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This study investigates a novel anchor-based method for controlled rock fragmentation, designed as an alternative to conventional excavation or explosive techniques. The proposed solution utilizes a specially modified undercut anchor that induces localized failure within the rock mass through radial expansion rather than traditional pull-out forces. Finite Element Method simulations, performed in ABAQUS with an extended fracture mechanics approach, were used to model the initiation and propagation of failure zones in sandstone. The results revealed a two-phase cracking process starting beneath the anchor’s driving element and progressing toward the rock’s free surface, forming a breakout cone. This behavior significantly deviates from conventional prediction models, such as the 45° cone or Concrete Capacity Design methods (cone 35°). The simulations were supported by field tests, confirming both the feasibility and practical advantages of the proposed anchor system, especially in confined or safety-critical environments. The findings offer valuable insights for the development of compact and efficient rock fragmentation technologies suitable for mining, rescue operations, and civil engineering applications.

Articular cartilage (AC) plays an important role in the biomechanics of synovial joints. Its task is to enable smooth movement and transfer of mechanical loads with minimised friction. AC is characterised by unique mechanical properties resulting from its complex structure, in which the dominant components are type II collagen, proteoglycans and water. Healthy articular cartilage shows elasticity in compression, viscoelastic properties, and the ability to relax stresses under the influence of cyclic loads. In response to different loading modes, it shows anisotropic and non-uniform behaviour, which translates into its cushioning and protective function for the subchondral bone. Significant changes occur in the structure and mechanical properties of cartilage with age as a result of mechanical overload or degenerative diseases, such as osteoarthritis. This results in a deterioration of the cushioning and mechanical function, which leads to progressive degradation of joint tissues. Understanding the mechanical properties of AC is crucial for developing effective diagnostic methods. Analysis of changes in mechanical properties contributes to the early detection of pathological changes. The aim of this paper is to review the current state of knowledge regarding the structure and biomechanical properties of articular cartilage, and to analyse conventional and alternative diagnostic methods in the context of their suitability for assessing the state of AC, particularly in the early stages of degenerative processes.

Background: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. Methods: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain–computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. Results: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. Conclusions: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.

Background/Objectives: To investigate the volume of the lateral geniculate nucleus (LGN) in patients with advanced retinitis pigmentosa (RP). Methods: Nineteen patients with advanced RP (mean age 52 years and mean duration of illness 357 months) and twenty-one age-matched normal subjects have been examined using 7 Tesla MRI of the brain. Brain segmentation was carried out with the “recon-all” function in the FreeSurfer software (version 7.4.1) package. Results: The volumes of the left and right LGN were significantly smaller in those patients with RP, in comparison to the controls. We found a significant positive correlation between the volume of the left LGN and the right eye best-corrected visual acuity in the RP group. Conclusions: Late-stage RP leads to a significant reduction in LGN volume, as measured with 7 Tesla MRI. The volume of the left LGN was correlated with the visual function of the right eye in patients with late-stage RP. The statistical power of the results is limited by there being a low number of RP patients included in this study, which is due to the low prevalence of this rare eye disease.

When performing cognitively demanding tasks, people tend to experience momentary distractions or personal associations that intercept their stream of consciousness. This phenomenon is known as Mind Wandering (MW) and it has become a subject of neuroscientific investigations. Off-task thoughts can be analyzed during task performance, but currently, MW is also understood as a dimension of individual differences in cognitive processing. We wanted to recognize the intrinsically-organized functional networks that could be considered the neuronal basis for MW dispositional variability. To achieve this goal we recruited a group of normal adults, and eventually divided the group in half, based on participants’ scores on the scale measuring dispositional MW. Next, these groups were compared regarding the arrangement of preselected intrinsic functional networks, which were reconstructed based on multi-channel signal-source resting-state EEG. It appeared that subjects who tend to mind wander often exhibited decreased synchronization within the default mode network, and, simultaneously, strengthened connectivity between ‘on-task’ networks of diverse functional specificity. Such within- and between networks integrity patterns might suggest that greater Mind Wanderers present an atypical organization of resting-state brain activity, which may translate into attenuated resources needed to maintain attentional control in task-related conditions.

