Hasil untuk "Information resources (General)"

J. Shapiro

Mengchi Liu, D. Yu

The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning.

J. B. Baxter, G. Guglietta

Xiaolong Xu, Haoyuan Li, Weijie Xu et al.

The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

DuckJung Shin, A. Konrad

Qingkai Liu, Haitao Jing, Xueying Wen et al.

Accurate, near real-time soybean phenology information is critical for crop management and breeding. Previous approaches relying on satellite remote sensing time-series data suffer from temporal delays, limiting their usefulness for in-season decision-making. To overcome this limitation, this study reframes phenology identification as a near real-time classification task using single-timepoint Unmanned Aerial Vehicle (UAV) imagery collected from 420 soybean germplasm resources across three experimental sites, and proposes an innovative multi-modal dynamic Gating Fusion Model that integrates two optimized pathways. one based on machine learning (ML) and the other on deep learning (DP). In the ML branch, systematic benchmarking of tabular-feature models identified the Soft Voting ensemble as the best classifier. In the DL branch, an enhanced BC-ConvNeXt model equipped with BiFPN and CBAM modules was developed to strengthen visual feature extraction. Building on these two optimal classifiers, the dynamic gating fusion model achieved the highest F1-score of 94.3% across seven key growth stages (V1, V2, R1, R2, R6, R7, R8). This result represents a significant improvement of 1.5% and 10.6% over the best performing ML and DL models, respectively. The superior performance arises from the intelligent arbitration of complementary strengths, with gating-weight analysis revealing a strategy that prioritizes ML predictions while leveraging DL for error correction. This work establishes a complete framework for near real-time crop phenology detection and demonstrates the strong potential of intelligent multi-modal fusion in high-throughput phenotyping.

Lawrence H. Yang, L. Y. Wong, Margaux M. Grivel et al.

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.

Eliott Lumet, Thomas Jaravel, Mélanie C. Rochoux

By 2050, two-thirds of the world's population will live in urban areas under climate change, exacerbating the environmental and public health risks associated with poor air quality and urban heat island effects. Assessing these risks requires the development of microscale meteorological models that quickly and accurately predict wind velocity and pollutant concentration with high resolution, as the heterogeneity of urban environments leads to complex wind patterns and strong pollutant concentration gradients. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to address this challenge by providing obstacle-resolved flow and dispersion predictions. However, CFD models are very expensive and require intensive computing resources, which can hinder their systematic use in practical engineering applications. They are also subject to significant uncertainties, particularly those arising from the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, some of which are aleatory and thereby irreducible. Given these issues, the construction of CFD datasets that account for uncertainty would be an interesting avenue of research for microscale atmospheric science.In this context, we present the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which consists of 200 large-eddy simulations (LES) characterizing the complex interactions between the turbulent airflow, the tracer dispersion, and an idealized urban environment. These simulations reproduce the canonical MUST dispersion field campaign while perturbing the model's mesoscale meteorological forcing parameters. PPMLES includes time series at human height within the built environment to track wind velocity and pollutant release and dispersion over time. PPMLES also includes complete 3-D fields of first- and second-order temporal statistics of the wind velocity and pollutant concentration, with a sub-metric resolution. The uncertainty of the fields induced by the internal variability of the atmospheric boundary layer is also provided. The computation of PPMLES required significant resources, consuming 6 million CPU core hours, equivalent to the emission of approximately 10 tCO2eq of greenhouse gases. This significant computational effort and associated carbon footprint motivates the sharing of the data generated.The added value of the PPMLES dataset is twofold. First, the perturbed-parameter ensemble of LES enables to quantify and understand the effects of the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, which has been identified as a major challenge in predicting atmospheric flow and pollutant dispersion in urban environments. Secondly, PPMLES reference data can be used to benchmark models of different levels of complexity, and to extract key information about the physical processes involved to inform more operational modeling approaches, for example through learning surrogate models.

Arın CELAYIR, Musa Batuhan YOLCU, Bedri KARAISMAILOĞLU et al.

