Water consumption has grown at more than 2.5 times, comparing the past century. About 2.8 billion people live in river basins with some form of water deficit, because more than 75 % of the river flows are withdrawn for agriculture and other needs. Challenges faced by more and more countries in their struggle for economic and social development are increasingly related to water. This paper proposes a test of an effective local river field environment management solution. And describing a part of a pilot project for the ministry of water resources of Uzbekistan. Current work focused on direct action items of the existing project and describe an IoT routing based solution for local river field environment management solutions. Suggested technological decisions provided by needs and on-site testing results. The paper describes the backbone of IoT routing based river water resources management system.

In this paper, we designed a soft switched synchronous boost converter, which can perform discharging the battery, is simulated, and experimented designed. The converter operates synchronous operation to increase efficiency of the converter. The converter has very small switching losses because of its soft switching characteristics. In this paper, battery discharger with a switching frequency of 100 kHz have been designed. The designed converter also simulated and experimented to prove the converter's characteristics during synchronous operation. The simulated and experimental results have confirmed that the battery discharger had soft switching characteristics. In addition, the experimental results confirm that the converter has high efficiency characteristics. The efficiency of the circuit exceeds 97%, the efficiency of soft switched synchronous boost converter is at least 6% higher than that of conventional PWM boost converter.

With the continuous development and application of Internet technology, in recent years, new technologies such as cloud computing, big data, the Internet of Things, and AI are becoming more and more familiar to the general public. The development of a digital society has entered a new period of development. In this paper, we used on the 2018 annual data of 50 listed companies with blockchain concept stocks in China. Using data envelopment analysis (DEA) to study and analyze the input-output efficiency, it can be concluded that the input-output efficiency of 50 listed companies is very different. Inefficient companies are as high as 62%. Most companies have a large room for improvement in input-output efficiency due to uneconomical scale or inefficient technology. In order to better improve the company's input-output efficiency, one must improve the efficiency of resource utilization, optimize the company's research and development costs and the input and management of technical personnel; the second is to increase technological innovation and business innovation.

YouTube has been gaining popularity all around the world. A lot of companies have created their own YouTube channels to leverage them in diverse ways; they upload commercial videos, show people user reviews, and conduct promotions with their products. Sports clubs are no exception; they upload diverse videos to gain popularity and to interact with their fans. This study analyzes how the English Premier League (EPL) clubs leverage their YouTube channel as soccer (football in European nations) players. YouTube activities of 20 clubs during the 2019/2020 Season are investigated. After careful consideration, we decide to examine two factors of the respective channel of the YouTube: 1) Popularity (the number of views and subscribers) 2) Contents of videos (e.g. interviews and highlight scenes). The study followingly inspects the benefits of utilizing YouTube channels and of direct communication between sports clubs and fans in online settings.

In this study, a nursing management application for fourth-year students majoring in nursing at university was developed and practically applied in lectures. The application consists of an introduction, learning contents, and quiz contents. Nursing students downloaded and used it on their smartphones, such as iPhones and Android phones. The learning contents of a nursing management application include theoretical contents related to courses on nursing management and quiz contents comprising basic, advanced, and mixed quizzes based on the learning contents. Nursing students had access to this application on their smartphones and studied the contents provided regardless of time and space. Moreover, they were allowed to check their scores as soon as they selected and answered the questions included in the quiz contents. Nursing students used a nursing management application during and after lectures. We confirmed their learning process and learning results anytime and provided relevant feedback.

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

Non-orthogonal multiple access (NOMA) has been recognized as a significant technology in the fifth generation (5G) and beyond mobile communication, which encompasses the advanced smart convergence of the artificial intelligence (AI) and the internet of things (IoT). In NOMA, since the channel resources are shared by many users, it is essential to establish the user fairness. Such fairness is achieved by the power allocation among the users, and in turn, the less power is allocated to the stronger channel users. Especially, the first and second strong channel users have to share the extremely small amount of power. In this paper, we consider the power optimization for the two users with the small power. First, the closed-form expression for the power allocation is derived and then the results are validated by the numerical results. Furthermore, with the derived analytical expression, for the various channel environments, the optimal power allocation is investigated and the impact of the channel gain difference on the power allocation is analyzed.

