Cornelius A James, Sarah L Krein, Sarah Yon
et al.
Abstract
BackgroundMobile health (mHealth) apps are widely available, and some have proven safe and effective for management of specific chronic conditions. Despite a high degree of interest, the potential of these technologies has yet to be realized. Patient and clinician attitudes are key factors that influence the adoption of mHealth apps but remain poorly understood, particularly in the United States.
ObjectiveThis study aimed to identify both patient and clinician attitudes that can influence recommending and adopting mHealth apps.
MethodsUsing well-established technology adoption and implementation science frameworks, this study included a deductive content analysis using a rapid qualitative analytic method. Semistructured interviews were conducted with patients and clinicians to identify technical and material, social and personal, and policy and organizational factors that can influence the recommendation or adoption of mHealth apps. The interviews and data analysis were performed between September 2023 and August 2024.
ResultsParticipants included 20 clinicians (n=12, 60% general internists) with a mean time in practice of 17 (SD 11.6) years, and 28 patients with a mean age of 59 (SD 12.1) years. A total of 7 categories related to patients’ and clinicians’ attitudes toward mHealth apps emerged: (1) apps as tools to improve health by extending care, (2) the role of apps in enhancing the patient–clinician relationship, (3) the need for simplicity and efficiency in app design, (4) the influence of prior experience with mHealth apps, (5) comfort with technology, (6) recommendations from trusted sources, and (7) education and hands-on experience. Although similar factors were considered by patients and clinicians, their views about older adults’ interest and ability to use mHealth apps differed.
ConclusionsUnderstanding patient and clinician views about mHealth apps provides critical insights for developing approaches to facilitate their use. These findings suggest patients and clinicians share similar views about the benefits of mHealth apps. Nonetheless, clinicians’ perceptions about older patients’ interest and ability to use mHealth apps may negatively impact recommendation of mHealth apps and subsequent adoption by older adults.
Information technology, Public aspects of medicine
Henrique Rodrigues Lelis, Daniel Jardim Pardini, Eloy Pereira Lemos Junior
Compliance programs have legal, administrative and technological attributes that help organizations find solutions related to strategy, management and organizational governance. In turn, blockchain has been described as a digital system with potential for use in numerous activities, as any activity whose function is to protect and transfer digital assets can be impacted by the system. However, there are criticisms and reservations regarding its adoption by organizations, especially regarding issues related to the regulatory framework, corporate governance and technological management. From this perspective, it becomes relevant to relate the attributes of compliance programs to the development of blockchain in the organizational dimension, which is the proposal of this research. The gap explored with this research is to describe the implications that the attributes of compliance programs can bring to the development of blockchain technology, in the context of organizations. To explore the topic, a panel of experts and a Delphi round were created to structure a survey that sought evidence that demonstrates the existence or not of contributions from compliance programs to the development of the blockchain. This article presents the results relating to the organizational dimension of the doctoral thesis “Attributes of Compliance Programs for the blockchain, in the context of the Dimensions of the State, Organization and Individual”, defended by the first author, in the Doctoral program in Information and Management Systems of Knowledge at Universidade Fumec, with UNIVERSIDADE FUMEC and FAPEMIG as funding institutions.
Social sciences (General), Bibliography. Library science. Information resources
Recommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role in e-commerce applications as well, with industry players like Amazon, AliBaba, eBay using recommender systems within their ecosystems to give suitable and value-driven insights. However, recommender systems face some main concerns such as data sparsity, cold-start problems and so on. As a result, research is currently ongoing to solve these issues and provide high-quality recommendations to consumers. This review aims to identify prevailing gaps surrounding these issues by analysing existing research on generative Artificial Intelligence (AI) recommender systems within an e-commerce context. It explores the underlying framework of common generative AI techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, diffusion models and so on. VAEs and Transformers hold great potential within e-commerce as noted by most researchers due to their ease of training and qualitative generations. This review intends to enhance recommender systems better to improve the quality of life of digital users, providing better recommendations in e-commerce as well as maximizing the value of stakeholders. It also includes potential future work for researchers to advance existing knowledge in this sector.