Leber’s hereditary optic neuropathy (LHON) is a rare, maternally inherited genetic disease caused by a mutation of mitochondrial DNA. Classical descriptions have highlighted structural abnormalities in various parts of patients’ optic tracts; however, current studies have proved that changes also affect many cortical and subcortical structures, not only these belonging to the visual system. This study aimed at improving our understanding of neurophysiological impairments in LHON. First of all, we wanted to know if there were any differences between the health control and LHON subjects in the whole-brain source electroencephalography (EEG) analysis. Second, we wanted to investigate the associations between the observed results and some selected aspects of Leber’s disease’s clinical picture. To meet these goals, 20 LHON patients and 20 age-matched healthy control (HC) subjects were examined. To investigate the electrophysiological differences between the HC and LHON groups, a quantitative analysis of the whole-brain current source density was performed. The signal analysis method was based on scalp EEG data and an inverse solution method called low-resolution brain electromagnetic tomography (eLORETA). In comparison with the healthy subjects, LHON participants showed significantly decreased neuronal activity in the alpha and gamma bands; more specifically, in the alpha band, the decrease was mainly found in the occipital lobes and secondary visual cortex, whereas, in the gamma band, the reduced activity occurred in multiple cortical areas. Additionally, a correlation was found between the alpha band activity of the right secondary visual cortex and the averaged thickness of the right retinal nerve fiber layer in the LHON participants. Our study suggests that LHON is associated with widespread cortical de-activation, rather than simply abnormalities of structures constituting the visual system.

Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data. We collected a dataset comprising 144 corneal scans from adults aged 18–35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. The Random Forest algorithm demonstrated the highest reliability, achieving an accuracy of 98% during training and 96% on the test set, while also identifying the most diagnostically relevant measurements. Unlike prior studies, our approach enables detailed comparison between model-selected features and clinically recognized diagnostic parameters. This interpretability provides a clinically meaningful bridge between AI-driven predictions and expert-based decision-making. The results suggest that machine learning models, particularly Random Forest, can effectively aid in the early detection of keratoconus in young individuals, potentially improving patient outcomes through timely intervention.

The use of synthetic fertilizers, including nitrogen [N] fertilizers, is an indispensable element in today’s agriculture. Through adequate fertilization, farmers have the opportunity to increase crop yields, which is essential in view of the growing population and demand for food. The European Union’s “Farm to Fork” [F2F] strategy, as part of the broader European Green Deal, aims to promote more sustainable agricultural practices by reducing chemical fertilizer use by 20% by 2030. This initiative is designed to mitigate the negative environmental impacts of excessive N application, such as soil and water contamination, greenhouse gas emissions, and biodiversity loss. In addition to addressing ecosystem concerns, this strategy also aims to reduce health risks associated with N overuse, such as the accumulation of nitrates [NO3−] in crops, which can lead to the formation of carcinogenic compounds. By integrating alternative fertilization methods, the agricultural sector can work toward more resilient and environmentally friendly systems while maintaining productivity. This paper focuses on a summary of the current knowledge about the consequences of N fertilization reduction and its connection to the soil environment, crops, yields, and human health.

Supporting the development of a child with autism is a multi-profile therapeutic work on disturbed areas, especially understanding and linguistic expression used in social communication and development of social contacts. Previous studies show that it is possible to perform some therapy using a robot. This article is a synthesis review of the literature on research with the use of robots in the therapy of children with the diagnosis of early childhood autism. The review includes scientific journals from 2005–2021. Using descriptors: ASD (Autism Spectrum Disorders), Social robots, and Robot-based interventions, an analysis of available research in PubMed, Scopus and Web of Science was done. The results showed that a robot seems to be a great tool that encourages contact and involvement in joint activities. The review of the literature indicates the potential value of the use of robots in the therapy of people with autism as a facilitator in social contacts. Robot-Assisted Autism Therapy (RAAT) can encourage child to talk or do exercises. In the second aspect (prompting during a conversation), a robot encourages eye contact and suggests possible answers, e.g., during free conversation with a peer. In the third aspect (teaching, entertainment), the robot could play with autistic children in games supporting the development of joint attention. These types of games stimulate the development of motor skills and orientation in the body schema. In future work, a validation test would be desirable to check whether children with ASD are able to do the same with a real person by learning distrust and cheating the robot.

Introducing the Minimum Spanning Tree (MST) algorithms to neural networks science eliminated the problem of arbitrary setting of the threshold for connectivity strength. Despite these advantages, MST has been rarely used to study network abnormalities in schizophrenia. An MST graph mapping a network structure is its simplification, therefore, it is important to verify whether the reconfigured network is significantly related to the behavioural dimensions of the clinical picture of schizophrenia. 35 first-episode schizophrenia patients and 35 matched healthy controls underwent an assessment of information processing speed, cognitive inter-trial variability modelled with ex-Gaussian distributional analysis of reaction times and resting-state EEG recordings to obtain frequency-specific functional connectivity matrices from which MST graphs were computed. The patients’ network had a more random structure and star-like arrangement with overloaded hubs positioned more posteriorly than it was in the case of the control group. Deficient processing speed in the group of patients was predicted by increased maximal betweenness centrality in beta and gamma bands, while decreased consistency in cognitive processing was predicted by the betweenness centrality of posterior nodes in the gamma band, together with duration of illness. The betweenness centrality of posterior nodes in the gamma band was also significantly correlated with positive psychotic symptoms in the clinical group.