Abstract

Abstract
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Abstract

Abstract
Alzheimer's disease (AD) places a profound global challenge, driven by its escalating prevalence and the multifaceted strain it places on individuals, families, and societies. Family caregivers (FCs), who are pivotal in supporting family members with AD, frequently endure substantial emotional, physical, and psychological demands. To better understand the determinants of family caregiving strain, this study employed machine learning (ML) to develop predictive models identifying factors that contribute to caregiver burden over time. Participants were evaluated across sociodemographic clinical, psychophysiological, and psychological domains at baseline (T1; N = 130), six months (T2; N = 114), and twelve months (T3; N = 92). Results revealed three distinct risk profiles, with the first focusing on T2 data, highlighting the importance of distress, forgiveness, age, and heart rate variability. The second profile integrated T1 and T2 data, emphasizing additional factors like family stress. The third profile combined T1 and T2 data with sociodemographic and clinical features, underscoring the importance of both assessment moments on distress at T2 and forgiveness at T1 and T2, as well as family stress at T1. By employing computational methods, this research uncovers nuanced patterns in caregiver burden that conventional statistical approaches might overlook. Key drivers include psychological factors (distress, forgiveness), physiological markers (heart rate variability), contextual stressors (familial dynamics, sociodemographic disparities). The insights revealed enable early identification of FCs at higher risk of burden, paving the way for personalized interventions. Such strategies are urgently needed as AD rates rise globally, underscoring the imperative to safeguard both patients and the caregivers who support them.

Abstract
The manufacturing process of thermoregulation products with polyester (PES) fabric and conductive polymers such as poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) with proper wearability, comfort, and high performance is still a challenge due to low adhesion, environment instability and nonuniform coatings. This study presents a simple and effective method for producing thermoregulatory PES fabrics using the Joule heating effect. Textiles treated with dielectric barrier discharge (DBD) plasma were functionalized with PEDOT:PSS incorporating secondary dopants, such as dimethyl sulfoxide (DMSO) and glycerol (GLY). PEDOT:PSS was used because it does not compromise the mechanical properties of base materials. DBD plasma treatment was applied to PES to improve the substrate's functional groups and consequently increase adhesion and homogeneity of the PEDOT:PSS on the substrate. The polymer were applied to the textiles by dip-pad-drycure method ensuring uniform distribution and homogeneous heating of the materials. The samples' conductivity, impedance, potential and Joule effect, and their morphological, chemical and thermal properties were studied. Control samples without plasma treatment and secondary dopants were also prepared. The results showed that the DBD-treated samples, coated with 5 layers of PEDOT:PSS, doped with DMSO 7 % (w/v), displayed the best conductivity and Joule effect performance reaching 44.3 degrees C after 1 h.

Abstract
Accurate surgical planning is critical in mandibular reconstruction to restore the oncology patient's function and aesthetics. However, the use of physical three-dimensional (3D) models is often limited by time-consuming manual segmentation procedures or the high cost of commercial solutions. This work addresses the need for an accessible, quick, and low-cost pipeline to obtain a 3D printed model of the segmented mandible from a Computed Tomography (CT) scan. The automatic segmentation stage relied on the two-dimensional U-Net architecture, which was trained and validated with slices across two public datasets (PDDCA, HaN-Seg) and tested with the other two public datasets (TCIA RT, Austrian). The best model achieved an average dice similarity coefficient (DSC) of 0.912 ± 0.077 across all test sets. The segmentation output was reconstructed into a 3D volume, improved through a post-processing method (with morphological closing, upsample, smoothing, and mesh reduction), and 3D printed through fused deposition modelling. The assessment of a stomatologist confirmed overall high anatomical fidelity to the CT and clinical utility, even though further improvements in important fine anatomical elements were suggested. This solution contributes to a promising alternative to producing 3D personalised mandibles for surgical planning, reducing time and manual effort while improving the quality and accessibility. Future work may explore the use of 3D DL architectures and a broader evaluation of the 3D mandible models. © 2025 IEEE.