Search Results (864)

This study aimed to compare the changes in brain functional connectivity between states of stress induction and recovery in mentally stable, healthy individuals to investigate the effects of stress on brain networks. We selected a stable group comprising 20 healthy adults with Perceived Stress Scale scores of 0–13 points and a mean age of 24.4 ± 4.3 years. We used the Montreal Imaging Stress Task to induce stress and captured images of the brain using a 3T magnetic resonance imaging scanner. We analyzed the region of interest (ROI)-to-ROI connectivity and compared the differences in functional connectivity between the stress and recovery phases. In the stress state, we observed increased connectivity between the dorsal attention and sensorimotor networks and between the visual and default mode networks. In the recovery state, the default mode network became reactivated, and connectivity supporting self-referential thinking and stability was observed. The connectivities observed only in the recovery phase were Language.pSTG (R)—DefaultMode.LP (R) and DefaultMode.LP (R)—Visual.Lateral (R). Our findings provide important basic data for the development of stress management and recovery strategies. By assessing healthy individuals, our findings provide new perspectives on stress resilience in the brain. Full article

Although approximately half of global lithium consumption is used in the rechargeable battery industry, lithium is also in demand for other specialized applications, such as high-temperature lubricants, ceramics, glass, and pharmaceuticals. The growing need for efficient lithium recovery and recycling underscores the importance of fast and accurate analytical tools for determining lithium concentrations in non-compliant and waste materials generated by industrial processes. In this paper, we present a machine learning-based procedure utilizing Laser-Induced Breakdown Spectroscopy (LIBS) to accurately quantify lithium concentrations in lithium-rich non-compliant materials derived from the industrial production of enamels used for coating metallic surfaces. This procedure addresses challenges such as strong self-absorption and matrix effects, which limit the effectiveness of conventional univariate calibration methods. By employing a multivariate approach, we developed a single model capable of quantifying lithium content across a wide concentration range. A comparison of the LIBS results with those obtained using conventional laboratory analysis (Inductively Coupled Plasma–Optical Emission Spectrometry, ICP-OES) confirms that LIBS can deliver the speed, precision, and reliability required for potential routine applications in the lithium recovery and recycling industry. Full article

Asymmetric nanomotors are a class of self-propelled nanoparticles that exhibit asymmetries in shape, composition, or surface properties. Their unique asymmetry, combined with nanoscale dimensions, endows them with significant potential in environmental and biomedical fields. For instance, glutathione (GSH) induced chemotactic nanomotors can respond to the overexpressed glutathione gradient in the tumor microenvironment to achieve autonomous chemotactic movement, thereby enhancing deep tumor penetration and drug delivery for efficient induction of ferroptosis in cancer cells. Moreover, self-assembled spearhead-like silica nanomotors reduce fluidic resistance owing to their streamlined architecture, enabling ultra-efficient catalytic degradation of lipid substrates via high loading of lipase. This review focuses on three core areas of asymmetric nanomotors: scalable fabrication (covering synthetic methods such as template-assisted synthesis, physical vapor deposition, and Pickering emulsion self-assembly), propulsion mechanisms (chemical/photo/biocatalytic, ultrasound propelled, and multimodal driving), and functional applications (environmental remediation, targeted biomedicine, and microelectronic repair). Representative nanomotors were reviewed through the framework of structure–activity relationship. By systematically analyzing the intrinsic correlations between structural asymmetry, energy conversion efficiency, and ultimate functional efficacy, this framework provides critical guidance for understanding and designing high-performance asymmetric nanomotors. Despite notable progress, the prevailing challenges primarily reside in the biocompatibility limitations of metallic catalysts, insufficient navigation stability within dynamic physiological environments, and the inherent trade-off between propulsion efficiency and biocompatibility. Future efforts will address these issues through interdisciplinary synthesis strategies. Full article

Line-Start Permanent Magnet Synchronous Motors (LSPMSMs) offer a hybrid solution that combines the high efficiency of permanent magnet motors with the self-starting capability of induction machines. This review examines their key performance characteristics, historical development, and design approaches. Advantages such as high efficiency, improved power factor, and operational stability are discussed alongside challenges like limited critical inertia and synchronization issues. Design enhancements through rotor topology optimization and cage resistance adjustment are also explored. Finally, market trends and economic considerations are evaluated, highlighting the strong potential of LSPMSMs in energy-efficient motor applications. Full article