There are many limitations in applying agile methodology to projects so far. In fact, projects overlap with other development methodologies and management processes, resulting in confusing progress, while there are often cases where the benefits of agile cannot be embraced due to a lack of understanding of agile processes and the organization's inability to adapt to change. It is an important role of information system development to improve the quality of agile application projects by discovering the problems and improvements in these cases. In this paper, we analyze the differences between existing development and project management methods and agile, and analyze agile processes. The optimal agile methodology was proposed by analyzing and applying methodologies established and applied mainly by large domestic development companies. In addition, the effectiveness was verified through a questionnaire of IT experts, and the result of the questionnaire was verified to be 4.75 points on a 5-point scale.

This study was conducted in order to determine how reductions in blood pressure during surgery affect intraoperative neuromonitoring. This retrospective study considered a total of 339 patients: 194 patients with normal neuromonitoring findings (57%), 145 (42%) with abnormal neuromonitoring findings, and 34 (10%) with postoperative neurological deficits. Comparisons between the three groups revealed that overall blood pressure during surgery, postoperative blood pressure, and the difference between the maximum and minimum blood pressure could affect the intraoperative neuromonitoring findings. While we indicate that a drop in blood pressure to below 70 mmHg could affect neuromonitoring results, differences in the dosage of anesthetic agents did not significantly affect reductions in blood pressure or neuromonitoring findings. The association of monitoring with blood pressure found in this study is expected to help future examiners. However, this study did not clarify the relationship between anesthesia and blood pressure and how it could affect intraoperative neuromonitoring. Therefore, further research on this part is thought to be necessary.

Due to social distancing caused by COVID 19, music concerts have not been allowed at home and abroad, and it is not known when overseas concerts will be able to resume unless the pandemics calm down, even if they are available. Meanwhile, people consume much more content through online services such as Netflix and YouTube than before. Under these circumstances, the music industry expects the virtual livestreamed performance to restore sluggish live performances again and bring about a digital renaissance. It also leads to the issue of reestablishing a copyright system regarding a livestreamed performance and of preparing a new rate of royalty standards for the use of works in OTT. In addition, a multi-territorial licensing of r ights in musical works is necessary to resolve copyright issues efficiently by applying the same term s to all countries from where the audience comes. In this paper, I examine the legal nature and the decision of copyright fees of performances as an integral part of the music industry.

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

In this paper, we propose a method to derive the kernel function directly from the measured apparent earth resistivity. At this time, the kernel function is obtained through the process of solving a nonlinear system. Nonlinear systems with many variables are difficult to solve. This paper also introduces a method for converting nonlinear derived systems to linear systems. The kernel function is a function of the depth and resistance of the Earth's layer. Being able to derive an accurate kernel function means that we can estimate the earth parameters i.e. layer depth and resistivity. We also use various Earth models as simulation examples to validate the proposed method.

The "New Korean Wave" (Shin Hallyu) phenomenon, which is represented by the global popularity of BTS and director Bong Joon-ho's film "Parasite," is making the world excited. The influence of the Korean Wave is not only on politics, society, and culture, but it also has considerable economic ripple effects. We applied individual in-depth interviews with seven media and marketing experts in China to assess the changes in the impact of Korean cultural content (i.e., Hallyu content) and examine the possibility of marketing for Korean small and medium-sized enterprises (SMEs) products using Hallyu content. Considering its massive market potential, China is still an attractive target for Korean companies and is of even greater importance to SMEs, which are highly dependent on exports and lacking marketing budgets as well as a professional workforce. Korean businesses and the economy at large would get a real boost if they could penetrate the Chinese market by utilizing the strength of Hallyu content. Based on the results of the in-depth interviews, the conclusion of our study suggests the direction of advertising strategy on how Korean products can attract the minds of Chinese consumers by utilizing the Shin Hallyu. This research is a practical study that diagnoses the reality of the Korean Wave in China objectively and has excellent practical implications as it proposes methods of how SMEs should use Hallyu for their marketing strategies.

