Search Results (8,539)

Background/Objectives: For low-grade glioma (LGG) patients, adjuvant treatment (AT) with radiotherapy and chemotherapy may adversely impact cognition. However, existing evidence is limited by methodological heterogeneity and shortcomings. This study explored the cognitive functioning of LGG patients who underwent resection with both radiotherapy and chemotherapy (AT+) or without AT (AT−), from before resection to one year after resection. Methods: We included patients with World Health Organization 2021 grade 2 isocitrate dehydrogenase-mutated glioma who underwent resection between 2011 and 2024. All patients completed a neuropsychological screening battery one week before (T0) and twelve months after resection (T12), measuring reaction time, attention span, information processing speed, working memory, inhibition, cognitive flexibility, and verbal fluency. We compared cognitive performance between AT+ and AT− patients at T0 and T12, as well as trajectories of cognitive functioning, at the group and individual level. Results: We included 60 LGG patients (M age = 38.8 years; 63.3% male). Compared to AT− patients (n = 35), AT+ patients (n = 25) were significantly older, more frequently had tumors that crossed the midline, and reported more depressive symptoms. At T0, no significant cognitive performance differences existed between AT+ and AT− patients, despite lower observed performance in the AT+ group. At T12, AT+ patients performed significantly worse than AT− patients on mean information processing speed, due to an improvement over time in the AT− group. Conclusions: Patients allocated to AT may show limited cognitive recovery of information processing speed up to 12 months after surgery, without pronounced effects on other cognitive functions. These findings can guide future studies into treatment-related cognitive decline of LGG patients. Full article

Automatic modulation classification (AMC) is a critical enabler of cognitive radio (CR) systems, allowing secondary users to identify primary user modulation schemes and adapt transmission parameters in real time. Traditional AMC approaches, based on likelihood functions or hand-crafted features, suffer from degraded performance under low signal-to-noise ratio (SNR) conditions and realistic channel impairments. In this paper, we propose MS-SENet (Multi-Scale Squeeze–Excitation Network), a novel deep-learning architecture that integrates multi-scale convolutional feature extraction, squeeze-and-excitation channel attention, residual learning, bidirectional long short-term memory (BiLSTM) temporal modelling, and global attention pooling into a unified framework for robust AMC. The multi-scale convolution module employs parallel branches with kernel sizes of 3, 5, and 7 to capture both fine-grained phase transitions and coarse envelope patterns from raw in-phase/quadrature (I/Q) signal samples. Squeeze–excitation residual blocks perform channel-wise feature recalibration, enabling the network to emphasize informative feature maps while suppressing less relevant ones. A bidirectional LSTM layer models temporal dependencies across the signal sequence, and a global attention pooling mechanism performs weighted temporal aggregation prior to classification. We present a comprehensive taxonomy of deep-learning architectures for AMC organised along five axes—input representation, feature extraction, temporal modelling, regularization strategy, and architectural complexity—and conduct a rigorous comparative evaluation against ten baseline architectures on a RadioML-style synthetic dataset (110,000 samples, 11 modulation classes, and 20 SNR levels from −20 to +18 dB). The experimental results demonstrate that MS-SENet achieves a mean classification accuracy of 87.9% at SNR ≥ 0 dB (the average of the medium and high SNR regime averages: 86.06% for 0 ≤ SNR < 10 dB and 89.68% for SNR ≥ 10 dB) while maintaining a compact footprint of approximately 406 K parameters, making it suitable for deployment on resource-constrained edge devices. We further analyze the robustness of the proposed architecture to multipath fading, carrier frequency offset, and sample rate offset, confirming its resilience under practical operating conditions. MS-SENet is an architecture designed for automatic modulation classification of I/Q signals and is not related to the homonymous architecture for speech emotion recognition. Full article

