Search Results (2,345)

Developmental dyslexia is a heterogeneous disorder that has been associated with deficits in various cognitive domains, such as memory and attention. The aim of the present study was to investigate possible deficits in memory and attention in students with developmental dyslexia. The sample consisted of 50 students (mean age 10.5 years), including 25 students diagnosed with dyslexia and 25 typically developing controls matched for age and gender. Participants were assessed using tests of short-term phonological memory, long-term memory, working memory, immediate verbal memory, auditory and visual memory, as well as auditory and visuospatial attention. The results revealed that students with dyslexia exhibited statistically significant deficits in all memory tests. In the attention domain, statistically significant deficits were observed in the visuospatial attention test but not in the auditory attention test. These findings support multiple-deficit models of dyslexia and suggest that memory and attention impairments may collectively contribute to the understanding of the cognitive profile of students with developmental dyslexia. Full article

Prosocial behavior is a core element of social harmony, and implicit prosocial attitudes, which may outperform explicit assessments in predicting real-world behavior, underscore their unique utility in prosocial and moral research contexts. Moreover, red songs, a distinctive musical form emerging in specific revolutionary and developmental periods of China, align with this prosocial potential, as they are characterized by lyrics advocating patriotism, collective memory, and emotional resonance. However, the specific effect of red songs on implicit prosocial attitudes, as well as the potential moderating role of music training experience in this relationship, remains underexplored. This study aimed to explore whether red songs enhance implicit prosocial attitudes compared to neutral songs, whether music training modulates this effect, and the underlying neural correlates using the Single-Category Implicit Association Test (SC-IAT) and event-related potentials (ERPs). A mixed-factorial design was used with 60 college students (30 with ≥5 years of music training, 30 without). Participants completed the SC-IAT (measuring implicit prosocial D-scores) while EEG data were recorded, while listening to red (“China in the Lantern Light”) and neutral (“Lake Baikal”) songs. ERP components (N1, P2, N3, LPCs) were analyzed. Behaviorally, no significant main effects of song type or music training were observed, but a significant interaction emerged (F(1, 58) = 4.09, p = 0.04): the music training group showed higher D-scores under red songs (M = 0.35, SD = 0.32) than neutral songs (M = 0.15, SD = 0.51), while the non-music training group exhibited the opposite non-significant trend. Neurally, repeated measures ANOVAs revealed a significant main effect of electrode site for N1 (F(4, 212) = 48.63, p < 0.001, partial η² = 0.48), with the largest amplitudes at FCz. Red songs elicited larger N1 amplitudes than neutral songs at Fz and FCz, and incongruent trials elicited larger N1 amplitudes at Pz. For P2, a main effect of condition was found (F(1, 52) = 7.02, p = 0.01), with larger amplitudes in incongruent trials, and a significant three-way interaction of song type, condition, and electrode site (F(4, 208) = 4.46, p = 0.006), with larger P2 amplitudes for red songs under incongruent trials at Fz. For N3, main effects of song type (F(1, 53) = 14.48, p < 0.001) and stimulus type (F(2, 106) = 8.32, p = 0.001) were observed; congruent trials elicited larger N3 amplitudes than incongruent trials at Fz and FCz. For LPCs, main effects of song type (F(1, 53) = 4.89, p = 0.03) and electrode site (F(4, 212) = 3.05, p = 0.047) were found, with the largest amplitudes at Pz and the smallest at FCz. Red songs enhance implicit prosocial attitudes specifically among individuals with music training, and are accompanied by multi-stage neurocognitive differences. These findings highlight the conditional effects of red songs and inform prosocial education. Full article

This article examines the interplay of memory, politics, and religion at the Buchenwald Memorial, focusing on the 2024 edition of the Genius Loci festival. Once staged by the German Democratic Republic as a monumental site of antifascist resistance, the memorial has undergone multiple reinterpretations, reflecting shifting regimes of remembrance and contested political claims, and an architectural vocabulary informed by Christian metaphors. Drawing on Durkheim’s sociology of religion and concepts of memory (Nora, Assmann), the analysis highlights how memorial architecture, ritual practices, and artistic interventions frame collective memory as both a political resource and a civic challenge. The Genius Loci festival exemplifies how contemporary art can reactivate debates around memorial spaces, exposing their religious frame of reference while simultaneously opening them to contemporary renegotiation. Full article

