Background: Multiple sclerosis is often a longitudinal disease continuum with an initial relapsing-remitting phase (RRMS) and later secondary progression (SPMS). Most currently approved therapies are not sufficiently effective in SPMS. Early detection of SPMS conversion is therefore critical for therapy selection. Important decision-making tools may include testing of partial cognitive performance and magnetic resonance imaging (MRI). Aim of the work: To demonstrate the importance of cognitive testing and MRI for the prediction and detection of SPMS conversion. Elaboration of strategies for follow-up and therapy management in practice, especially in outpatient care. Material and methods: Review based on an unsystematic literature search. Results: Standardized cognitive testing can be helpful for early SPMS diagnosis and facilitate progression assessment. Annual use of sensitive screening tests such as Symbol Digit Modalities Test (SDMT) and Brief Visual Memory Test-Revised (BVMT-R) or the Brief International Cognitive Assessment for MS (BICAMS) test battery is recommended. Persistent inflammatory activity on MRI in the first three years of disease and the presence of cortical lesions are predictive of SPMS conversion. Standardized MRI monitoring for features of progressive MS can support clinically and neurocognitively based suspicion of SPMS. Discussion: Interdisciplinary care of MS patients by clinically skilled neurologists, supported by neuropsychological testing and MRI, has a high value for SPMS prediction and diagnosis. The latter allows early conversion to appropriate therapies, as SPMS requires different interventions than RRMS. After drug switching, clinical, neuropsychological, and imaging vigilance allows stringent monitoring for neuroinflammatory and degenerative activity as well as treatment complications.

Problem: in recent years, new studies have been published on biological effects of strong static magnetic fields and on thermal effects of high-frequency electromagnetic fields as used in magnetic resonance imaging (MRI). Many of these studies have not yet been incorporated into current safety recommendations. Method: scientific publications from 2010 onwards on the biological effects of static and electromagnetic fields of MRI were searched and evaluated. Results: new studies confirm older work that has already described effects of static magnetic fields on sensory organs and the central nervous system accompanied by sensory perception. A new result is the direct effect of Lorentz forces on ionic currents in the semicircular canals of the vestibular organ. Recent studies on thermal effects of radiofrequency fields focused on the development of anatomically realistic body models and more accurate simulation of exposure scenarios. Recommendation for practice: strong static magnetic fields can cause unpleasant perceptions, especially dizziness. In addition, they can impair the performance of the medical personnel and thus potentially endanger patient safety. As a precaution, medical personnel should move slowly in the field gradient. High-frequency electromagnetic fields cause tissues and organs to heat up in patients. This must be taken into account in particular for patients with impaired thermoregulation as well as for pregnant women and newborns; exposure in these cases must be kept as low as possible.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

This paper conducts a comparative analysis of mentoring and metacognition in education, unveiling their intricate connections. Both concepts, though seemingly disparate, prove to be interdependent within the educational landscape. The analysis showcases the dynamic interplay between mentoring and metacognition, emphasizing their reciprocal influence. Metacognition, often perceived as self-awareness and introspection, is found to complement the relational and supportive nature of mentoring. Within this context, metacognitive education within mentoring emerges as a vital component. Practical recommendations are offered for effective metacognitive training, highlighting its role in enhancing cognitive and metacognitive skills. Moreover, the paper introduces the concept of a “mentoring scaffolding system.” This system emphasizes mentor-led gradual independence for mentees, facilitating their professional and personal growth. The necessity of fostering a metacognition culture in education is a central theme. Such a culture promotes improved performance and lifelong learning. The paper suggests integrating metacognition into curricula and empowering learners as essential steps toward achieving this culture. In conclusion, this paper advocates for the integration of metacognition into mentoring and education, fostering self-awareness, independence, and adaptability. These attributes are deemed crucial for individuals navigating the challenges of the information age.