In this paper, we present an effective single image fog removal by using the Joint Bright and Dark Channel (JBDC) and pixel-based transmission estimation to enhance the visibility of outdoor images susceptible to degradation due to weather and environmental conditions. The conventional methods include refinement process of coarse transmission with heavy computational complexity. The proposed transmission estimation reveals excellent edge-preserving performance and does not require the refinement process. We estimate the atmospheric light in pixel-based fashion, which can improve the transmission estimation performance and visual quality of the restored image. Moreover, we propose an adaptive transmission estimation to enhance the visual quality specifically in sky regions. Comprehensive experiments on various fog images show that the proposed method exhibits reduced computational complexity and excellent fog removal performance, compared with the existing methods; thus, it can be applied to various fields including real-time devices.

The purpose of this study is to measure the productivity of fisheries in China's coastal areas (including inland and marine fisheries) and to analyze the factors influencing the development of fishery economy in each province by comparing regional differences. The input indicators used for efficiency analysis are fish farms, ships and staff numbers, and the output indicators are fish catch and net income. In this paper, we used six years of data from 2013-2018 and DEA-Malmquist index was used to analyze the efficiency of coastal fisheries production in 11 areas of China's coast. According to the analysis from 2013 to 2018, the fishery output efficiency in China's coastal areas has improved to some extent, but most provinces and regions are characterized by technological progress, and the technological efficiency still needs to be further improved. The conclusion can provide reference for the development planning and policy measures of regional fishery industry in China's coastal areas.

Orthogonal time frequency space (OTFS) modulation is considered as one of the solutions to cope with high mobility channel environments. It converts the time-varying channel to the near-constant channel response in the delay-Doppler domain. This modulation scheme also benefits from the diversity in two-dimensional modulation. According to recent researches, this method outperforms the conventional OFDM modulation, especially in high-speed channel conditions. In this paper, to investigate the performance of OTFS in a practical system, channel estimation in the delay-Doppler domain is compared with the conventional method in the time-frequency domain at different mobile speeds. Simulation results confirm that the delay-Doppler domain channel estimation brings a better performance compared to the conventional one under the same overhead rate.

Iterative decision feedback equalizer (IDFE) is a recursive equalization technique that can help to achieve an additional performance gain for the system by combining iterative channel decoding and interference cancellation. In a single carrier-based system, the intersymbol interference (ISI) is a critical problem that must be resolved since it causes frequency selective fading. Based on the idea of sharing the estimated information in the process of iteration, IDFE is considered as an efficient solution to improve the robustness of the system performance on the ISI channel. In this paper, the IDFE is applied on single carrier FDMA (SC-FDMA) system to evaluate the performance under ISI channel. The simulation results illustrate that IDFE helps to improve the performance of the SC-FDMA system, especially with long delay spread channels.

When conducting a health screening, it is important to select the most appropriate hospitals for the screening items. There are various packages in the screening hospitals, and the screening items and price are very different for each package. In this paper, we provide a method of recommending the screening packages in consideration of the customer's preferences such as screening items and minimum matching ratio. First, after collecting package information of hospitals, information such as basic items and optional items in the package are extracted. Then, we determine whether the client's screening items exist in the basic item or optional item of the package and calculate the matching rate of the package. Finally, we recommend screening packages with the lowest price while meeting the minimum matching rate suggested by the client. For performance analysis, we implement a prototype for recommending screening packages and provide the experimental results. The performance analysis shows that the proposed approach provides a real-time response time and recommends appropriate packages.

This research aims to provide empirical basic data for the development of local communities through the establishment of efficient strategies for the formation of positive relations between the club and the region through the Creation of Shared Value (CSV) activities. We come up with the results of this study to show that CSV, social problem orientation, and core competency utilization affect the team trust of local community, and that CSV, long-term orientation, social problem orientation and core competency utilization affect community identification. The research should enhance the value of professional sports teams that promote their own path to self-sustaining as a single independent business area, and identify their trust through CSV activities for co-prosperity with their local communities, and thus their relationship with the community. Therefore, various strategies and efforts will ultimately increase the team trust and community identity of local residents in professional volleyball teams, while at the same time gaining positive effects on professional volleyball teams.