Background: Undergraduate nursing students (UNSs) often enter clinical training just as they are still mastering the emotional labor of the profession. In Nepal, where teaching hierarchies discourage upward dialogue and hospitals routinely struggle with overcrowding, supply shortages, and outward nurse migration, these learners confront a distinct, under-documented burden of psychological distress. Objective: This study examines how UNSs interpret, negotiate, and cope with the mental health challenges that arise at the intersection of cultural deference, resource scarcity, and migration-fueled uncertainty. Methods: A qualitative design employing reflexive thematic analysis (RTA), guided by the Reflexive Thematic Analysis Reporting Guidelines (RTARG), was used. Fifteen second-, third-, and fourth-year Bachelor of Science in Nursing students at a major urban tertiary institution in Nepal were purposively recruited via on-campus digital flyers and brief in-class announcements that directed students (by QR code) to a secure sign-up form. Participants then completed semi-structured interviews; audio files were transcribed verbatim and iteratively analyzed through an inductive, reflexive coding process to ensure methodological rigor. Results: Four themes portray a continuum from silenced struggle to systemic constraint. First, Shrouded Voices, Quiet Connections captures how students confide only in trusted peers, fearing that formal disclosure could be perceived as weakness or incompetence. Second, Performing Resilience: Masking Authentic Struggles describes the institutional narratives of “strong nurses” that drive students to suppress anxiety, adopting scripted positivity to satisfy assessment expectations. Third, Power, Hierarchy, and the Weight of Tradition reveals that strict authority gradients inhibit questions in classrooms and clinical placements, leaving stress unvoiced and unaddressed. Finally, Overshadowed by Systemic Realities shows how chronic understaffing, equipment shortages, and patient poverty compel students to prioritize patients’ hardships, normalizing self-neglect. Conclusions: Psychological distress among Nepalese UNSs is not an individual failing but a product of structural silence and resource poverty. Educators and policymakers must move beyond resilience-only rhetoric toward concrete reforms that dismantle punitive hierarchies, create confidential support avenues, and embed collaborative pedagogy. Institutional accountability—through regulated workloads, faculty-endorsed wellbeing forums, and systematic mentoring—can shift mental health care from a private struggle to a shared professional responsibility. Multi-site studies across low- and middle-income countries are now essential for testing such system-level interventions and building a globally resilient, compassionate nursing workforce. Full article

Background: Solid organ transplantation improves survival and quality of life but requires lifelong self-management. While models exist for kidney and liver recipients, a comprehensive framework for all solid organ transplant recipients is lacking. Addressing this gap is essential for optimizing post-transplant care. Objectives: This report aims to conceptualize self-management after solid organ transplantation by addressing questions related to (1) the contexts studied to date, (2) research methodologies and publication types used, and (3) core aspects associated with self-management post-transplantation. Methods: A scoping review was used to address the above objectives. A comprehensive search strategy identified relevant studies, followed by systematic screening, data extraction, and qualitative content analysis. Findings were categorized using a deductive–inductive coding approach to map core self-management aspects after solid organ transplantation. Results: The search yielded 34,417 records, with 742 ultimately included. Publications from 43 countries spanned 43 years, with many (48.9%) published after 2016. Research articles dominated (80.1%), covering kidney (61%), liver (22%), heart (21%), and lung (16%) transplants. A qualitative analysis identified four self-management domains containing various categories: (1) Managing the medical–therapeutic regimen, (2) managing biographical work, (3) managing (new) life roles, and (4) generic self-management skills. The conceptual model illustrates their interconnections, with aspects of the medical–therapeutic regimen management most frequently covered. Conclusions: Self-management after solid organ transplantation is complex, involving medical–therapeutic, emotional, social, and behavioral aspects. Aspects of managing the medical–therapeutic regimen dominate the research literature, while other aspects need further exploration. Future studies should address gaps to support holistic, patient-centered post-transplant care strategies. Full article

Background/Objectives: Lucid dreaming (LD), during which the dreamer becomes aware of the dream state, offers a unique opportunity for a variety of applications, including motor practice, personal well-being, and nightmare therapy. However, these applications largely depend on the dreamer’s ability to control their dreams. While LD research has traditionally focused on induction techniques to increase dream frequency, the equally important skill of dream control remains largely underexplored. This study provides an exploration into the mechanisms of LD motor practice, dream control, and its potential influencing factors. We specifically examined whether a complex motor skill—juggling—could be performed during LD, calling for relatively high levels of dream control and access to procedural memory. Methods: Four healthy participants underwent overnight polysomnography (PSG), provided detailed dream reports, and completed questionnaires assessing dream control and self-efficacy. Dream-task success was assessed using predefined in-dream motor performance criteria. Differences between high and low LD control participants were examined, and two detailed case reports of lucid dream juggling attempts provide insight into the challenges of executing complex motor tasks during LD. Results: Dream control varied between and within participants. Both dream control and self-efficacy seemed to predict participants’ ability to execute the LD motor task. Conclusions: Despite the low sample size, this study highlights the potential roles of individual traits like self-efficacy in shaping dream control abilities and motor performance during LD. By using empirical, task-based measures, this study helps build the foundation for future research aimed at optimizing LD applications in clinical and non-clinical fields. Full article

