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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Purchasing Intention of Tour Package Brands in Access to Digital technology and User Generated Content on Bali Travel Tour

Purchasing Intention of Tour Package Brands in Access to Dig...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Ngr. Putu Raka Novandra Asta Erma Suryani Raden Venantius Hari Ginardi Doctoral Program in Technology Management Interdisciplinary School of Management Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia Politeknik NegeriBali Bali Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Digital technology is currently a vital medium to influence consumers with diverse behaviours and a high level of sensitivity when purchasing goods and services. The purpose of this study is to analyse the impact of exposure to digital technology and user-generated content on the brand purchase intention of travel packages in the Bali tourism sector. As digital technology changes the way we communicate or convey information to attract consumers, one of the methods used to attract consumers is user-generated content (UGC) to create a more intimate engagement between products, services, and consumers. A total of 200 tourists who bought tour packages were used as respondents using accidental sampling. The results of the study explain that digital technology and user integration center have a significant influence on brand purchase intention, and brand exposure and brand familiarity have a considerable positive influence on brand purchase intention. A different influence is found: brand knowledge and attitude have a negative impact on brand purchase intention. The results of this study can enrich knowledge about the currently developing topic of e-marketing aimed at academics. From a practical perspective, this research can provide marketers with insight into the effectiveness of content as an advertising platform in the travel and tourism industry.

关键词： Sensitivity Consumer behavior Brand management User-generated content Tourism industry Companies Media Sustainable development Advertising

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Integrating System Dynamics and Random Forest Algorithm for Sales Forecasting

Integrating System Dynamics and Random Forest Algorithm for ...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Natalia Magdalena Rafu Mamulak Erma Suryani Jerry Dwi Trijoyo Purnomo Doctoral Program in Technology Management Interdisciplinary School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

This study proposes an approach to improve sales forecasting accuracy by integrating System Dynamics (SD) modeling with the Random Forest (RF) algorithm. The combination of these two methods offers a promising way to make sales predictions more reliable, especially in uncertain market conditions. Using SD principles within the RF algorithm helps us simulate expected sales growth, gain insights into market dynamics, and make our forecasting model more understandable to stakeholders. This study using synthetic sales data shows that the integrated approach is offered as an open idea and customizable, particularly in scenarios with dynamic growth patterns and feedback effects.

关键词： Radio frequency System dynamics Heuristic algorithms Predictive models Prediction algorithms Market research Stakeholders Reliability Forecasting Random forests

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UX and Industry 5.0: A Study in Repairing Equipment Using Augmented Reality 2nd

UX and Industry 5.0: A Study in Repairing Equipment Using Au...

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2nd International Conference on Intelligent systems Design and Engineering Applications, ISDEA 2023

作者： Margolis, Iara Carrança, Alexandre Sousa, Nuno Rocha, José Santos, Eduardo Evangelista, Luis Ferreira, António Adão, Telmo Sousa, Emanuel Centro de Computação Gráfica Campus de Azurém Guimarães4800-058 Portugal Lab2PT University of Minho Guimarães4800-058 Portugal Postdoctoral Program Post Graduate Program of Design University of Pernambuco Recife50740-550 Brazil Doctoral Program on Information Systems and Technology University of Minho Campus de Azurém Guimarães4800-058 Portugal Sonae Arauco Maia4470-177 Portugal Centro ALGORITMI Campus de Azurém Guimarães4800-058 Portugal

ISBN: (纸本)9789819963027

Within the scope of industry 5.0, mixed reality (MR) is an eminent technology for problem solving and process optimization. The present research aimed to assess how users perceive an augmented reality (AR) application for diagnosis and intervention on industrial equipment. 18 participants with 3 different professional and technological backgrounds (general users (GU), information technology users (SD) and human centered design experts (HF)) took part in this study. Generally, the developed system was valued as slightly positive and some improvement points were identified. The results also revealed differences in the perceived User Experience (UX) between different user profiles. Also, there are important differences in perception depending on the person's analysis experience, with less rigor for new users and higher rigor for people who work with human factors, for example. This reinforce the concept of user centered design and the importance to understand the human being in its relation with the novel user interface and tendency to new technologies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Augmented Reality Towards Industry 5.0: Improving Voice and Tap Interaction Based on User Experience Feedback 2nd

Augmented Reality Towards Industry 5.0: Improving Voice and ...