Over the last half-century, computer science has revolutionarily changed the landscape of humanities research. This digital shift in research methodology has reached from the brainstorming process to preserving, constructing, collecting, visualizing, and even analyzing materials. Such transformation has brought about the birth of the new field of study: Digital Humanities (DH). DH undeniably has saved much of the physical chores and provided a new angle to interpret the text, thereby making its meteoric rise as a promising future of the humanities. Based on such innovation, electronic circuitry can seem to replace the imagination that detects relationships and significances of research data with ever-improving interfaces. However, despite hitherto technological development, the thousands-year-old essence of traditional liberal arts-human creativity-remains the heart of humanities research and always will. This paper starts by proving this proposition in the way of comparing the old and new liberal arts research methods, focusing on literary studies. Meanwhile, it thoroughly investigates how digitalized bibliographies, search engines, databases, and digital projects provide the most useful data preservation and virtual experience of browsing in the library, along with their limitations due to the intrinsic quality of humanities research data. Also, it probes the differences between traditional and digital data analysis in current methods of literary studies, ultimately presenting the ideal direction for humanities development in the era of digital convergence.

A lot of research has been conducted on a system that collects and observes patients' health information in real time using Internet of Things (IoT) technology, and cares for and supports patients based on this. However, most studies have focused on underlying diseases such as diabetes or cardiovascular disease, and research on IoT systems to cope with respiratory infectious diseases such as COVID-19 is still insufficient. In a COVID-19 situation, the purpose of using an IoT respirator may vary depending on the user. In this paper, we design a system that can adequately cope with respiratory infectious diseases such as COVID-19 by applying IoT technology to respiratory protection. We categorize IoT respirator wearers into patients, medical staff, and self-quarantine persons, and define the purpose and use case of the IoT respirator system according to each classification. The proposed IoT respirator system was designed to achieve each purpose. We developed a prototype system consisting of a smart sensor, a communication module, and a non-motorized hooded respirator to show that the proposed IoT respirator system works.

In this paper, to analyze the meaning of middle-aged men's economic activities, TEXTOM was used to analyze them. The data collection period is set from 2017 to 2019. Among the collected data, 100 refined words were converted into a matrix in which the degree of social connection was calculated, and the keyword network analysis was performed again with the NetDraw program. According to the study, middle-aged men put more meaning on their current work and family than their future retirement. Also, the related word commonly included in the top five for all three years was 'work'. Related words commonly included in the top 10 were 'old age', 'family', and 'work', and in 2018 and 2019, 'health' was included in the top 10. As a result of this, the middle-aged men living in the modern age are the generation who keep their families through economic activities and are increasingly interested in health and prepare for retirement. Therefore, policy support for stable economic activities is needed to improve the quality of life for middle-aged men. It is necessary to extend the retirement age, expand jobs and provide effective vocational training so that it can handle its role as the head of a family. In addition, measures should be taken to reduce the wage gap between highly skilled and low-skilled workers.

Today, taking photos using smartphones has become an essential element of modern people. According to these social changes, modern people need a larger storage capacity, and the number of unnecessary photos has increased. To support the storage, cloud-based photo storage services from various platforms have appeared, and many people are using the services. As the number of photos increases, it is difficult for users to find the photos they want, and it takes a lot of time to organize. In this paper, we propose a cloud-based photo management service that facilitates photo management by classifying photos and recommending unnecessary photos using deep learning. The service provides the function of tagging photos by identifying what the subject is, the function of checking for wrongly taken photos, and the function of recommending similar photos. By using the proposed service, users can easily manage photos and use storage capacity efficiently.

The 3D point cloud is a key technology of object detection for virtual reality and augmented reality. In order to apply various areas of object detection, it is necessary to obtain 3D information and even color information more easily. In general, to generate a 3D point cloud, it is acquired using an expensive scanner device. However, 3D and characteristic information such as RGB and depth can be easily obtained in a mobile device. GNN (Graph Neural Network) can be used for object detection based on these characteristics. In this paper, we have generated RGB and RGBD by detecting basic information and characteristic information from the KITTI dataset, which is often used in 3D point cloud object detection. We have generated RGB-GNN with i-GNN, which is the most widely used LiDAR characteristic information, and color information characteristics that can be obtained from mobile devices. We compared and analyzed object detection accuracy using RGBD-GNN, which characterizes color and depth information.