Electronic computers. Computer science, Information technology
The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into the functionalities of existing LLMs designed for healthcare applications and elucidates the trajectory of their development, starting with traditional Pretrained Language Models (PLMs) and then moving to the present state of LLMs in the healthcare sector. First, we explore the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications, particularly focusing on clinical language understanding tasks. These tasks encompass a wide spectrum, ranging from named entity recognition and relation extraction to natural language inference, multimodal medical applications, document classification, and question-answering. Additionally, we conduct an extensive comparison of the most recent state-of-the-art LLMs in the healthcare domain, while also assessing the utilization of various open-source LLMs and highlighting their significance in healthcare applications. Furthermore, we present the essential performance metrics employed to evaluate LLMs in the biomedical domain, shedding light on their effectiveness and limitations. Finally, we summarize the prominent challenges and constraints faced by large language models in the healthcare sector by offering a holistic perspective on their potential benefits and shortcomings. This review provides a comprehensive exploration of the current landscape of LLMs in healthcare, addressing their role in transforming medical applications and the areas that warrant further research and development.
Anna S. Kolosova, Anna S. Kameneva, Georgii G. Chukov
et al.
The major algorithms and methods of machine learning are considered. A possibility of machine learning and neural network using for electronic equipment quality prediction is assessed. The paper provides examples of the successful application of these algorithms to improve such quality of electronic components indicators as reliability, resistance to external influencing factors, etc. Before testing electronic components on resistance to external influencing factors it is necessary to identify samples of electronic components by fluoroscopy in order to identify possible heterogeneity in the structure of samples belonging to the same batch. A solution of the electronic components batches uniformity problem using computer vision and clustering algorithms is proposed.
Machine learning digunakan untuk menganalisis, mengklasifikasikan, atau memprediksi data. Untuk melakukan tugas dari machine learning diperlukan alat bantu dengan kinerja serta lingkungan yang kuat demi mendapatkan akurasi dan efisiensi waktu yang baik. MLlib Apache Spark adalah library machine learning yang memiliki kemampuan dan kecepatan yang sangat baik. Hal ini dikarenakan dalam melakukan pemrosesan data, MLlib berjalan di atas memori. Penelitian ini menggunakan MLlib Apache Spark untuk melakukan klasifikasi berita palsu berbahasa Indonesia dengan jumlah data sebanyak 1786 yang diperoleh dari situs penyedia berita palsu dan fakta, yaitu TurnBackHoax.id. Algoritma klasifikasi yang diterapkan adalah Naïve Bayes, Gradient-Boosted Tree, SVM dan Logistic Regression. Keempat algoritma dipilih karena kemampuannya yang sudah terbukti baik dalam melakukan klasifikasi dan beberapa algoritma yang jarang digunakan namun memiliki kemampuan yang baik juga dalam hal klasifikasi. Tahap pengolahan data diantaranya adalah preprocessing, feature extraction, penerapan algoritma. Evaluasi dilakukan berdasarkan accuracy, test error, f1-score, confusion matrix, dan running time. Hasil menunjukkan bahwa MLlib Apache Spark terbukti memiliki kinerja yang cepat dan baik karena dalam melakukan pemrosesan machine learning, running time tercepat yang didapat adalah 6.46 detik dengan menggunakan algoritma Logistic Regression. Akurasi yang didapat juga cukup baik dengan rata-rata test error dari keempat algoritma hanya 0.180. F1-score yang diperoleh pada keempat algoritma juga cukup baik dengan rata-rata sebesar 0.818. Confusion matrix yang dihasilkan juga baik, karena jumlah prediksi benar jauh lebih banyak dibandingkan dengan jumlah yang salah.