In this study, we present a wearable sensing system for monitoring the physiological status of damaged biological tissues based on a flexible, frequency-coded electromagnetic spiral resonator array. The physiological parameter evaluation is performed in a contactless way, avoiding the placing of electronically active elements directly upon the patient’s skin, thus ensuring safety and comfort. Firstly, we report in detail the physical principles behind the sensing strategy: a passive array is interrogated through an actively fed external single-loop probe that is inductively coupled with the double-layer spiral unit cells. The variation in the physiological parameters influences the array response, thus providing sensing information, due to the different complex dielectric permittivity values related to the tissue status. Moreover, the proposed frequency-coded approach allows for spatial information on the lesion to be retrieved, thus increasing the sensing ability. In order to prove the validity of this general methodology, we created a numerical test case, designing a practical implementation of the wearable sensing system working at a radiofrequency regime (10–100 MHz). In addition, we also fabricated prototypes, exploiting PCB technology, and realized stratified phantoms by incorporating opportune additives to control the dielectric properties. The numerical results and the experimental verification demonstrated the validity of the developed sensing strategy, showing satisfying agreement and, thus, proving the good sensibility and spatial resolution of the frequency-coded array. These results can open the path to a radically novel approach for self-care and monitoring of inflamed status and, more generally, for wearable sensing devices in biomedical applications. Full article

Background/Objectives: Policy implementers play a crucial role in the effective delivery of policies aiming at promoting a healthy lifestyle in the most vulnerable populations. This study aimed to explore (a) policy implementers’ knowledge and perceptions of the policy framework promoting physical activity and healthy nutrition among children in need in Greece, and (b) self-perceived barriers and facilitators of the framework implementation. The term children in need refers to children who are at risk of poverty and/or social exclusion. Methods: A qualitative study design was employed consisting of semi-structured interviews with 25 policy implementers, who represented four delivery systems (health, social protection, food, and education sectors) from three geographical regions in Greece. Interviews were completed between November and December 2023. Thematic analysis was conducted using inductive and deductive approaches to identify key themes, following data management in the N-VIVO 14 software. Results: Commonly mentioned policies that study participants were involved in included school- and/or community-level-based behavioral interventions. Participants perceived policy implementation efforts that often relied on individual initiatives as inconsistent. Most participants argued that existing policies were not tailored to the needs of children in need. Major self-perceived barriers included limited personnel training, limited facilities and infrastructure, and lack of incentives or opportunities to encourage active participation. Major self-perceived facilitators included personnel motivation, integration of nutrition and physical education into school curricula, and provision of free school meals, which was associated with regular school attendance of children from the Roma communities. Conclusions: Individual, sociocultural, and structural issues are shown to persist across different delivery systems indicating the complexity of tackling obesogenic environments, especially among children in need. This is the first study in Greece to provide evidence on self-perceived barriers and facilitators and could inform ongoing national and European efforts to address obesogenic environments in children in need. Full article

CareCoach seeks to enhance self-efficacy in family caregivers of people living with dementia and has been feasibility tested in a multicentre randomised controlled trial. The intervention offers two face-to-face sessions with a trained coach and access to an online platform with nine modules. This paper reports findings from an embedded qualitative process evaluation assessing implementation from the implementer’s (‘coach’s’) (n = 8) perspective using individual interviews and implementer group discussions. Qualitative data were transcribed verbatim, inductively coded and analysed using Normalisation Process Theory. Implementers demonstrated (1) ‘Coherence’ by seeking to understand how CareCoach compared to current practice, highlighting the importance of supporting coaches to differentiate and identify boundaries between their new ‘coach role’ and usual practice; (2) ‘Cognitive Participation’ by reviewing training and resources to understand their role own responsibilities and facilitate delivery of coaching sessions; group supervision and peer support were also emphasised; (3) ‘Collective Action’ through interactions with carers to deliver key behavioural aspects such as goal setting, problem solving, and providing feedback; and (4) ‘Reflexive Monitoring’ by appraising the intervention to gain useful insights that could facilitate refinement of CareCoach training and delivery. This study provides a theoretically informed understanding of the implementation of CareCoach for caregivers of people living with dementia and provides recommendations to enhance training for coaches, intervention delivery and carer engagement. Full article