A wheelchair is an essential rehabilitation assistant device for the movement of paraplegia patients and generally paralyzed patients who cannot walk normally. In particular, the applicability of the manual/motorized wheelchair is gradually increasing. Until now, decelerators using belt, chain and worm gears, etc have been widely used. However, a decelerator takes a large space although it is a simple device and thus is not ideal for the driving part of manual/motorized wheelchair. For these reasons, in this study we developed a driving part producing a large driving force through a decelerator using planetary gears rather than conventional worm gear-based decelerator. We designed the tooth profile of the planetary gears for decelerator using Kisssoft program, In addition, we designed the driving part so as to apply it to the wheels of conventional wheelchairs, and then optimized the mechanism for the principles of manual/motorized transposition of the driving part and the operational principles. Based on the results of this study, we finally designed and manufactured a driving part for wheelchair decelerator in the form of planetary gears with 1 sun gear, 2 planetary gears and 1 ring gear.

Currently, the operation of unmanned aerial vehicle (UAV) is regulated to be able to fly only within the visible range, but in recent years, the needs for operation in the invisible area, in the urban area and at night have increased. In order to operate UAVs in the invisible area, at night, and in the urban area, a flight path for UAVs must be prepared like those operated by manned aircraft, and for this, it is necessary to establish an unmanned aircraft system traffic management (UTM). In order to establish the UTM, information on the minimum separation distance to prevent collisions with UAVs and buildings is required, and accordingly, information on the navigation performance of UAVs is required. In order to analyze the navigation performance of an UAV, total system error (TSE), which is the difference between the planned flight path and the actual location of the UAV, is required. If the collected data are insufficient and classification according to integrity, independence, and direction is not performed, accurate navigation performance is not derived. In this paper, propose a navigation performance analysis method of UAV that is derived TSE using flight data and modeled with normal distribution, analyze performance.

The purpose of this study is to analyze the structural relationship among expectation value(relational, hedonistic), brand image, brand attitude, continuance viewing intention. The survey subjects to achieve the purpose of this study were selected the 521 YouTube sports contents Viewer in the metropolitan area. Data processing was done with SPSS 23 for frequency analysis, Cronbach's α analysis. Also, AMOS 21 was used for confirmatory factor analysis and structural equation model analysis. The results of the analysis are as follows: First, both relational value and,, hedonistic value had a positive effect on the brand image. Second, both relational value and,, hedonistic value had a positive effect on the brand attitude. Third, both brand image and brand attitudee had a positive effect on the brand attitude.

Smart homes have become the state of the art in the reduction and monitoring of energy usage within a residential setting. Emerging threats such as climate change, global warming and volatility in energy prices have fuelled the interest in smart systems. Given that environmental sustainability has become a more significant factor for consumers, this research examines whether consumers' attitudes toward smart home services for efficient energy management differ according to their regulatory focus. Specifically, it is predicted that consumers will have more favorable attitudes toward smart home services for efficient energy management when they are promotion-focused (vs. prevention-focused). The results indicate that respondents with a promotion (vs. prevention) focus reported significantly more favorable attitudes toward smart home services for energy management (e.g., smart cooling/heating system, smart ventilation & air conditioning system, smart thermostats, smart plugs, and smart switches). We suggest that regulatory focus may be an effective marketing and segmentation tool in promoting smart home services for energy management and facilitating their receptiveness to the services.

Recently, the importance of user experience (UX) has been rapidly increasing. Its utilization is emphasized for development of systems, products, and services. User experience is widely used across industries including services, products, processes, society, and culture. Therefore, if it is unsatisfactory, it is likely to have a direct negative impact on the corresponding system, product, or service. The failure to analyze user experience causes significant damage to the project, which may lead to its failure or redevelopment; it is hence necessary to prioritize the verification of UX in the earliest stages of development. The requirements development stage, which is a preceding stage, is an appropriate stage for the verification of user experience because the identification of user needs is completed and prototypes can be implemented. In this paper, we proposed a systematic requirements development stage; it adds user experience verification activities to the requirements development stage, using the Honeycomb model, which is a widely used tool for verifying the overall UX. User experience verification was added to the existing requirements development activities, which consisted of three steps: model definition and requirements placement, discussions between external and internal stakeholders, and review by internal stakeholders. By easily validating the user experience through this systematic requirements development stage, we expect to minimize the damage to the project due to the failure of the user experience analysis and increase the possibility of success.

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

The Ewha Music Database (EMDB) is an online database comprised of primary source materials related to music education from East Asia during the modern era (1880 to 1945) when Korea, Japan, and China were geopolitically and culturally intertwined. We developed the incipit search in EMDB as an embedded tool. This is the first attempt in Korea to implement a unique search function of musical data using alphabets of musical notes. Unlike in traditional search system that uses general literature information search conditions, such as author, title, publisher, year, number of pages, etc., it offers a new way of searching a musical piece/work and sheet music. This study confirms that digital information technology is an important methodology for research of music culture as a field of humanities.

Facial recognition is a biometric technology that is used in various fields such as user authentication and identification of human characteristics. Face recognition applications are practically used in various fields, but very few applications have been developed to improve the factory work environment. We implemented applications that uses face recognition to identify a specific employee in a factory .work environment and provide customized information for each employee. Factory workers need documents describing the work in order to do their assigned work. Factory managers can use our application to register documents needed for each worker, and workers can view the documents assigned to them. Each worker is identified using face recognition, and by tracking the worker's face during work, it is possible to know that the worker is in the workplace. In addition, as a mobile app for workers is provided, workers can view the contents using a tablet, and we have defined a simple communication protocol to exchange information between our applications. We demonstrated the applications in a factory work environment and found several improvements were required for practical use. We expect these results can be used to improve factory work environments.

Most of today's large-scale cloud systems and enterprise data centers are distributing resources to improve scalability and resource utilization. NVMe-over-Fabric protocol allows submitting NVMe commands to a remote NVMe SSD through RDMA (Remote Direct Memory Access) network. It is attracting attention recently because it is possible to construct a disaggregation storage system with low latency through the protocol. However, the current I/O stack of NVMe-over-Fabric has an inefficient structure for maintaining compatibility with the traditional I/O stack. Therefore, in this paper, we propose a new mechanism to reduce I/O latency and CPU overhead by modifying I/O path of NVMe-over-Fabric to pass through legacy block layer. According to the performance evaluation results, the proposed mechanism is able to reduce the I/O latency and CPU overhead by up to 22% and 24% compared to the existing NVMe-over-Fabrics protocol, respectively.

Smartphones provide blind users with screenreader as an accessibility tool. However, blind users often experience difficulties accessing online shopping malls via smartphones due to their inconsistent and image-based layouts. To enable screenreader users to get access to the detailed information about products while they are shopping online, we have developed BarrierFreeShop, an accessible mobile shopping application for people with visual impairments. BarrierFreeShop has three accessibility features: (1) layout automation, (2) review summarization, and (3) optical character recognition. We conducted a user study with 80 participants with visual impairments where they were asked to use BarrierFreeShop for a month. The findings revealed the effectiveness of our app in terms of speed and post interview feedback. We have also discovered typical shopping experiences that participants had during the test. This research suggests that computer vision technologies can improve accessibility issues in online shopping malls. In addition, we have confirmed that extracting contents from images help people with visual impairments to get better access to product information.

Hashtags (#) have received a great deal of attention from academia and industry as an effective digital tool for engaging social media users and facilitating electronic word-of-mouth for brands. We delved into motivations concerning people's brand-related hashtag-posting behavior on social media. The findings revealed three motivations for posting brand-related hashtags on social media: social acceptance, brand altruism, and incentive seeking. Additionally, we examined the relationships between motivations and brand relationship variables. The results showed that social acceptance and brand-related altruism predicted all of the five relationship variables (i.e., brand attitude, brand trust, brand affective and calculative commitment and brand loyalty), while incentive-seeking predicted brand calculative commitment and brand loyalty. The findings of the study provide some meaningful insights into the development of brand communication strategies and help marketers capitalize on social media platforms to achieve higher user involvement.