As digital technology has advanced, the digital divide is of growing concern, with disadvantaged groups with limited access to digital resources and skills disproportionately affected. This divide exacerbates social and educational inequalities, making it increasingly important to understand its scope and implications. While numerous studies have examined digital disparities within specific populations, there has been insufficient comprehensive analysis of research trends. To address this gap, this study systematically reviews trends in research on the digital divide from 2001 to 2024 by focusing on publication trends, research methodologies, research topics, target populations, and the inclusion of disadvantaged groups. This study analyzes academic publications from 2001 to 2024, categorizing research by method, topic, and target population. A frequency analysis was conducted to identify key trends and assess the extent to which disadvantaged groups were included. The findings indicate a sharp increase in digital divide research after 2020, with a growing emphasis on disadvantaged groups. Quantitative and qualitative approaches were used in nearly equal proportions, while studies on awareness and perception dominated. However, impact analysis and intervention studies remain scarce. Elementary and middle school students were the most frequently studied groups, while university students and adults were underrepresented. Among disadvantaged groups, economic factors have been the most studied, while physical and sociocultural factors have received less attention. This study underscores the importance of broader inclusion of disadvantaged populations and a greater emphasis on policy-driven and intervention-based research to bridge the digital divide. By identifying key research trends, this study offers valuable insights for future research and informed policy development in digital inclusion efforts.
Jesús Antonio Mayorquín Robles, Gabriel Antonio López Valencia, José Jesús Rodríguez Senday
et al.
Cuando se trabaja en el desarrollo de diferentes interfaces que funcionan como una sola, es complicado, en ocasiones es poco entendible, en la mayoría de las veces eso pasa debido a que el o los desarrolladores no siguen un proceso metodológico adecuado. En el desarrollo de servicios WEB, APP móviles e inclusive en los sistemas embebidos es posible que se basen en técnicas como MVC o UML, los cuales le dan al desarrollo del proyecto una base científica que puede ser entendida por cualquier persona que realice este tipo de proyectos. El presente trabajo muestra el desarrollo de una aplicación basada en IoT, es decir, un servicio WEB, una APP móvil y un sistema embebido, raspberry pi, que trabajan de manera conjunta y basados en técnicas estructuradas de desarrollo MVC con la finalidad de mostrar que es posible que trabajos con cierto grado de complejidad puedan mostrarse más sencillos. El sistema fue probado en un servicio de préstamo de bicicletas, generando una base de datos de usuarios y haciendo pruebas de manera individual con cada uno.
Abstract In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO2 based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO2. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO2 materials, offering fresh insights into the design and performance prediction of HfO2 ferroelectric thin films.
Materials of engineering and construction. Mechanics of materials, Computer software
Abstract One of the most common forms of audio forgery is copying and moving certain audible segments of audio to other locations in the same audio. The audio features of the pasted regions in such audio forgeries become very dissimilar to the audio features of the copied segments after post-processing. This dissimilarity makes detecting such tampering a major challenge. To address this problem, we propose a robust audio copy-move forgery detection method using a Decreasing Convolutional Kernel Neural Network (DCKNN), data augmentation, and digital fusion. In the proposed algorithm, Mel spectrogram and Hilbert–Huang spectrogram of the audio are extracted, and then they are fused by weighting coefficients, which are gained through extensive experiments. New spectrogram images are generated by weighted fusion, and these spectrogram images are used to train the proposed DCKNN model. The trained DCKNN can effectively detect copy-move forgery. The DCKNN model consists of a combination of four convolutional groups, each with different sensitivities to the two audio categories. We solve the problem of different sensitivities by sequentially lowering the parameters of the convolutional layers in the four convolutional groups, thus obtaining high accuracy in audio classification. The experimental results show that the proposed scheme is robust to most typical post-processing operations, including additive noise, compression, median filtering, resampling, re-quantization, and low-pass filtering, etc al. In addition, our method shows better performance in the detection of forged audio with multiple attacks. Compared to the state-of-the-art algorithms, the proposed algorithm has advantages in terms of accuracy, precision, and F1 score.