Background: As the population ages, dementia poses a critical public health challenge. This study examined the oral health and nutritional status of institutionalized Portuguese adults with dementia, exploring their interrelated predictors. Methods: This multicenter, cross-sectional study assessed institutionalized patients using the Decayed, Missing, and Filled Teeth (DMFT) index, posterior functional units (PFUs), plaque (PI) and gingival (GI) indices, the Short Xerostomia Inventory (SXI-5), and the Mini Nutritional Assessment (MNA). DMFT was modeled using multivariable ordinary least squares (OLS) regression for demographic and clinical predictors and separate negative binomial models for medication-related predictors. Other outcomes were analyzed using outcome-specific multivariable models. Results: The study included 71 participants (mean age: 82.5 ± 6.9 years). A high dental disease burden (mean DMFT score of 24.3 ± 7.5) was observed, independently predicted by advanced age (β = 0.48, p = 0.002) and residence in public long-term care units (LTCUs) (β = 6.65, p = 0.001). Total edentulism affected 28.2% of the sample. Polypharmacy emerged as a significant predictor of tooth loss; each additional medication was associated with an 18% decrease in the likelihood of retaining natural teeth (OR = 0.82, p = 0.008). Higher cognitive decline (GDS) was associated with increased plaque (p = 0.043), and modified-texture diets were associated with lower plaque levels (β = −0.64, p = 0.021). The mean MNA score (16.9 ± 3.8) indicated a high risk of malnutrition, with a trend toward lower gingival inflammation with better nutritional status (p = 0.061). Conclusions: Institutionalized dementia patients face severe oral and nutritional risks associated with age, polypharmacy and institutional environment. This emphasizes the need for multidisciplinary protocols and caregiver training. Full article

Advancing urban sustainability transitions through effective environmental policies requires understanding how residents perceive and respond to policies. While perceived policy effectiveness (PPE) has been studied in waste management and recycling programs, its role in shaping demand for bio-based materials remains underexplored. This study investigates whether and how PPE is associated with bamboo product consumption among 1121 urban residents in Zhejiang Province, China. Drawing on an extended Theory of Planned Behavior (TPB) framework, we use ordinary least squares estimators to examine the direct and interactive associations between PPE and actual bamboo consumption behavior. Results show that PPE is significantly and positively associated with bamboo product consumption. Interaction analysis reveals heterogeneous effects: PPE shows a weak positive interaction with environmental knowledge, but a negative interaction with environmental values. This suggests that policy signals may complement cognitive preparedness while partly compensating for low value-based motivation. A supplementary analysis indicates that this conditioning extends to economic resources, with the association concentrated among lower-income, more price-sensitive consumers. This study extends PPE research from post-consumption management to the purchasing stage of sustainable products. It highlights the role of policy perceptions in shaping demand-side adoption of lower-impact materials, with implications for urban sustainability transitions and city-level policies promoting bio-based alternatives. Full article

Background/Objectives: The precise identification of Alzheimer’s disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phases of illness progression. This study presents a comprehensive and advanced diagnostic system, termed FLAME, featuring an enhanced federated learning architecture for privacy-preserving multi-institutional implementation. It provides a systematic review of machine learning (ML) and deep learning (DL) models for the classification of five stages of Alzheimer’s disease (AD). The models include cognitively normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). Methods: Sixteen traditional machine learning models and eleven deep learning architectures—including FT-Transformer and NODE—were evaluated using a structured clinical dataset comprising 362 features. A hybrid ensemble was created at the probability level by combining the two top-performing models, LightGBM and a five-layer DNN. The weights of this ensemble were automatically optimised using a Genetic Algorithm (GA) with Macro-F1 as the fitness criterion, confirmed stable across 30 independent runs ($w^{★}=0.5024\pm 0.0001$). A federated learning architecture was then established, deploying the DNN across non-IID clients while keeping LightGBM centralised. We examine four distinct aggregation algorithms: FedAvg, FedProx, FedNova, and SCAFFOLD. Results: Among all deep learning architectures, FT-Transformer achieved the highest standalone performance (accuracy = 0.7810, $\kappa$ = 0.7081). The five-layer deep neural network (DNN) was selected as the DL representative for the hybrid ensemble. LightGBM attained superior machine learning performance (accuracy = 0.8156, $\kappa$ = 0.7537), confirmed deterministic across 10 seeds. The LightGBM vs. XGBoost difference is not statistically significant (McNemar $p=0.4227$). The GA-optimised hybrid ensemble (w = 0.685) surpassed both individual baselines across all evaluation metrics. The FedNova hybrid design achieved superior overall performance in federated configurations, surpassing all centralised arrangements in accuracy (accuracy = 0.8213, $\kappa$ 0.7614). Conclusions: Evolutionary ensemble optimisation combined with federated learning provides a robust, scalable, and privacy-preserving solution for AD stage classification, offering a clinically viable framework for real-world multi-institutional decision-support systems. However, the AD class remains severely under-recalled across all configurations (F1 ≤ 0.21), identifying this as the primary open challenge for clinical translation. Full article

