Search Results (8,141)

Teacher wellbeing is a matter of social justice since burnout syndrome disproportionately affects those working in under-resourced and diverse educational contexts by limiting their ability to foster inclusive and equitable learning. To this situation, art museums respond as pedagogical spaces for wellbeing while contributing to socially just and sustainable arts education. School teachers are offered new opportunities for ongoing professional development tailored to their well-being needs, such as burnout prevention. A two-round international Delphi study with experts from universities, schools, museums, and arts-and-wellbeing organizations (n = 26 1st round, n = 17 2nd round)—rather than focusing on teachers’ personal accounts—develops consensus on a pedagogical framework for art-based programs designed to prevent teacher burnout and enhance wellbeing. The findings identify nine pedagogical guidelines highlighting participatory approaches—audience, objectives, content, methodology, scheduling, facilitators, activities, evaluation, and program adherence. By positioning art museums as democratic, inclusive, and relational spaces, the framework advances the role of the arts in addressing systemic challenges in education, such as supporting teachers’ wellbeing. This research contributes to the international debate on socially just arts education by demonstrating how teacher wellbeing can be fostered through innovative, evidence-based museum practices aligned with SDG 4. Full article

Background: Aging populations make expanded workforce participation among older adults an economic and public health priority. The COVID-19 pandemic accelerated the growth of virtual work, providing new opportunities for healthy aging in the workplace through increased flexibility and less physical strain. However, digital exclusion, ergonomically challenging tasks, and social isolation can limit these opportunities for older populations. Objective: This scoping review aimed to synthesize interdisciplinary research on the relationship between remote work and labor force participation among adults aged 45 years and older, focusing on health-related outcomes, barriers, and facilitators. Methods: Following the JBI Manual for Evidence Synthesis and PRISMA-ScR guidelines, we conducted a comprehensive search across seven databases for peer-reviewed and gray literature published between 2000 and 2025. Of 2108 records screened, 33 studies met the inclusion criteria. Data were extracted using a standardized charting tool and analyzed thematically. Results: Most studies were published after 2020 and originated in North America (45%) and Europe (40%). Core barriers included digital exclusion, ageism, and adverse ergonomic environments. Facilitators involved flexible working hours, a supportive organizational environment, and digital skills. Health-related outcomes such as stress reduction and improved well-being were commonly reported. However, only 18% of studies assessed policy effects, and very few examined intersectionality (e.g., gender, socioeconomic status). Conclusions: Remote and flexible work options can improve the health and participation of older adults in the workforce, but technology, infrastructure, and social barriers remain. Age-inclusive policies, digital equity efforts, and inclusive workplace practices are necessary to maximize the benefits of remote arrangements for aging populations. Full article

Aiming at the drawbacks of the classic CoCrFeNi high-entropy alloy (HEA)—low room-temperature strength and softening above 600 °C, which fail to meet strict material requirements in high-end fields like aerospace—this study used the vacuum hot-pressing sintering process to prepare CoCrFeNiTa_x HEAs (x = 0, 0.5, 1.0, 1.5, 2.0 atom, designated as H4, Ta_0.5, Ta_1.0, Ta_1.5, Ta_2.0, respectively). This process effectively inhibits Ta segregation (a key issue in casting) and facilitates the presence uniform microstructures with relative density ≥ 96%, while this study systematically investigates a broader Ta content range (x = 0–2.0 atom) to quantify phase–property evolution, differing from prior works focusing on limited Ta content or casting/spark plasma sintering (SPS). Via X-ray diffraction (XRD), scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), microhardness testing, and room-temperature compression experiments, Ta’s regulatory effect on the alloy’s microstructure and mechanical properties was systematically explored. Results show all alloys have a relative density ≥ 96%, verifying the preparation process’s effectiveness. H4 exhibits a single face-centered cubic (FCC) phase. Ta addition transforms it into a “FCC + hexagonal close-packed (HCP) Laves phase” dual-phase system. Mechanically, the alloy’s inner hardness (reflecting the intrinsic property of the material) increases from 280 HV to 1080 HV, the yield strength from 760 MPa to 1750 MPa, and maximum fracture strength reaches 2280 MPa, while plasticity drops to 12%. Its strengthening mainly comes from the combined action of Ta’s solid-solution strengthening (via lattice distortion hindering dislocation motion) and the Laves phase’s second-phase strengthening (further inhibiting dislocation slip). Full article