In this paper, the accuracy of basic and advanced spiral inductor models for gallium nitride (GaN) integrated inductors is evaluated. Specifically, the experimental measurements of geometrically scaled circular spiral inductors, fabricated in a radio frequency (RF) GaN-on silicon technology, are exploited to estimate the errors of two lumped geometrically scalable models, i.e., a simple π-model with seven components and an advanced model with thirteen components. The comparison is performed by using either the standard performance parameters, such as inductance (L), quality factor (Q-factor), and self-resonance frequency (SRF), or the two-port scattering parameters (S-parameters). The comparison reveals that despite a higher complexity, the developed advanced model achieves a significant reduction in SRF percentage errors in a wide range of geometrical parameters, while enabling an accurate estimation of two-port S-parameters. Indeed, the correct evaluation of both SRF and two-port S-parameters is crucial to exploit the model in an actual circuit design environment by properly setting the inductor geometrical parameters to optimize RF performance. Full article

This review explores the emerging role of functionalized hydrogels in modulating the microbiome for therapeutic applications in tissue regeneration and infection control. The skin and gut microbiomes play crucial roles in maintaining tissue homeostasis, regulating immune responses, and influencing the healing process. Disruptions in microbial balance—such as those observed in chronic wounds, autoimmune conditions, or post-surgical environments—can impair regeneration and increase susceptibility to infection. Hydrogels, due to their tunable physical and chemical properties, serve as versatile platforms for delivering probiotics, prebiotics, antimicrobials, and immune-modulatory agents. The encapsulation of beneficial bacteria, such as Lactobacillus plantarum or Prevotella histicola, within hydrogels could enhance bacterial viability, targeted delivery, and immune tolerance. Additionally, hydrogels functionalized with silver nanoparticles, nitric oxide donors, and bacteriocins have demonstrated effective biofilm disruption and pathogen clearance. These systems also promote favorable immune responses, such as M2 macrophage polarization and the induction of regulatory T cells, which are essential for tissue repair. Innovative approaches, including 3D bioprinting, self-healing materials, and photothermal-responsive hydrogels, expand the clinical versatility of these systems. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

Aim: To explore the experiences, beliefs, and values of patients who participated in a two-arm randomized clinical trial assessing a nurse-led intervention program for chronic pain self-management, which demonstrated positive effects on pain reduction, depression, and anxiety, and on health-related quality of life 24 months after completion of the program. Design: Descriptive phenomenological qualitative study. Methods: Patients were recruited via telephone, informed about the study, and invited to participate in an individual interview at a place of their choice (hospital or home). All interviews were audiotaped, and an inductive thematic analysis was performed. Results: Seven interviews were carried out between both groups. Six emerging categories were found: effective relationship with the healthcare system, learning to live with pain, family and social support, behaviors regarding pain, resources for self-management, and concomitant determinants. Conclusions: Patients report key aspects that help us to understand the impact of this type of nurse-led group intervention: the intrinsic therapeutic effect of participating in the program itself, the ability to learn to live with pain, the importance of family and social support, the modification of pain-related behaviors, and the identification of resources for self-care. The findings highlight the need for gender-sensitive, individualized care approaches to chronic pain, addressing stigma and social context. Expanding community-based programs and supporting caregivers is essential, as is further research into gender roles, family dynamics, and work-related factors. Full article

Shaded-pole induction motors are the most frequently used single-phase electric motors in low power applications. Their main advantages are reliability, robustness, low level of noise and vibration, relatively simple manufacturing technology and cost effectiveness. These motors are the driving units of choice in the applications where the variable speed and high starting torque are not of utmost importance, in spite of the fact that they are characterized by inferior efficiency, power factor and starting torque compared to their single-phase counterparts. They are equipped with auxiliary massive copper coils at the stator side, which makes them self-starting, and strongly influence the motor characteristics. This study deals with the numerical modeling and analysis of a shaded-pole induction motor with a C-shaped stator frame. The analysis was performed using 2D finite element-based transient magnetic numerical modeling. The primary objective was to investigate the influence of the number and size of the auxiliary shaded coils on the output torque speed characteristic. We explored the possibility of reducing the amount of material used while preserving the crucial/nominal properties of the motor. Our results have important implications in manufacturing simplification, which may be important for the eco-design of small motors and actuators, including their recycling and/or reuse process. Full article