Abstract
Machine learning is used to analyze, classify, or predict data. To do the task of machine learning, we need tools with a strong performance and environment to get good accuracy and time efficiency. MLlib Apache Spark is a machine learning library that has excellent capabilities and speed. This is because in performing data processing, MLlib runs on memory. This research uses MLlib Apache Spark to classify fake news in Indonesian language with 1786 data that were obtained from fake news and fact provider sites, TurnBackHoax.id. The classification algorithm applied was Naïve Bayes, Gradient-Boosted Tree, SVM and Logistic Regression. The four algorithms were chosen because of their proven ability to classify and several algorithms that are rarely used but have good abilities in terms of classification. Data processing stages include preprocessing, feature extraction, and algorithm implementation. Evaluation was done based on accuracy, error test, f1-score, confusion matrix, and running time. The results showed that MLlib Apache Spark was proven to have a fast and good performance because in doing machine learning processing, the fastest running time was 6.46 seconds using the Logistic Regression algorithm. The accuracy obtained was also quite good with an average test error of the four algorithms of only 0.180. F1-scores obtained on the four algorithms were also quite good with an average of 0.818. The result of confusion matrix was also good, because the number of correct predictions was far more than the number of incorrect ones.
Ivan S. Blekanov, Nikita Tarasov, Svetlana S. Bodrunova
Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the <i>Charlie Hebdo</i> massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.
Sumalee Ngeoywijit, Tawamin Kruasom, KiengKwan Ugsornwongand
et al.
ABSTRACT: One of the industries with the fastest growth rates worldwide, and notably in Thailand, is medical tourism. With connections to Cambodia and Laos, Ubon Ratchathani is located in lower northeastern Thailand, close to Vietnam and Myanmar. Therefore, there is a significant chance that this region will welcome medical travelers. High-quality medical facilities are available in Ubon Ratchathani to fulfill the needs of medical tourists. A visitor’s decision to travel to Ubon Ratchathani for medical treatment is influenced by factors other than the high-level medical facilities, such as lodging, accessibility to public transportation, and tourist attractions. The public transportation services in Ubon Ratchathani, especially the public bus system, are poorly designed and may let down visitors. The purpose of this study is to develop a smart public bus route design that will meet tourists’ demands. The concept of open innovation will be utilized to develop the model. We surveyed 400 visitors to Ubon Ratchathani. The tourists’ opinions and views of public transportation will be made public and used as an input parameter when designing bus routes. The bus route can then be constructed using the differential evolution algorithm (DE). A web-based smart public transportation system was built. In order to construct an efficient smart public bus system (SPBS), open innovation was used in the development phase. According to the computational results, the new routes using DE lead to a 5.97% reduction in travel distance when compared to the output of the more well-known genetic method. More than 98.5% of visitors are satisfied with the new routes, and once they start running, 99.5% of all respondents plan to use public transit.
Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo-handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal port operations where demand is time dependent. A resource profile simulation is run to generate random events across the terminals and estimate the capacity requirement in the form of workload distributions on port resources over time-shifts. The effects on workload requirement, arising from multiterminal cooperation, are subsequently evaluated in consideration of different container flows among terminals. Experimental results suggest that higher transferring rate between terminals will reduce the QC intensity and storage space requirements but increase gate congestion. Variabilities in the QC intensity and storage space requirements also increase due to shorter stays and more movements in container inventory at the yard. The interaction effect between transferring and trans-shipment rates further shows that the average resource requirements for a terminal can be greatly reduced when the interterminal transferring of containers contributes positively to a more even workload redistribution across terminals. The most significant improvements occur when trans-shipment rate is 85% and transferring rate is 75% for QC intensity; trans-shipment rate is 90% and transferring rate is 60% for storage capacity; and trans-shipment rate is 80% and transferring rate is 75% for gate congestion.
Cristhian David Chicaiza Ortiz, Vanessa Pamela Navarrete Villa, Christian Orlando Camacho López
et al.