With the increasing number of patients with Parkinson’s disease, the detection of Parkinson’s disease is crucial for the early intervention and treatment of this condition. The motor characteristics of Parkinson’s disease primarily include typical motor features. Flexible pressure sensor arrays, due to their unique mechanical properties and biocompatibility, have shown great potential for capturing movement characteristics. This research aims to develop a deep learning model based on foot pressure data for the detection of Parkinson’s disease. By collecting the pressure data of patients during walking and analyzing the distribution of foot pressure, the model can capture the unique biomechanical characteristics of Parkinson’s disease patients. To address the core challenges of spatial irregularity and data disorder in footprint data, we propose an innovative approach that leverages the Transformer-based attention mechanism and tensor fusion technique to enable accurate identification of Parkinson’s disease. This attention mechanism has inherent permutation invariance, which is highly suitable for the feature learning of footprint data. The tensor fusion technique can effectively integrate the foot features at different levels. A large-scale dataset of foot pressure data was used for training and validation. The experimental results show that the model achieves a high accuracy of 87.03% and good stability in Parkinson’s disease detection, enabling effective differentiation between patients and healthy individuals. On the one hand, our work is critical for analyzing pressure data and fusion features from large-area flexible force-sensitive sensors, which enables the accurate identification of foot data. On the other hand, it greatly facilitates gait analysis, gait evaluation, and the diagnosis of Parkinson’s disease. Full article

Globally, intensive groundwater extraction has led to widespread land subsidence, posing severe threats to urban infrastructure, structural safety, and flood control capacity, and resulting in substantial economic losses and ecological degradation. Based on dynamic monitoring data and a poroelastic fluid–solid coupling model developed using COMSOL Multiphysics 6.2, this study systematically investigates the characteristics and evolution of land subsidence in Decheng District before and after the implementation of a groundwater extraction ban. Furthermore, recommendations and strategies for the sustainable management of regional groundwater resources are proposed. The results indicate that after the ban was enforced in 2020, the extraction volumes of deep and shallow groundwater in Decheng District decreased from 830,000 m³/a and 33,070,000 m³/a to 178,000 m³/a and 20,775,000 m³/a, respectively. The ban significantly influenced groundwater levels, with the recovery rate of deep groundwater increasing markedly from approximately 0.5 m/a before the ban to about 5 m/a afterward. Groundwater levels directly govern the rate of land subsidence; their decline increases the effective stress within the strata, leading to aquifer compaction and subsequent subsidence. Following the ban, the subsidence rate in Decheng District decreased significantly, with the annual subsidence volume reduced by more than 80% compared to the pre-ban period. Predictive analysis using the fluid–solid coupling model reveals that extraction from deep confined aquifers is the main driver of regional subsidence, with a time lag of approximately five years between groundwater level changes and subsidence response. After the implementation of the extraction ban, the subsidence rate slowed considerably. Over the long term, the subsiding strata tend to stabilize, although most of the subsidence that has already occurred is irreversible, making it difficult for the strata to return to their original state. In summary, the groundwater extraction ban has effectively facilitated groundwater recovery and mitigated land subsidence in Decheng District, though the response exhibits both temporal lag and spatial variability. Future work should focus on establishing an integrated monitoring and regulation system for land subsidence and groundwater dynamics to ensure the coordinated security of both water resources and the geological environment. These findings provide a scientific basis for informing land subsidence prevention and guiding the rational exploitation of groundwater resources in Decheng District. Full article

Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article

Background: The combination of multi-omics data, including genomics, transcriptomics, and epigenomics, with medical imaging modalities (PET, CT, MRI, histopathology) has emerged in recent years as a promising direction for the advancement of precision oncology. Many researchers have contributed to this domain, exploring the multi-modality aspect of using both multi-omics and image data for better cancer identification, subtype classifications, cancer prognosis, etc. Methods: We present an umbrella review summarizing the state of the art in fusing imaging modalities with omics and artificial intelligence, focusing on existing reviews and meta-analyses. The analysis highlights early, late, and hybrid fusion strategies and their advantages and disadvantages, mainly in tumor classification, prognosis, and treatment prediction. We searched review articles until 25 May 2025 across multiple databases following PRISMA guidelines, with registration on PROSPERO (CRD420251062147). Results: After identifying 56 articles from different databases (i.e., PubMed, Scopus, Web of Science and Dimensions.ai), 35 articles were screened out based on the inclusion and exclusion criteria, keeping 21 studies for the umbrella review. Discussion: We investigated prominent fusion techniques in various contexts of cancer types and the role of machine learning in model performance enhancement. We address the problems of model generalizability versus interpretability within the clinical context and argue how these multi-modal issues can facilitate translating research into actual clinical scenarios. Conclusions: Lastly, we recommend future work to define clearer and more reliable validation criteria, address the need for integration of human clinicians with the AI system, and describe the trust issue with AI in cancer care, which requires more standardized approaches. Full article