Bacopa monnieri (L.) Wettst. (Family: Scrophulariaceae) is a well-known edible plant used in ethnic and Ayurveda medicine for centuries to improve memory deficit, enhance cognitive function, and treat nervous system disorders. Despite accumulating in vivo evidence for its cognitive benefits, the detailed mechanisms by which its bioactive compounds act on primary neurons remain elusive. In the present study, we dissect the mechanism by which Bacopa monnieri promotes neuronal development by treating primary hippocampal neuronal cultures with its ethanolic extract (BMEE) and integrating insights from in silico network pharmacology. We identified that BMEE at different concentrations promotes neuritogenesis and has a remarkable impact on early neuronal maturation, and axonal and dendritic outgrowth. Also, BMEE regulated synaptic plasticity by increasing the expression of NMDA receptors. Metabolites of BMEE were identified by gas chromatography–mass spectrometry (GC-MS) analysis, from which a network pharmacology model was constructed, in which BMEE metabolites were projected to regulate the neurotrophin signaling pathway. Indeed, the BMEE-mediated neuritogenic effect was abolished by the presence of a TrkA receptor-specific inhibitor, suggesting that the neuritogenic effect of BMEE is TrkA-dependent. Also, molecular docking following MD simulations supported the idea that BMEE metabolites, particularly δ-Tocopherol and O-methyl-, bind with high affinity to the TrkA receptor (NGF-binding domain). This study collectively illuminates the TrkA-mediated pathway through which Bacopa monnieri promotes neuronal development and suggests that bioactive metabolites from BMEE might hold potential as a source for designing therapeutic agents for various cognitive disorders. Full article

Background/Objective: Older patients with end-stage renal disease who receive dialysis often discontinue treatment before the end of their lives. However, the trajectory of family caregiving in this specific context remains under-researched. This study explored the experiences of family members caring for older patients with end-stage kidney disease (ESKD), from the introduction of dialysis to end-of-life care. Methods: This qualitative descriptive study included three family members caring for older patients with end-stage renal disease who were undergoing dialysis in Japan. Data were collected through semi-structured, one-on-one interviews and analyzed using inductive qualitative content analysis within a qualitative descriptive design. Results: The results identified seven categories regarding the family’s experience from dialysis initiation to end-of-life care: Key findings, particularly regarding the terminal phase, included ‘shock of dialysis treatment discontinuation’, ‘last moments shared with the patient’, ‘nostalgic memories of the patient over time, and ‘reflections on end-of-life care for the patient.’ Families described a process wherein the sudden need for proxy decision-making, often without prior discussion, was linked to feelings of regret. Conclusions: The findings describe the continuous experiences of family caregivers in the Japanese context. These exploratory insights suggest that the absence of early Advance Care Planning may contribute to caregiver distress during the withdrawal phase. The results highlight the need for culturally sensitive renal supportive care that fosters communication and understanding of patients’ wishes to mitigate the ethical burdens on families. Full article

Industrial heritage in resource-depleted mining towns faces the dual challenge of physical decay and social severance. To achieve sustainable urban revitalization, adaptive reuse strategies must align with local collective memory and emerging experiential consumption trends. Adopting a Scene Theory perspective, this study constructs a multi-level analytical framework using Meitanba Town (Hunan, China) and its power plant as a case study. A mixed-methods approach was employed, combining semantic network analysis of 1582 online user comments with 61 offline questionnaires distributed to local residents to quantitatively diagnose current scene elements, functions, and features. The quantitative results reveal a significant imbalance: while “Functional Media” achieved the highest comprehensive score (10.0) due to strong historical recognition, “Diverse Groups” scored the lowest (3.4), indicating a lack of social inclusivity. Specifically, residents expressed the highest demand for sports facilities (31.2%) and cultural spaces (23.7%), identifying the main workshop (26.4%) and chimney as core carriers of industrial identity. Responding to these findings, the paper proposes three targeted strategies: (1) Activate: creating open-access recreation scenes to satisfy urgent sports demands; (2) Link: constructing immersive cultural scenes to narrate the “coal–electricity–life” history; and (3) Enhance: developing industry-powered commercial scenes to avoid homogenization. This study enriches the localized application of Scene Theory and provides a data-driven, context-adjustable analytical and strategic model that can inform the sustainable renewal of mining towns globally, with its specific implementation requiring adaptation to local social, economic, and cultural characteristics. Full article