Since the development of ChatGPT, many areas are explored about its use and its potentials. Medicine in general and especially medical education is one of these promising areas. This study aims to investigate the capabilities of artificial intelligence technologies, focusing specifically on ChatGPT and emerging text-to-video features, in the development of educational materials for orthopedic medicine. The study is structured into steps, where the first focus is on their application in generating content related to shoulder examination techniques, evaluating the accuracy and effectiveness of these artificial intelligence–generated materials. The primary research question examines whether artificial intelligence–generated educational tools can serve as reliable and accessible resources for these purpose. ChatGPT and other artificial intelligence tools were utilized with simple prompts to generate detailed descriptions of shoulder examination techniques. These texts were reviewed by orthopedic specialists for accuracy. Visual aids based on the artificial intelligence–generated content were created and assessed for anatomical correctness. Additionally, these visuals were converted into short videos using the Vidful.ai platform to assess the feasibility and effectiveness of incorporating dynamic content into medical education. The artificial intelligence–generated texts were comprehensive and showed promise as educational materials. However, the visuals derived from these texts exhibited deficiencies in anatomical accuracy. The attempt to transform these visuals into short videos using Vidful.ai demonstrated limited success, highlighting challenges in producing dynamic and precise content from artificial intelligence–generated visuals. Artificial intelligence–supported tools can offer an accessible and innovative approach to medical education but require expert oversight to ensure content accuracy and effectiveness. This study suggests that leveraging broader and higher-quality datasets could enhance the quality of artificial intelligence–generated educational materials. As artificial intelligence technologies continue to evolve, their role in medical education and patient information dissemination is expected to expand, potentially establishing them as effective and widely adopted tools. Cite this article as: Celayir, A., Yolcu, M. B., Karaismailoğlu, B., R., & Celayir, S. (2025). First experiences in creating orthopedic  medical education content using ChatGPT and similar AI tools. HAYEF: Journal of Education, 22, 0005, doi:10.5152/ hayef.2025.25005.

Stephen P. Boyd, Arpita Ghosh, B. Prabhakar et al.

Clizia Cincidda, Clizia Cincidda, Silvia Francesca Maria Pizzoli et al.

IntroductionProstate cancer patients (PCP) often struggle with a significant emotional, physical, and social burden during the care-flow pathway. Noteworthy, PCP should not be considered a standalone patient, but someone who is connected with a relevant social environment and that is usually supported by a beloved one, the caregiver. The involvement of the caregivers through the care pathway might bring significant benefits both on the psychological and the treatment and decision-making side. The present pilot study aimed at preliminarily assessing quantitatively the psychological impact of a prostate cancer diagnosis on the degree of agreement of PCPs and their caregivers on medical decisions, coping resources and psychological distress levels.Methods16 PCP and their caregivers were enrolled in the study and fulfilled a battery of standardized questionnaires.ResultsResults showed low concordance in decision making styles and preferences in patients and their caregivers and that the dyads showed similar depression symptoms levels. Relevant features of the psychological needs of the analyzed dyads, such as need for information and support, also emerged.ConclusionOn the basis of these preliminary results, guidelines for the construction of tailored brief psychological support interventions for PCP dyads are provided.

G. Macones

Raymart O. Basco

Research is one of the trifocal functions of higher education in the Philippines. It is further cascaded to undergraduate research to ensure that undergraduates in collaboration with their mentor contribute intellectual discoveries or creative output relative to their discipline through research. This phenomenological research was undertaken to highlight the lived experiences of preservice teachers on their research course. To achieve this purpose, twenty-four preservice teachers enrolled in the course Research in Elementary Education A.Y. 2020-2021 were interviewed through online video conferencing. Based on the accounts of the participants, partnership, interaction, guidance, and fulfillment were among the four significant experiences in the course. It was further highlighted that challenges encompassed internet connection, writing the literature review, gathering data, and time constraints. Resources, hardware, support system, and time management were found to be the best practices of the participants in terms of addressing the identified challenges in the course. In terms of the necessary skills and attitude to be successful in the course, participants emphasized the need for communication, teaming and collaboration, information, media, and technology literacy, flexibility and adaptability, critical thinking, and time management skills. Also, motivation and perseverance were among the significant attitudes to be developed in taking the course.

