the method determines concentration changes of antibodies inthe immune system drawing the experience of cloud model, and divides the risk levels to guide immuneresponses. Experimental results show that the method has better detection performance and adaptability thantraditional methods.