Research on evacuation in built-up areas within corridor-dependent counties is shifting from static shelter-coverage assessment toward dynamic simulation of spatial constraints, behavioral heterogeneity, and organizational capacity. This study takes a typical built-up area within Ruoqiang County, Xinjiang, as the simulation unit, rather than the entire county administrative area. GIS-based shortest-path analysis shows that the origin-to-nearest-shelter distance ranges from approximately 0.03 km to 0.64 km, indicating a short-distance pedestrian evacuation context. Based on multi-source spatial data from 2025, this study constructs an agent-based evacuation simulation framework and positions the model as a general evacuation-capacity experiment rather than a predictive simulation of a specific hazard process. Five scenarios are compared: fully disordered, 25% ordered, 50% ordered, 75% ordered, and fully ordered evacuation. Under the ideal ordered-information assumption, the simulated system reaches complete evacuation within 9.25 min, whereas the fully disordered scenario enters a low-level plateau after approximately 4.88 min, with a final evacuation rate of about 13%. The 25%, 50%, and 75% ordered scenarios reach plateau levels of approximately 37–38%, 61–62%, and 71–72%, respectively. Origin-type results further indicate that origins near shelters, directly connected to shelters, or embedded in continuous road networks respond more strongly to improved organization, whereas origins near boundaries, in low-connectivity areas, far from shelters, or adjacent to bottleneck nodes are more likely to generate late-stage retention. This study reveals how destination cognition, route organization, and origin spatial conditions jointly shape evacuation efficiency in a typical built-up area within a corridor-dependent county under specified scenario assumptions. Full article

As resource and environmental constraints continue to tighten, corporate green transformation has become a key micro-level foundation for achieving high-quality development. Using data on Chinese A-share listed firms from 2001 to 2024, this study examines the effect of executive environmental awareness on corporate green transformation and explores the underlying mechanisms. The results show that executive environmental awareness significantly promotes corporate green transformation, and this finding remains robust across a series of robustness checks. Mechanism tests indicate that green innovation serves as an important channel through which executive environmental awareness affects corporate green transformation. This channel operates primarily through symbolic green innovation, whereas the mediating role of substantive green innovation is not significant. Further analysis shows that pressure-oriented environmental awareness has a stronger positive effect on corporate green transformation than development-oriented environmental awareness, suggesting that corporate green transformation in the current institutional context remains largely responsive to external pressures. Heterogeneity analysis further reveals that the positive effect of executive environmental awareness is more pronounced among non-state-owned firms and firms located in eastern China. This study uncovers the internal cognitive mechanism underlying corporate green transformation from the perspective of executive awareness and provides empirical evidence on how environmental awareness can be translated into green transformation practices. Full article

Background/Objectives: Persistent cognitive impairments are prevalent in methamphetamine (meth) use disorder and contribute to maladaptive decision-making and increased relapse vulnerability. There are currently no effective treatments for meth-associative cognitive deficits, and their neurobiological underpinnings remain incompletely understood. This study investigated the effects of chronic meth self-administration on episodic-like recognition memory and evaluated whether pharmacological potentiation of metabotropic glutamate receptor subtype 2 (mGlu2) could rescue these deficits. Methods: Adult male Long–Evans rats underwent 7 days of limited- (1 h/day) followed by 14 days of extended-access (6 h/day) meth self-administration, followed by 30 days of abstinence. Recognition memory was assessed using the object-in-place (OIP) task. A positive allosteric modulator of mGlu2 receptors, LY-487379 (25 mg/kg, s.c.), was administered prior to the memory test. In parallel, changes in total and surface mGlu2/3 protein levels in the prelimbic and perirhinal cortices were evaluated. Results: Rats with extended access to meth self-administration exhibited escalated drug intake and persistent deficits in OIP memory. Administration of LY-487379 acutely rescued this deficit. Total mGlu2/3 protein levels were unaltered; however, meth exposure was associated with a significant increase in surface mGlu2/3 receptor expression in both cortical regions examined. Conclusions: These results demonstrate that chronic meth produces persistent cognitive dysfunction that can be rescued by mGlu2 receptor potentiation. The observed increase in surface mGlu2/3 expression may represent a compensatory response to chronic glutamatergic dysregulation, but it appears to be insufficient to restore cognitive function alone, without pharmacological enhancement. The current data encourage further exploration of mGlu2’s role in stimulant-associated cognitive dysfunction. Full article

