Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

This study explores the use of a Siamese neural network architecture to enhance classification performance in few-shot learning scenarios, with a focus on bovine facial recognition. Traditional methodologies often require large datasets, which can significantly stress animals during data collection. In contrast, the proposed method aims to reduce the number of images needed, thereby minimizing animal stress. Systematic experiments conducted on datasets representing both full and few-shot learning scenarios revealed that the Siamese network consistently outperforms traditional models, such as ResNet101. It achieved notable improvements, with mean values increasing by over 6.5% and standard deviations decreasing by at least 0.010 compared to the ResNet101 baseline. These results highlight the Siamese network’s robustness and consistency, even in resource-constrained environments, and suggest that it offers a promising solution for enhancing model performance with fewer data and reduced animal stress, despite its slower training speed.

In recent years, healthcare environments have witnessed an increase in incidents of violence, often attributed to dissatisfaction with the quality of services provided or with the manner in which care is delivered during healthcare assistance. In the quest to find a theoretical basis for this observation, a systematic review was conducted over the past 5 years, and the literature indicates that this phenomenon is particularly pronounced in the aggression directed towards nurses and physicians. Regardless of geographical location, there is a higher prevalence of violent incidents in environments where interaction and collaboration between patients and healthcare professionals are essential. These aggressive and violent behaviors encompass a variety of deliberate actions that are often misconstrued and perceived as entitlements by patients in a state of illness. Although physical violence is conspicuous due to the visible marks it leaves, psychological aggression is the most commonly reported form of violence by nurses and doctors. Unfortunately, verbal violence often goes unnoticed and is inadequately quantified. Continuous exposure to violence poses a significant threat and risk factor to all healthcare professionals, making it essential to address and mitigate this issue to ensure a safe and healthy working environment for all involved in healthcare delivery.

Graphene aerogel is an ultralight material with outstanding thermal, electrical, and mechanical properties, which positions it as a promising candidate for advanced aerospace engineering applications. Recent advances in additive manufacturing, including three-dimensional printing and bio-inspired fabrication strategies, have enabled the design of architected graphene aerogel structures that draw on hierarchical and cellular architectures found in natural systems. By translating biological structural principles into engineered materials, bio-inspired design allows precise control over porosity, load transfer, and functional grading, thereby enhancing strength-to-weight ratios, energy dissipation, and multifunctional integration, all of which are critical requirements for aerospace structures. This review examines the convergence of graphene aerogel research with these advanced fabrication approaches, highlighting their implications for structural efficiency, weight reduction, and the integration of sensing and electromagnetic interference (EMI) shielding functionalities. Despite persistent challenges related to mechanical fragility under extreme loading conditions, energy-intensive production routes, and scalability constraints, reported properties such as ultra-low densities as low as 3.13 mg/cm ³ and EMI shielding effectiveness of up to 87 dB at a thickness of 2.0 mm underscore the technological relevance of graphene aerogels. By synthesizing current literature and identifying key knowledge gaps, this work outlines critical research pathways grounded in bio-inspired design principles to advance the application of graphene aerogels in future aerospace structures.

This study, aimed at professionals in research and development in the fields of computer vision, artificial intelligence, and intelligent transportation, presents a systematic literature review on recent machine learning methodologies applied to the detection and tracking of vehicles, pedestrians, and traffic flow. The analysis of articles published between 2022 and 2025 (early access) in the post-COVID era explored the integration of machine learning and deep learning to address traffic challenges, allowing for the comparison of different approaches and the formulation of hypotheses based on the 46 articles that comprised the review corpus. Furthermore, the evaluation of the reported metrics revealed inconsistencies in the methodologies employed, attributed to the lack of standardization across the studies. In light of this, this work proposes alternatives for future experiments, emphasizing the emerging potential of the field through the adoption of new standardization systems and the exploration of experimental combinations.