In Latin America and the Caribbean, the suitable processing of waste through the use of landfills is around 55%, while the local governments with municipal solid waste (MSW) management plans are around 20%. In Quito, for instance, approximately 2000 ton/day of solid waste are collected, and disposed in El Inga Landfill. The objective of this study is evaluating the MSW management of Quito through Life Cycle Assessment (LCA) approach. For achieving this goal, the ISO 14040 methodology was followed and SimaPro 8.4 was used as analysis software. The functional unit used is 1 ton of MSW, while, the material of study was the waste generated in households, commercial sector, schools and markets; whose values were obtained by the public companies EMASEO-EP, EMGIRS-EP, as well as from the open-access data of the city. The results show that using of biogas from the landfill allows the maximum saving of greenhouse gases (GHG) emissions. Therefore, the biogas plant is the process with less environmental impact. The compaction and transportation of MSW displayed a slightly higher impact comparing with the previous process, presenting the second-best environmental performance. On the other hand, the leachate treatment shows the greatest environmental impact according to the model, despite of the effluents does not exceed the permissible limits of the environmental Ecuadorian legislation. The researchers consider suitable the analysis of composting and anaerobic digestion techniques as complementary options to reduce this environmental impact, due to the high organic fraction in the analyzed waste
Carlos Pais-Montes, María Jesús Freire-Seoane, Beatriz López-Bermúdez
Examination of the transition of successful students from university into the labour market reveals a significant gap between the competencies acquired during higher education and those needed in the workplace. In this article, the authors discuss a research study undertaken to analyse the employability traits of engineers who had recently graduated. In order to do this, an estimation was made of both acquired and applied competencies in each area of engineering examined (computer engineers, naval engineers and industrial engineers). This analysis enabled the calculation of a choice model to clarify which competencies act as a stimulus for employment and which discourage recruitment. The results provide clear policy directions both for management teams in each of the three areas of engineering and for universities.
By analyzing the necessity and training objectives of high-quality innovative talents training, this paper expounds the restriction of traditional education on the cultivation of high-quality innovative talents. On the basis of analyzing the relationship between inquiry and experiential mixed teaching method and high-quality innovative talents, it shows that inquiry and experiential teaching methods is an effective way to train high-quality innovative talents. On the basis of analyzing the connotation of inquiry and experiential mixed teaching method, the applicable environment of this teaching mode is expounded. The theory and method of human resource management is one of the important contents that constitute the integrity of industrial engineering disciplines. The course of human resource management in industrial enterprises is of great significance to the cultivation of innovative thinking of students majoring in industrial engineering in higher education. Taking the teaching method of human resource management in industrial enterprises as an example, this paper expounds the application process of inquiry and experiential mixed teaching method.
Students who want to address the “Techno-Economics” (TE) scientific community in their final assignment (i.e. master, diploma or doctoral thesis) are confronted with the specific challenges that interdisciplinary research poses. Hence, to help them respond to these challenges, this paper aims to draft a reference frame that provides orientation in this interdisciplinary research and teaching setting at the interface of “applied natural sciences” (in terms of engineering science resp. technology) and “applied social sciences” (in terms of business economics), without limiting freedom of thought within the research process. The presented TE teaching and research reference frame is primarily based on the needs of Austrian students of industrial engineering and management (IEM). It intends to enable both IEM students and researchers, first, to identify the relevant TE scientific community and, second, to become active within this scientific community with an adequate line of research.
Pengolahan hasil evalusai siswa SMP Negeri Satu Atap Kuala Sebatu saat ini masih dirasakan belum akurat, data-data tersimpan secara terpisah dan perlu melaukan pengumpulan data ulang, sehingga fasilitas sistem tidak efektif dan efisien. Hal ini mendorong penulis untuk menganilisis dan merancang sistem informasi evaluasi siswa agar penyajian informasi bisa lebih cepat, tepat, ekonomis, relevan dan akurat. Analisis sitem dilakukan dengan menggunakan metode PIECES, sedangkan dalam perancangan menggunakan diagram konteks, DFD, ERD, dan Flowchart. Hasil analisis dan perancangan ini dapat mempermudah dalam memperoleh data siswa, data guru, data mata pelajaran dan hasil evaluasi dengan baik dan benar serta inofatif.
Kata Kunci : Pengolahan data, Database MYSQL