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2nd International Conference on Intelligent systems Design and Engineering Applications, ISDEA 2023

作者： Carrança, Alexandre Sousa, Nuno Rocha, José Santos, Eduardo Evangelista, Luis Ferreira, António Adão, Telmo Sousa, Emanuel Margolis, Iara Centro de Computação Gráfica Campus de Azurém Guimarães4800-058 Portugal Doctoral Program on Information Systems and Technology University of Minho Campos de Azurém Guimarães4800-058 Portugal Sonae Arauco Maia4470-177 Portugal Centro ALGORITMI Campos de Azurém Guimarães4800-058 Portugal Lab2PT University of Minho Guimarães4800-058 Portugal Postdoctoral Program Post Graduate Program of Design University of Pernambuco Recife50740-550 Brazil

ISBN: (纸本)9789819963027

EXtended Reality (XR) has numerous applications in industrial contexts, including to assist in activities such as preventive maintenance and/or reactive interventions in machinery, wherein time-effectiveness is required to mitigate the costs of inactive production lines or the need to subcontract specialized technicians to perform those operations. However, developing an Augmented Reality (AR) application capable of being user-friendly and truly serviceable for aiding in the referred context requires keeping in mind human factors as key aspects and, as such, interaction must be a concern to address, namely, considering user feedback. Therefore, starting from a previous AR system oriented to industrial interventions that was developed and assessed with users and considering the feedback of the participant, the duly alterations were done and a new test was conducted, with a total of 27 participants, divided into 2 groups: 11 users who participated in the first experience and 16 general users who had never contacted with the solution before. A qualitative improvement in the user experience was noticed from the questionnaires feedback. Points such as fluidity, responsiveness, ease, speed, and intuitiveness were highlighted. Voice commands matched the tap commands in terms of user preferences. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation

Rice Variety Identification Using Edge Feature Extraction an...

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International Seminar on Application for technology of information and Communication (ISemantic)

作者： Aji Setiawan Kusworo Adi Catur Edi Widodo Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Information Technology Faculty of Engineering Darma Persada University Jakarta Indonesia School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor, and LBP for texture analysis are popular feature extraction techniques widely employed in various cases. The approach combines edge and texture features with SVM to classify rice types into five categories: Ipsala, Jasmine, Karacadag, Arborio, and Basmati. Experimental results demonstrate that SVM using Canny, Sobel, and Prewitt combined Gabor texture features outperforms the comparative methods. A comparative analysis based on accuracy scores using confusion matrices is conducted. Combining all feature edge methods, Canny, Sobel, and Prewitt with Gabor on SVM classification is the best model for rice object classification, achieving an accuracy of 96%.

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The next cryptocurrency price movement prediction application uses patterned datasets

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AIP Conference Proceedings 2024年第1期3116卷

作者： Rizky Parlika Mustafid Basuki Rahmat Diponegoro University Information Systems Doctoral Study Program Graduate School 50241 Jl. Imam Bardjo SH Pleburan Semarang Indonesia University of Pembangunan Nasional “Veteran” Jawa Timur Department of Informaton Technology Faculty of Computer Science 60294 Rungkut Madya Gunung Anyar Surabaya Indonesia

Researchers and experts have developed various techniques, models, and methods to predict the price movements of cryptocurrencies, especially Bitcoin. However, among the many techniques studied in the literature, there is still a lack of focus on mining, creating, and developing datasets with specific patterns for predicting the next cryptocurrency price movement. This is an exciting reason to conduct further research. A web-based Patterned Dataset Application and a Telegram bot were constructed to address this issue. These tools read the price position of each cryptocurrency and predict the next price direction based on the last position indicated by the Patterned Dataset Application. The experiment’s results show that when the Patterned Dataset Application shows a diamond crash position, it is time to make a purchase; conversely, when it shows a diamond moon position, it is time to make a sale. It is hoped that by utilizing the Patterned Dataset Application, potential losses can be minimized, and there is more potential for profit in cryptocurrency trading. Even though the initial data source comes from Indonesia’s most prominent digital cryptocurrency trading market, according to coinmarketcap, namely Indodax, the results of this patterned dataset application can often describe the same cryptocurrency conditions globally. The novelty of this research is to produce a new way of predicting the next cryptocurrency price movement using patterned datasets. At the end of this paper, it will be proven that hypothesis 1 and hypothesis 2 on the results of the patterned dataset are true.

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Prediction of traffic congestion based on time series dataset number of vehicles using neural network algorithm

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AIP Conference Proceedings 2024年第1期2982卷

作者： Prasetyo Wibowo Yunanto Rahmat Gernowo Oky Dwi Nurhayati Doctoral Program of Information System Diponegoro University Jl. Imam Bardjo SH No.5 Semarang Indonesia Information Systems and Technology Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta Indonesia Department of Physics Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia Computer Engineering Department Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia

The increasing number of vehicles that was not accompanied by an adequate road infrastructure readiness causes traffic jams. Traffic jams often occur repeatedly every day, especially at certain times, such as when people are going to and from work. The same traffic jams can also occur at the beginning or the end of the week; this usually repeats every week. As a result, if the congestion dataset is known, the repeating traffic congestion for daily and weekly congestion can be anticipated. In this study, traffic congestion predictions are modeled using the Neural Network Algorithm based on traffic congestion data collected within 24 hours for one to two weeks. The parameters used in optimizing Neural Network performance are learning rate, momentum, and epochs (training cycles). Based on these experiments, the Neural Network Algorithm successfully modeled traffic density patterns which are recurring congestion patterns, with quite good results.

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