Background: Analyzing gait deviation is one of the crucial factors during the diagnosis and treatment of children with Cerebral Palsy (CP). The typical diagnostic procedure requires an expensive and complicated three-dimensional gait analysis system based on visual sensors. In this work, we focus on predicting well-known gait pathology scores using only information collected from the BS4P, the affordable isokinetic dynamometer. Using such equipment, it is possible to determine gait pathological indices such as the gait deviation index (GDI) or the Gillette gait index (GGI). Research question: Are there correlations between the results of examining patients with CP on the Biodex Pro 4 device and the gait quality metrics (GDI and GGI)? Methods: The isokinetic data acquired from biometric sensors (74 records) were analyzed using big data methods. We used several Machine Learning methods to find the correlation between gait deviation and isokinetic data: Adaptive Boosting Regression, K-nearest Neighbor, Decision Tree Regression, Random Forest Regression, and Gradient Boost Regression. Results: In this paper, we provided a detailed comparison of different machine learning regression models in predicting gait quality in patients with CP based only on the data gathered from affordable Biodex 4 Pro device. The best result was obtained using the gradient boosting regression model with Mean Absolute Percentage Error of 6%. However, it was not possible to precisely predict the GGI index using this method. Significance: The results obtained showed promising results in the evaluation of gait index scores, which gives the possibility of diagnosing patients with CP without the use of expensive optometric systems. Evaluating gait metrics using the approach proposed in this paper could be very helpful for both physicians and physiotherapists in assessing the condition of patients with CP, as well as other diseases related to gait problems.

A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.

Robotic systems can support crisis and disaster management when direct human intervention would be dangerous or delayed. This review examines 76 English-language publications indexed in Scopus and Web of Science and published from 2006 to July 2025. The studies were classified across three management domains and three Robotics Ecosystem categories. Within the reviewed sample, most publications appeared after 2015, and conference papers were the dominant document type. None of the seven reported chi-square statistics had a p-value below 0.05. Five comparisons involved overlapping labels and were therefore treated only as exploratory summaries; the descriptive counts and qualitative findings received greater interpretive weight. The reviewed literature suggests that no single robotic platform meets the demands of every disaster scenario. UAVs are useful for surveying, mapping, and observing inaccessible areas, while ground and specialized robots support close inspection, environmental sensing, and work in confined or unstable environments. Multi-robot systems can extend mission coverage but also increase the demands on communication, coordination, data fusion, task allocation, and operator supervision. Many of the included solutions were evaluated only in simulations, prototype demonstrations, or short experiments. Recurrent limitations concerned field validation, perception, localization, real-time processing, communication, autonomy, interoperability, reliability, energy use, and human–robot collaboration. Future studies should test complete systems in repeatable field exercises and retain human supervision over safety-critical decisions.

Over the years, the impact of music on the prenatal and neonatal stages of human life has gained significant scientific attention. This study provides a comprehensive bibliometric review of research investigating how music influences fetal and newborn development. Using the Scopus and Web of Science databases, a search of relevant studies published in English between January 2006 and July 2025 was conducted, whose basic criterion was the use of the following keywords: “music” and “fetus” or “fetal”. Additional terms such as “fetus development”, “fetus heart rate”, “fetus movement”, “mother–fetus relationship”, “newborn”, etc., were also utilized. In result, 75 publications were selected, and their bibliographic data and full sources were retrieved. The included studies were grouped according to two perspectives that consider the impact of music (1) on the development of the fetus and the newborn, and (2) on maternal health and mother–fetus bonding. Using VOSviewer, bibliometric mapping was performed, which allowed to obtain keyword co-occurrence network and co-authorship network. The chosen literature was then quantitatively and qualitatively analyzed. The analysis revealed a sharp upward trend in publications starting in 2015, with a temporary decline in 2020 due to the COVID-19 pandemic. The highest number of publications were from Iran. The most investigated topics were related to the fetus heart and maternal health. The most common publication type and research methodology were, respectively, article and experiment. While the key authors Lordier, L., Filippa, M., Grandjean, D., and Monaci, M.G. lead the field, the co-authorship network remains fragmented into isolated and relatively small research groups. The Journal of Maternal–Fetal and Neonatal Medicine emerged as the leading publication outlet, while the study by Graven et al. entitled “Auditory Development in the Fetus and Infant” remains the most cited work. The keyword co-occurrence network allowed the identification of three main thematic clusters indicating the physiological, clinical, and therapeutic aspects of the impact of music on fetal and neonatal development. A qualitative analysis revealed that music plays a vital role in early human development and maternal well-being, demonstrating positive effects of auditory stimuli on fetal and newborn physiology, as well as on the mother–fetus relationship, while being a non-invasive and non-pharmacological method of intervention. However, the lack of a fully connected global research community and standardized protocols for, e.g., choosing the musical repertoire, sound administration, and the duration of exposure suggests the need for increased international collaboration to further integrate music therapy into standard clinical practices for prenatal and neonatal care.

Large language models complicate text-origin assessment and increase risks from nonexistent, inaccurate, or strategically used citations. This scoping review maps methods for detecting AI-generated or substantially AI-modified text and for evaluating reference integrity and citation manipulation in scholarly communication. We searched Scopus and Web of Science Core Collection on 31 August 2026, and IEEE Xplore on 2 September 2026. Four thematic search streams yielded 1633 records after filters. Following deduplication, removal of five 2027 records, citation searching, title-and-abstract screening, and full-text assessment, 71 peer-reviewed publications were retained; 69 formed the main synthesis and two provided conceptual background. Detection approaches included statistical signals, conventional machine learning, transformer classifiers, stylometry, watermarking, retrieval, and process-based provenance. Performance varied across generators, domains, languages, text lengths, paraphrases, and mixed human–AI texts, limiting the evidentiary value of a single detector score. Citation-integrity studies addressed fabricated or inaccurate references, claim–source agreement, citation function, coercion, cartels, stacking, self-citation, paper mills, and metadata manipulation. On the basis of this mapped evidence, we propose a layered conceptual audit combining provenance, targeted detection, bibliographic verification, claim-level checking, and network or metadata analysis. The integrated workflow itself has not yet been empirically validated, and consequential decisions should remain subject to documented human review.