Undergraduate life science education faces high attrition rates, especially among students from underrepresented groups. These disparities are often linked to differences in prior knowledge, self-efficacy, and interest, which are rarely addressed in traditional lecture-based instruction. This work explores the use of machine learning-based Intelligent Tutoring Systems (ITSs) to support personalized instruction in biology education by examining stochasticity in molecular systems. Accordingly, we developed and validated a Random Forest classification model and used it to assign instructional materials based on students’ prior knowledge and interests. We then applied the model in an introductory biology classroom and individually estimated the most promising instructional format. Results show that the most effective instruction can be reliably predicted from student performance and interest profiles, and model-based assignments may help reduce pre-existing opportunity gaps. Thus, machine-learning-driven instruction holds promise for enhancing equity in life science education by aligning materials with students’ needs, potentially reducing differences in achievement, self-efficacy, and cognitive load, which might be relevant to promoting underrepresented students. To facilitate a straightforward implementation for educators facing similar challenges associated with teaching molecular stochasticity, we developed an open-access ITS tool and provided a scalable approach for developing similar personalized learning tools. Full article

Implantable technologies targeting the cerebral cortex and deeper brain structures are increasingly utilised in human–machine interfacing, advanced neuroprosthetics, and clinical interventions for neurological conditions. These systems require highly efficient and low-power methods for exchanging information between the implant and external electronics. Traditional approaches often rely on inductively coupled data transfer (ic-DT), where the same coils used for wireless power are modulated for communication. Other designs use high-frequency antenna-based radio systems, typically operating in the 401–406 MHz MedRadio band or the 2.4 GHz ISM band. A promising alternative is intrabody communication (IBC), which leverages the bioelectrical characteristics of body tissue to enable signal propagation. This work presents a theoretical investigation into two schemes—inductive coupling and galvanically coupled IBC (gc-IBC)—as applied to cortical data links, considering frequencies from 1 to 10 MHz and implant depths of up to 7 cm. We propose a hybrid solution where gc-IBC supports data transmission and inductive coupling facilitates wireless power delivery. Our findings indicate that gc-IBC can accommodate wider bandwidths than ic-DT and offers significantly reduced path loss, approximately 20 dB lower than those of conventional RF-based antenna systems. Full article

Nature has long served as a prolific source of bioactive compounds, offering structurally diverse scaffolds for the development of therapeutics. In recent years, increasing attention has been given to nature-inspired covalent inhibitors, molecules that form covalent bonds with pathogen- or cancer-specific targets, due to their potential selectivity and sustained biological activity. This review explores the landscape of covalent inhibitors derived from natural sources, with a focus on compounds from fungi, marine organisms, bacteria and plants. In particular, emphasis is placed on the molecular mechanisms through which these compounds exert their activity against different types of pathogens and other biomedically relevant targets, highlighting key structural motifs that facilitate covalent interactions. Furthermore, the review discusses recent advances in synthetic modification, target identification, and optimization strategies that bridge natural compound discovery with modern drug development. By drawing insights from nature’s chemical repertoire, this work ultimately displays the potential of natural covalent inhibitors as a promising foundation for next-generation anti-infective and anticancer therapeutics. Full article

Quantum energy teleportation (QET) has been proposed to overcome the restrictions of strong local passivity (SLP) and to facilitate energy transfer in quantum systems. Traditionally, QET has only been considered under strict constraints, including the requirements that the initial state be the ground state of an interacting Hamiltonian, that Alice’s measurement commute with the interaction terms, and that entanglement be present. These constraints have significantly limited the broader applicability of QET protocols. In this work, we demonstrate that SLP can arise beyond these conventional constraints, establishing the necessity of QET in a wider range of scenarios for local energy extraction. This leads to a more flexible and generalized framework for QET. Furthermore, we introduce the concept of a “local effective Hamiltonian,” which eliminates the need for optimization techniques in determining Bob’s optimal energy extraction in QET protocols. As an additional advantage, the amount of energy that can be extracted using our new protocol is amplified to be 7.2 times higher than that of the original protocol. These advancements enhance our understanding of QET and extend its broader applications to quantum technologies. To support our findings, we implement the protocol on quantum hardware, confirming its theoretical validity and experimental feasibility. Full article

