In material physics, characterization techniques are foremost crucial for obtaining the materials data regarding the physical properties as well as structural, electronics, magnetic, optic, dielectric, and spectroscopic characteristics. However, for many materials, ensuring availability and safe accessibility is not always easy and fully warranted. Moreover, the use of modeling and simulation techniques need a lot of theoretical knowledge, in addition of being associated to costly computation time and a great complexity deal. Thus, analyzing materials with different techniques for multiple samples simultaneously, still be very challenging for engineers and researchers. It is worth noting that although of being very risky, X-ray diffraction is the well known and widely used characterization technique which gathers data from structural properties of crystalline 1d, 2d or 3d materials. We propose in this paper, a Smart GRU for Gated Recurrent Unit model to forcast structural characteristics or properties of thin films of tin oxide SnO$_2$(110). Indeed, thin films samples are elaborated and managed experimentally and the collected data dictionary is then used to generate an AI -- Artificial Intelligence -- GRU model for the thin films of tin oxide SnO$_2$(110) structural property characterization.

This study explores the development of a hybrid deep convolutional neural network (DCNN) model enhanced by autoencoders and data augmentation techniques to predict critical heat flux (CHF) with high accuracy. By augmenting the original input features using three different autoencoder configurations, the model's predictive capabilities were significantly improved. The hybrid models were trained and tested on a dataset of 7225 samples, with performance metrics including the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), and normalized root-mean-squared error (NRMSE) used for evaluation. Among the tested models, the DCNN_3F-A2 configuration demonstrated the highest accuracy, achieving an R2 of 0.9908 during training and 0.9826 during testing, outperforming the base model and other augmented versions. These results suggest that the proposed hybrid approach, combining deep learning with feature augmentation, offers a robust solution for CHF prediction, with the potential to generalize across a wider range of conditions.

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements.

Digital Twins have gained attention in various industries for simulation, monitoring, and decision-making, relying on ever-improving machine learning models. However, agricultural Digital Twin implementations are limited compared to other industries. Meanwhile, machine learning, particularly reinforcement learning, has shown potential in agricultural applications like optimizing decision-making, task automation, and resource management. A key aspect of Digital Twins is representing physical assets or systems in a virtual environment, which aligns well with reinforcement learning's need for environment representations to learn the best policy for a task. Reinforcement learning in agriculture can thus enable various Digital Twin applications in agricultural domains. This review aims to categorize existing research employing reinforcement learning in agricultural settings by application domains like robotics, greenhouse management, irrigation systems, and crop management, identifying potential future areas for reinforcement learning-based Digital Twins. It also categorizes the reinforcement learning techniques used, including tabular methods, Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic algorithms, to overview currently employed models. The review seeks to provide insights into the state-of-the-art in integrating Digital Twins and reinforcement learning in agriculture, identifying gaps and opportunities for future research, and exploring synergies to tackle agricultural challenges and optimize farming, paving the way for more efficient and sustainable farming methodologies.

Writing a scientific article is a challenging task as it is a highly codified and specific genre, consequently proficiency in written communication is essential for effectively conveying research findings and ideas. In this article, we propose an original textual resource on the revision step of the writing process of scientific articles. This new dataset, called CASIMIR, contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. Pairs of consecutive versions of an article are aligned at sentence-level while keeping paragraph location information as metadata for supporting future revision studies at the discourse level. Each pair of revised sentences is enriched with automatically extracted edits and associated revision intention. To assess the initial quality on the dataset, we conducted a qualitative study of several state-of-the-art text revision approaches and compared various evaluation metrics. Our experiments led us to question the relevance of the current evaluation methods for the text revision task.

The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AIbased approaches, especially those employing transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artifcial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of transformers in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research feld.

The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and real-time data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios.

One of the most important problems in computer vision and remote sensing is object detection, which identifies particular categories of diverse things in pictures. Two crucial data sources for public security are the thermal infrared (TIR) remote sensing multi-scenario photos and videos produced by unmanned aerial vehicles (UAVs). Due to the small scale of the target, complex scene information, low resolution relative to the viewable videos, and dearth of publicly available labeled datasets and training models, their object detection procedure is still difficult. A UAV TIR object detection framework for pictures and videos is suggested in this study. The Forward-looking Infrared (FLIR) cameras used to gather ground-based TIR photos and videos are used to create the ``You Only Look Once'' (YOLO) model, which is based on CNN architecture. Results indicated that in the validating task, detecting human object had an average precision at IOU (Intersection over Union) = 0.5, which was 72.5\%, using YOLOv7 (YOLO version 7) state of the art model \cite{1}, while the detection speed around 161 frames per second (FPS/second). The usefulness of the YOLO architecture is demonstrated in the application, which evaluates the cross-detection performance of people in UAV TIR videos under a YOLOv7 model in terms of the various UAVs' observation angles. The qualitative and quantitative evaluation of object detection from TIR pictures and videos using deep-learning models is supported favorably by this work.

In this paper, we present a novel person reidentification (PRe-ID) system that based on tensor feature representation and multilinear subspace learning. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA) algorithm is used for multilinear subspace learning, which models the data in a tensor framework to enhance discriminative capabilities. Similarity measure based on Mahalanobis distance is used for matching between training and test pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets demonstrate the effectiveness of our method. Introduction In the past few years, artificial intelligence has sparked a transformative revolution across multiple domains, significantly impacting people's lives.

Retinex (MSR), which enhances image quality. MSIDA typically performs the projection of the input region tensor into a novel multilinear The objective of kinship verification from face images is to subspace, which results in an increased distance between ascertain the biological relationship between two individuals samples belonging to different classes and a decreased distance by analyzing their faces appearances [1].