Rafi Helmi Rabani, Prijana Prijana

Problems in libraries can be overcome one way by collaborating between libraries. Collaboration can be carried out by any type of library, including state university libraries. College libraries have the same functions as libraries in general and the functions included in the Tri Dharma of Higher Education include education, research and community service. The development of library resources at the Central Library of the Indonesian Education University is carried out in collaboration with the Salatiga State Islamic Institute Library. This research aims to find out what cooperation looks like, how cooperation is carried out, and what are the challenges in carrying out collaboration between the two libraries. The research method used is a qualitative research method and data collection techniques in the form of interviews with the Head of the Library Services Division of the UPI Central Library. The research results show that the cooperation carried out is cooperation in the field of information services and is bound by a memorandum of agreement with six scopes of cooperation. Several challenges were found, namely in terms of busyness of library managers, permission from the head of the library, and budget in carrying out collaboration. There are several scopes that cannot be realized in the implementation of cooperation. This cannot be used as a barrier to working together, but is a motivation for both parties because the priority is service to users.

A. Zohdy, G. Eaton, D. Mabey

Guan Yang, Chao Li, Xiaojun Liu et al.

A terahertz (THz) passive imager with automatic target detection is an effective solution in the field of security inspection. The high-quality training datasets always play a key role in the high-precision target detection applications. However, due to the difficulty of passive image data acquisition and the lack of public dataset resources, the high-quality training datasets are often insufficient. The generative adversarial network (GAN) is an effective method for data augmentation. To enrich the dataset with the generated images, it is necessary to ensure that the generated images have high quality, good diversity, and correct category information. In this paper, a GAN-based generation model is proposed to generate terahertz passive images. By applying different residual connection structures in the generator and discriminator, the models have strong feature extracting ability. Additionally, the Wasserstein loss function with gradient penalty is used to maintain training stability. The self-developed 0.2 THz band passive imager is used to carry out imaging experiments, and the imaging results are collected as a dataset to verify the proposed method. Finally, a quality evaluation method suitable for THz passive image generation task is proposed, and classification tests are performed on the generated images. The results show that the proposed method can provide high-quality images as supplementary.

Benito Bisso Schmidt, Rubens Mascarenhas Neto

This article focuses on Red Latinoamericana de Archivos, Museos, Acervos y Investigadores LGBTQIA+ (AMAI LGBTQIA+), a network composed of researchers and institutions related to LGBTQIA+ memory in Latin America, founded in 2019. First, the authors analyse the network’s creation arising from the discontent of some participants of the June 2019 Archives, Libraries, Museums and Special Collections (ALMS) Conference, in Berlin, who felt bothered by the lack of attention given to subaltern perspectives on LGBTQIA+ history and memory. Next, the authors describe and analyse the network’s first year of activities communicated through its Facebook group. Multiple challenges arose from creating a network with members from different national origins, languages, and identities, especially considering the conservative political contexts of several Latin American countries and the social distancing measures imposed by the COVID-19 pandemic. Next, the authors present a general profile of the network’s members and a map of partner institutions. Finally, the article points out some challenges to the network’s continuity and its desire to render Latin America more visible in the broader panorama of global LGBTQIA+ history. The authors conclude by highlighting the importance of AMAI LGBTQIA+ in stimulating further discussions about the participation of global-south researchers and perspectives on global queer history initiatives.

Oksana Z. KLYMENKO, Olena L. SOKUR

Objective. To represent library communication in the socio-cultural space of modern society. Methods. The methodology of the article was based on a set of general and special methods of scientific knowledge. Results. We substantiated the essence and content of the term "library communication" as a pre-planned activity in the socio-cultural space of modern society. There are presented the structural content elements of library communication:, goal, objective, essence, driving mechanism, purpose, implementation, basic tools, main communication product, indifferent components, sources, environment and specifics of functioning, levels and result. We established that library practice today is constantly enriched with new phenomena and concepts to denote them, for which library science as a science should formulate appropriate terms, provide theoretical and methodological justification. Conclusions. It is argued that in the context of the theory of social communications, the institutionalization of the term "library communication" as a separate type of professional communication and the driving force of the transformation of the library business is relevant.

I. Sfiligoi, D. Bradley, B. Holzman et al.