Engagement in everyday creative activities is widely considered to be a good way to develop creative thinking ability. Perhaps, by engaging in such activities, creators can learn at which point their work could be acknowledged as novel and useful by peers or experts in the field. However, little is known about the underlying mechanisms of this development process. So, our study aimed to examine the mediating effect of self-rated originality on the relationship between everyday creative activities and AI-rated originality in divergent thinking. In our dataset, the indirect effect of everyday creative activities on AI-rated originality was significant, whereas the direct effect was not significant, indicating full mediation. These results revealed that engaging in everyday creative activities did not directly enhance AI-rated originality but instead indirectly contributed to AI-judged originality through students’ generation of self-perceived original ideas. Our empirical findings will open the door to a better understanding of how incorporating students’ self-evaluations into creative education might facilitate the transition from creative activities to creative thinking, and eventually to domain-specific creative achievements. Full article

This study addresses the critical issue of environmental pollution from plastic waste by investigating an effective chemical recycling method for polyethylene terephthalate (PET) via neutral catalytic hydrolysis. We utilized a recoverable and regenerable composite catalyst based on cracking zeolite and γ-Al₂O₃, which possesses both Brønsted and Lewis acidic sites that facilitate the depolymerization of PET into its constituent monomers, terephthalic acid (TPA) and ethylene glycol (EG). This investigation reveals that the catalytic performance is strongly dependent on the total acid site concentration and the specific nature of these sites. A key finding is that a balanced acidic profile with a high proportion of Brønsted acid sites is crucial for enhancing PET hydrolysis attributed to a significant decrease in the activation energy of the reaction. The experiments were conducted in a stirred stainless-steel autoclave reactor, where key parameters such as temperature (210–230 °C), the PET-to-water ratio (1:2 to 1:5), and reaction time were systematically varied. Under optimal conditions of 210 °C and a 6 h reaction time, the process achieved near-complete PET depolymerization (99.5%) and a high TPA yield (90.24%). The catalyst demonstrated remarkable recyclability, maintained its activity over multiple cycles and was easily regenerated. Furthermore, the recovered TPA was of high quality, with a purity of 98.74% as confirmed by HPLC, and exhibited a melt crystallization temperature 14 °C lower than that of the commercial standard. These results not only demonstrate the efficiency and sustainability of neutral catalytic hydrolysis using zeolite/alumina composites but also provide valuable insights for designing advanced catalysts with tunable acidic properties. By demonstrating the importance of tuning acidic properties, specifically the balance between Brønsted and Lewis sites, this work lays a foundation for developing more effective catalysts that can advance circular economy goals for PET recycling. Full article

Work investigates the doping of molybdenum oxide (MoO_x) with tungsten (W). The successful incorporation of W into the MoO_x lattice was confirmed through X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDS). Structural and optical analysis revealed the presence of oxygen vacancies within the W-MoO_x film, which are known to facilitate resistive switching (RS) in memristive devices. Based on this, a flexible memristor with the structure PET/ITO/W-MoO_x/polymethyl methacrylate (PMMA)/Al was fabricated. PMMA was strategically introduced between the W-MoO_x layer and the aluminum electrode to modulate interfacial properties that influence RS behavior. The W-MoO_x/PMMA-based memristor exhibited good resistive switching characteristics, with a memory window of approximately 12 and a retention time exceeding 2 × 10⁴ s, demonstrating a non-volatile memory behavior. In the high-resistance state (HRS), the conduction mechanism under higher applied voltages follows a space-charge-limited current (SCLC) model, indicating that the RS process is primarily governed by charge trapping and de-trapping at the interface. Overall, the consistent and robust switching performance of the W-MoO_x/PMMA heterostructure underlines its potential as a reliable functional layer for next-generation resistive random-access memory (ReRAM) devices. Full article

Low-cost missions are ideal for applications that require spacecraft formation flying. The use of GNSS signals provides an economical solution to determine the orbital status of the formation. This paper facilitates the development of such missions by simulating spacecraft orbital formation conditions through the use of software-defined radio to generate the GNSS signals being received by each spacecraft. The simulation environment integrates low-cost commercial GNSSs, one for each member of the formation, to capture the signals generated. The analysis of the recorded raw signals shows that the instrumental error of the receivers is predominant because they have not been designed to work in orbital conditions. In addition to noise, the bias errors introduced must be taken into account by the mathematical trilateration methods, which can be very sensitive to these errors. This paper shows how sensitivity can be quantified using the condition number for matrix inversion. A condition number analysis determines that the optimal solution for trilaterating the orbital position of a spacecraft should use as few GNSS satellites as possible. The paper also introduces how to use the condition number to evaluate different methods for determining the state of the spacecraft formation: the independent trilateration method, the difference method, and the double difference method. The comparison of the methods shows that the difference and double difference methods are more sensitive to instrumental errors, because they are worse conditioned, but can be improved by reducing their order. Despite the limitations shown, at best, errors in the relative positions of the spacecrafts of the order of metres are obtained, demonstrating the feasibility of this type of mission and the usefulness of the condition number analysis method presented. Full article