Fluid mechanics in disordered structures gives rise to rich multiscale dynamics through the interplay of topology, symmetry breaking, and fluid–structure interactions. Heterogeneous networks encode mechanical responses, regulate flow organization, and shape energy dissipation, enabling memory effects and emergent collective behaviors across both natural and engineered systems. These principles operate across vast scales: from seamounts with characteristic scales of $L\approx {10}^3\mathrm{m}$ and Froude numbers $Fr\approx {10}^{-2}–{10}^{-1}$ generating deep-ocean turbulent mixing, to marine tidal turbines operating at Reynolds numbers $Re\approx {10}^7–{10}^8$ and Euler numbers $Eu\approx {10}^{-1}–{10}^0$, where inertial forces dominate flow dynamics. Although the dominant physical forces may vary across scales—for example, planetary rotation and stratification in large-scale oceanic flows versus viscous or interfacial effects in microscale systems—the comparison of dimensionless parameters provides a useful framework for discussing similarities in flow organization and scaling behavior. Empirical observations, network-based descriptions, and multiscale simulations collectively demonstrate how topological features constrain symmetry, organize transport pathways, and support predictive reconstruction and inverse design. These principles underpin applications ranging from engineered systems that exploit broken symmetries to rectify chaotic transport, to biological architectures where flows mediate information transfer, locomotion, and structural self-organization. In this Review, we synthesize recent advances to propose a unifying physical paradigm: fluid flows actively interact with disorder, reorganize dissipation, and convert structural asymmetry into functional mechanical performance across scales. Full article

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article

Saudade is a cultural concept expressing a profound sense of longing, nostalgia, or melancholy associated with absence, loss, or unattainable experiences. Emerging in medieval Portugal and shaped by historical, social, and literary developments, it has evolved from an individual emotion into a collective cultural construct reflecting the identity, history, and aesthetic sensibilities of Lusophone communities. Drawing on peer-reviewed scholarship and interdisciplinary research in cultural studies, this entry examines how saudade is expressed in the literature, music, and philosophical discourse, and its role in national memory, emigration, and cultural imagination. While sometimes described as untranslatable, its uniqueness reflects deep historical and cultural embedding rather than a linguistic limitation. Saudade, therefore, functions as a multilayered symbolic category, revealing the interplay between emotion, language, and cultural identity in Lusophone contexts. Full article

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs. Full article

Background: Despite universal infant hepatitis B virus (HBV) vaccination, declining circulating anti-HBs levels are increasingly observed in young healthcare professionals, a high-risk group for occupational exposure. Although several studies have evaluated HBV antibody persistence in healthcare workers, data specifically addressing newly incorporated medical residents in the Spanish context remain limited. This study evaluated baseline anti-HBs levels and serological response to a vaccination booster dose in medical residents at a Spanish tertiary hospital. Methods: A retrospective longitudinal observational study was conducted among medical residents attending the Preventive Medicine Service of Hospital Universitario San Cecilio (Granada, Spain) between 2021 and 2024. Anti-HBs antibody titers were obtained at baseline and ≥10 mIU/mL were considered the conventional protective threshold. Residents with anti-HBs < 10 mIU/mL received an Engerix-B booster followed by repeat serology. Demographic and occupational variables were analyzed. Measles serostatus was collected for comparisons. Results: A total of 275 residents were included (mean age 25.4 years, SD = 2.3 years; 64% females). Baseline serology showed anti-HBs levels < 10 mIU/mL in 53.1% of participants. Lower baseline anti-HBs levels were associated with younger age (adjusted OR = 0.75; 95% CI: 0.64–0.88) and earlier residency year (R1–R2) (adjusted OR = 0.28; 95% CI: 0.13–0.61). Among 116 residents receiving a booster, 94.8% achieved anti-HBs ≥ 10 mIU/mL after booster administration. Measles serology was negative in 54.6% of participants. Conclusions: More than half of newly incorporated medical residents had anti-HBs levels below the conventional protective threshold (10 mIU/mL), yet almost all demonstrated a strong anamnestic response, supporting the persistence of immunological memory despite reduced circulating antibody concentrations. Systematic baseline screening combined with targeted booster vaccination appears to be an effective strategy to ensure occupational protection. Further research incorporating cellular immunity markers may refine future vaccination policies and booster strategies. Full article

Electroencephalography (EEG) has attracted growing interest as a biometric modality because it reflects ongoing brain activity and is inherently difficult to counterfeit. At the same time, EEG signals are influenced by internal conditions such as emotions, which may affect identification stability, particularly when recordings are obtained using portable consumer-grade systems. This study examines how emotional states influence EEG-based biometric performance and evaluates deep learning architectures to determine an effective modeling approach for cross-emotion robustness. EEG data were collected from 65 participants using a 14-channel Emotiv EPOC X headset, with 54 subjects retained after self-reported emotional validation. Recordings were acquired under neutral, positive, and negative visual stimuli. To address variability associated with portable acquisition, preprocessing made use of the device’s internal signal quality metrics to select reliable segments, compensate for degraded regions, and reduce noise. Among the evaluated models, a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced with Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA) achieved highest performance in our experiments. The model was trained on neutral-state data and subsequently evaluated under emotional conditions. It reached 95.91% accuracy in the neutral condition and maintained high performance under positive (94.31%) and negative (92.99%) states. Despite a modest decline under negative stimuli, identification performance remained stable. These findings support the feasibility of robust EEG-based biometric authentication using consumer-grade devices in realistic settings. Full article