Electronic computers. Computer science, Information technology
Non-Line-of-Sight (NLOS) imaging, which combines imaging and computational reconstruction, describes the reconstruction of hidden scenes in a medium by capturing scattered or reflected information without directly imaging the scene. NLOS imaging is still in the early stages of its development, and systematic research methods for scene modeling and target information reconstruction are lacking. To address these issues, an NLOS imaging method for unobstructed and non-self-luminous scenes is proposed. Based on optical radiation theory, the relationship between the imaging of diffuse reflection surfaces in the scene and the shape of hidden objects is analyzed to determine the NLOS imaging model and reconstruction targets. A Diffuse reflection full-Shadow passive NLOS (DS-NLOS) dataset that resembles physical reality is generated by combining a rendering software with the Motion Picture Experts Group 7 (MPEG7) dataset . A passive NLOS Reconstruction network model (Re-NLOS) is constructed using a Visual Transformer (ViT) structure in combination with a Generative Adversarial Network (GAN) to extract global features from captured diffuse reflection surface images and recover the shape of hidden objects. Experimental results on the DS-NLOS dataset demonstrate that this method can recover the shape information of hidden objects from diffusely reflected surfaces. In comparison with the diffuse reflection full-shadow images, the average Peak Signal-to-Noise Ratio (PSNR) for 20 object categories in the present test set is increased by 5.85 dB, and the average Structural SIMilarity (SSIM ) is increased by 0.038 1. This method also demonstrates restore capabilities in real indoor scenes.
Abstract Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review briefly the history of atomic simulations in catalysis and then focus on the recent trend shifting toward ML potential calculations. The advanced methods developed by our group are outlined to illustrate how complex structures and reaction networks can be resolved using the ML potential in combination with efficient global optimization methods. The future of atomic simulation in catalysis is outlooked.
Materials of engineering and construction. Mechanics of materials, Computer software
Elham Monaghesh, Ramin Negahdari, Taha Samad-Soltani
Abstract Background and objective A treatment approach that is widely used as a permanent and natural replacement for missing or extracted teeth is dental implants .VR is a computer-generated simulation that creates a three-dimensional (3D) image or environment. Advances in VR -based learning allow learners and students to practice and also help professionals plan a wide variety of surgical procedures, including the correct placement of dental implants. Therefore, in this systematic review, our aim was to investigate and evaluate the available virtual reality tools for dental implants and their effectiveness. Materials and methods Studies published up to 01/30/2023 which report the applications of using virtual reality technology in dental implants, were reviewed in three databases, including PubMed, Web of Science, and Scopus. All studies with evidence reporting the role of virtual reality technology in the field of dental implants were included in our analyses, written in English and published in peer-reviewed form, are included. Theoretical articles, and letters that did not provide original data, as well as studies that reported incomplete information, were excluded. Two reviewers independently assessed search results, extracted data, and assessed the quality of the included studies, and decisive agreement was reached by discussion and consultation with the third researcher. Narrative synthesis was undertaken to summarize and report the findings. Results Out of 1633 initial search results, nine were included in the present study based on the inclusion criteria. The focus of seven studies was on teaching and learning, and two studies have examined the implant planning procedure. The most commonly used hardware and software were head-mounted display and Unity3D, respectively. In almost all studies, the results showed that the use of virtual reality-based systems improves and enhances the skills of users, including dental students and specialists. Conclusions Our findings showed that VR is an effective method for teaching and planning the implant process. Although the use of VR technology is limited for various reasons such as cost, it can increase the skills of dental professionals in performing dental implants.
Hsiao-Lung Chan, Yuan Ouyang, Cheng-Chou Lai
et al.
Abstract Background Various neurocognitive tests have shown that cycling enhances cognitive performance compared to resting. Event-related potentials (ERPs) elicited by an oddball or flanker task have clarified the impact of dual-task cycling on perception and attention. In this study, we investigate the effect of cycling on cognitive recruitment during tasks that involve not only stimulus identification but also semantic processing and memory retention. Methods We recruited 24 healthy young adults (12 males, 12 females; mean age = 22.71, SD = 1.97 years) to perform three neurocognitive tasks (namely color-word matching, arithmetic calculation, and spatial working memory) at rest and while cycling, employing a within-subject design with rest/cycling counterbalancing. Results The reaction time on the spatial working memory task was faster while cycling than at rest at a level approaching statistical significance. The commission error percentage on the color–word matching task was significantly lower at rest than while cycling. Dual-task cycling while responding to neurocognitive tests elicited the following results: (a) a greater ERP P1 amplitude, delayed P3a latency, less negative N4, and less positivity in the late slow wave (LSW) during color-word matching; (b) a greater P1 amplitude during memory encoding and smaller posterior negativity during memory retention on the spatial working memory task; and (c) a smaller P3 amplitude, followed by a more negative N4 and less LSW positivity during arithmetic calculation. Conclusion The encoding of color-word and spatial information while cycling may have resulted in compensatory visual processing and attention allocation to cope with the additional cycling task load. The dual-task cycling and cognitive performance reduced the demands of semantic processing for color-word matching and the cognitive load associated with temporarily suspending spatial information. While dual-tasking may have required enhanced semantic processing to initiate mental arithmetic, a compensatory decrement was noted during arithmetic calculation. These significant neurocognitive findings demonstrate the effect of cycling on semantic-demand and memory retention-demand tasks.
Bahman Arasteh, Razieh Sadegi, Keyvan Arasteh
et al.
When the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. Software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches' main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently.