• Acta Via Serica
• /
• v.7 no.2
• /
• pp.39-64
• /
• 2022

Ancient Iranians highly esteemed the horse and horse tacks, one of which is the jul (saddlecloth). It is a felt, sheepskin, or woven pad placed between the horse's back and saddle. The aim of this paper is to identify and categorize jul designs in the Sāsānian period. The research questions are about the variety of jul designs and how to categorize them. This is fundamental research and the method is descriptive and analytical. Neither a jul nor a saddle-cover remains from the Sāsānian period, therefore the statistical population includes all available items, such as metal and stone items and parget and plasterworks, in which juls are recognizable. Due to the scarcity of such items, all the available samples were studied; so the sampling method is a total enumeration. This is documentary research by means of note-taking and using reliable websites; the data has been analyzed qualitatively. The results show that jul designs were not diverse in the Sāsānian period. All-over designs were dominant. In terms of pattern types, these designs are classified into five groups, each of which has its own formal and aesthetic characteristics: all-over design with a four-petal flower pattern, allover design with a checkered pattern, all-over design with a spotted pattern, allover design with a tiger stripe pattern, and all-over design with a zigzag pattern.

• Computers and Concrete
• /
• v.28 no.5
• /
• pp.487-495
• /
• 2021

The use of prestressed concrete box girder bridges built by segmentally balanced cantilevers has bloomed in the last decades due to its significant structural and construction advantages in complex topographies. In Colombia, this typology is the most common solution for structures with spans ranging of 80-200 m. Despite its popularity, excessive deflections in bridges worldwide evidenced that time-dependent effects were underestimated. This problem has led to the constant updating of the creep and shrinkage models in international code standards. Differences observed between design processes of box girder bridges of the Colombian code and Eurocode, led to the need for a validation of in-service status of these structures. This study analyzes the long-term behavior of the Tablazo bridge with data scarcity. The measured leveling of this structure is compared with a finite-element model that consider the most widely used creep and shrinkage models in the literature. Finally, an adjusted model evidence excessive deflection on the bridge after six years. Monitoring of this bridge typology in Colombia and updating of the current design code is recommended.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.259-264
• /
• 2022

The food purchase platform using the metaverse-based multinational student community is a metaverse purchase platform using the multinational food preference community. Through the use of this platform, it is expected to establish a community of multinational international students, a community of international student sellers, and foster a new industry of metaverse purchase platforms. The metaverse platform guarantees anonymity, is space-time-free, and can freely communicate with avatars to create a second business communication. In addition, by selling many kinds of favorite foods in small quantities, it customizes scarcity while reducing the burden of providing services and making it easier for international students to participate.This platform can enhance to more business opportunities using community manpower.

• Journal of Information Processing Systems
• /
• v.18 no.4
• /
• pp.535-548
• /
• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• The Journal of Economics, Marketing and Management
• /
• v.11 no.4
• /
• pp.1-10
• /
• 2023

Purpose: This study investigated the relationship between product characteristics and customer purchase intention in live-streaming commerce. Research design, data and methodology: Six independent factors namely, scarcity, customization, discount, experimentalism, novelty, and informativeness were identified to analyze their effects on customer purchase intention in live-streaming commerce. The perceived value was accepted as a mediator between independent and dependent variables. Data were gathered from 643 respondents who experienced purchases in live-streaming commerce in China. Results: The results show that product characteristics strongly affect customer purchase intention, and perceived value plays an important mediating role in live-streaming commerce. Therefore, when developing a sales strategy in live-streaming commerce, product characteristics. Such as customization, discount, experimentalism, novelty, and information must be considered. Conclusions: The majority of live-streaming commerce research has focused on platform interactions and consumers. This study is meaningful in that it dealt with product characteristics and confirmed the mediating roles of perceived value in live-streaming commerce. The findings of this study have significant implications and offer valuable insights and practical guidance for both the academic community and practitioners engaged in the field of live-streaming commerce.

• Journal of Veterinary Clinics
• /
• v.40 no.4
• /
• pp.243-259
• /
• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• The Journal of Industrial Distribution & Business
• /
• v.14 no.9
• /
• pp.31-39
• /
• 2023

Purpose: Social media's increasing adoption and the development of digital technology have completely changed how businesses interact with their clients. The current study is to examine the impact of live broadcasts on consumers' perceptions and actions across a range of fresh food goods. Research design, data and methodology: The scrutiny relies on the existing peer-reviewed literature, which may prevent a comprehensive evaluation of some recent advancements in the subject. Despite these caveats, the outcomes of this scrutiny are anticipated to contribute significantly to our understanding of the effect of live broadcast marketing on consumers' propensity to make purchases. Results: Previous literature review clearly states that 'Live Broadcast of Fresh Food' to attract relevant customers should be followed: (1) Increased Product Transparency and Trust, (2) Enhanced Customer Engagement, (3) Impact on Customer's Perception of Product Quality, and (4) sense of urgency and scarcity. Conclusions: All in all, the study's advice for firms in the food industry to improve their marketing efforts through live broadcasts have important practical ramifications. Promoting openness and trust in the production process and with the audience boosts a brand's reliability. Customers are more likely to participate and feel more connected to a brand.

• Geomechanics and Engineering
• /
• v.32 no.2
• /
• pp.193-207
• /
• 2023

In general, the numerous classical approaches available in the literature can anticipate the settlement of shallow foundations. As long as the footings are not in close proximity to other subsurface buildings, the findings achieved using these methods are legitimate and acceptable. However, due to increased urbanisation and land scarcity, footings are frequently built close together. As a result, these footings' settlement behaviour differs from those of isolated footings. A simpler approach for assessing the settlement behaviour of two square or rectangular footings placed in close proximity is presented in this work. A Parametric study has been carried out to examine the interference effect on the settlement of these footings placed in close vicinity on the surface of a homogeneous, isotropic and elastic soil medium. The interaction factors are examined by varying the different aspect ratios (L/B), clear spacing ratio (S/B) and intensity of loading on the right footing with respect to the left footing. Further, variation of the settlement ratio (δ/B) with respect to embedment depth ratio D_f/B is examined. For square and rectangular footings, the interference settlement profile is also investigated by varying the clear spacing ratio (S/B) and the degree of loading. The results were compared to 3D finite element analysis and experimental data that were available.

• International Journal of Computer Science & Network Security
• /
• v.23 no.1
• /
• pp.140-146
• /
• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.

• Journal of Information Technology Services
• /
• v.22 no.1
• /
• pp.43-56
• /
• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.