Structural symmetry appears as a natural feature in both optimal solution landscapes and hospital scheduling behaviors, representing an inherent balance that can be deliberately leveraged to improve how quickly algorithms converge and how reliably systems perform in intricate healthcare optimization contexts. Managing hospital resources is a multifaceted challenge that requires simultaneously addressing several competing goals, such as reducing costs, improving patient experiences, making the most of available resources, distributing staff workload fairly, and strengthening readiness for emergencies. Traditional optimization approaches frequently struggle to cope with the complexity and ever-changing nature of modern healthcare environments. To address this gap, this study introduces a novel Multi-Objective Dream Optimization Algorithm (MO-DOA) tailored for smart healthcare resource management, which adapts a biologically inspired optimization framework to meet the specific demands of healthcare settings. The MO-DOA is built around three core mechanisms: a foundational memory component that retains high-quality solutions, a forgetting-supplementation component that maintains a productive balance between exploration and exploitation, and a dream-sharing component that promotes diversity among candidate solutions. Rigorous testing across realistic hospital environments confirms MO-DOA’s outstanding effectiveness, with results showing a 21.86% gain in resource utilization, a 30.95% decrease in patient waiting times, a 19.06% boost in patient satisfaction, and a 29.56% improvement in how evenly staff workloads are distributed. The algorithm’s emergency response capabilities are especially noteworthy, achieving bed assignments within 4.23 min and an equipment deployment success rate of 94.56%. Computationally, the algorithm proves highly efficient, with an average response time of 18.87 s and strong scalability across different operational scales. Collectively, these findings position MO-DOA as a powerful and practical tool for optimizing hospital operations in real time. Full article

Cognitive Learning Analytics (CLA) is an interdisciplinary domain that combines cognitive science and learning analytics to interpret and enhance human learning through theoretically grounded data analysis. It integrates learning analytics with models of cognition to support theoretically grounded interpretation of learner data. Learning analytics, since its inception in 2011, has developed as a research field and applied practice, focusing on “the measurement, collection, analysis, and reporting of data about learners and their contexts.” It focuses on understanding and optimizing learning processes and environments by leveraging large-scale, multimodal educational data. Cognitive science, in parallel, provides established theories of human learning, memory, attention, and metacognition. CLA links observable behaviors with theoretically defined cognitive mechanisms. Through the integration of cognitive theories and computational techniques, CLA models how learners process information, make decisions, and construct knowledge in digital learning environments. CLA employs diverse data sources—including clickstream logs, eye tracking, biometric signals, and linguistic traces—to infer learners’ cognitive and affective states. These inferences inform adaptive learning systems, personalized feedback mechanisms, and intelligent tutoring tools that respond dynamically to the learner’s mental workload, engagement, or metacognitive strategies. Full article

As a critical actuator in aero-engine control systems, the health condition of the Fuel Metering Unit (FMU) directly influences flight safety and maintenance efficiency, making the precise prediction of its degradation process a core task in the engine’s Prognostic and Health Management (PHM). This paper presents a novel inverted stabilized LSTM-Transformer (isLTransformer) approach for predicting the health state of aero-engine FMUs, addressing the limitations of existing methods in modeling long-sequence multivariate data. Firstly, a Composite Health Indicator (CHI) is constructed through semi-supervised learning (SSL), which fuses multi-sensor monitoring data to quantitatively characterize the degradation trend of the FMU throughout its operational lifecycle. Secondly, the proposed isLTransformer model is designed by replacing the feedforward network in traditional iTransformer with a stabilized LSTM module, which maintains the self-attention mechanism’s capability to explicitly model dynamic correlations between multiple variables while enhancing the ability to capture nonlinear degradation within individual variables. A physical FMU test bench is designed for the real-world PHM degradation experiments, and the collected dataset was used to demonstrate the effectiveness of the proposed method. Evaluation metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are employed to assess the prediction accuracy. The proposed method demonstrates high monotonicity and trend consistency in CHI construction. Compared to the inverted Transformer (iTransformer) and iTransformer- Bi-directional Long Short-Term Memory (BiLSTM), the proposed isLTransformer framework demonstrates significantly reduced prediction errors, validating its superiority in multivariate long-sequence prediction tasks and effectiveness for aero-engine FMU health prediction. Full article