Football performance depends on multiple interacting factors, including physical, technical, tactical, and psychological components. Among the psychological factors associated with optimal performance are athletes’ emotional states, their regulation, and executive functions. Although executive functions and emotional states have been widely studied in sport settings, research examining the relationship between these variables in athletes is limited, particularly in female football players. The aim of this study was to explore the relationship between emotional states, emotional regulation, and performance on cognitive tasks in female players from the Mexican football league. Twenty-eight players participated in two individual assessment sessions in which anxiety and depression levels, emotional regulation, and executive functions—planning, inhibitory control, working memory, and cognitive flexibility—were evaluated using psychological and neuropsychological tests. Results indicated a positive correlation between decision-making and emotional attention (rho = 0.36; p < 0.05), as well as between depression levels and onset latency in a working memory task (rho = 0.38; p < 0.04). Finally, a negative correlation was identified between the percentage of risk cards and the TMMS attention score (rho = −0.47; p < 0.01). These findings suggest associations between emotional processes and cognitive functioning in professional female football players and warrant further investigation in sport-performance settings. Full article

Mulberroside A (MsA) possesses neuroprotective effects, but whether it alleviates Alzheimer’s disease (AD)-like cognitive impairment through the microbiota–gut–brain axis remains unclear. Using a scopolamine-induced mouse model of acute cognitive impairment (male ICR mice, n = 10/group), we demonstrated that daily administration of MsA (10, 20, and 30 mg/kg/day) for 5 weeks significantly ameliorated cognitive performance in novel object recognition and Morris water maze tests. At the optimal dose (30 mg/kg/day), MsA suppressed hippocampal microglial activation, reduced pro-inflammatory cytokines (IL-6, IL-1β, TNF-α), and attenuated oxidative stress by decreasing malondialdehyde (MDA) while restoring superoxide dismutase (SOD) and glutathione (GSH) levels. MsA also strengthened intestinal barrier integrity (ZO-1, occludin) and significantly altered the gut microbiota, notably increasing the beneficial genus Dubosiella. Brain metabolomics indicated that MsA reversed scopolamine-induced metabolic disturbances, mainly restoring phospholipid balance. Correlation analysis demonstrated a strong gut–brain connection, with Dubosiella abundance positively associated with neuroprotective phospholipids and negatively with stress markers. Furthermore, fecal microbiota transplantation from MsA-treated donors successfully replicated these behavioral improvements in recipient mice, underscoring the functional involvement of the reshaped microbiome rather than a simple autonomous recovery. These results suggest that MsA alleviates AD-like cognitive impairment by reducing neuroinflammation and oxidative stress through microbiota remodeling, enhancing the intestinal barrier, and modulating the Dubosiella-associated gut–metabolite–brain axis, making MsA a promising multi-target nutraceutical for ameliorating AD-like cognitive deficits. Full article

Background/Objectives: Older adults often experience difficulties in smartphone use, especially when digital tasks require goal maintenance, visual search, sequential action, and response verification. Working memory and parietal theta-band activity may support these cognitively demanding operations, but it remains unclear whether a single session of theta-frequency high-definition transcranial alternating current stimulation (HD-tACS), centered over CP5 as a parietal scalp location intended to approximate the left inferior parietal region, is associated with short-term changes in smartphone-task performance in aging. Methods: This study examined performance in a controlled smartphone-based shopping task and exploratory post-stimulation EEG correlates. In Experiment 1, 40 older adults were randomly assigned to active HD-tACS or sham stimulation. In Experiment 2, 28 older adults completed a reduced-trial EEG extension of the same task with electroencephalography (EEG) recording before and after stimulation. Results: Active stimulation improved smartphone-task performance, including faster completion under high cognitive load, higher target selection accuracy, and reduced difficulty–time slope. Working-memory performance on a two-back task was also improved, and individual differences in working-memory gains were associated with improvements in smartphone-task efficiency. Active HD-tACS most strongly improved target selection accuracy, and exploratory post-stimulation theta-power changes in posterior/parietal regions may have accompanied high-demand target-selection-accuracy improvement. These neural findings should be interpreted cautiously because the omnibus EEG effects were trend-level, EEG–behavior correlations were based on a small active-stimulation subgroup, data-quality sensitivity analyses indicated artifact-related instability in theta-power estimates, and the full exploratory EEG–behavior correlation matrix did not survive FDR correction. Conclusions: These findings provide short-term behavioral evidence that CP5-centered parietal HD-tACS may support performance in a cognitively demanding smartphone-based task and motivate further work at the intersection of neuromodulation, cognitive aging, and human–technology interaction. Full article

Neuroimaging is a promising modality for early AD detection, facilitating timely clinical intervention. This study proposes an enhanced deep learning framework that extracts critical AD biomarkers from structural MRI (sMRI) data acquired from the ADNI. Our novel CNN architecture integrates conventional convolutional layers with residual and skip connections for efficient feature extraction, achieving substantially lower computational cost than standard deep architectures such as VGG-16 (138 M), while remaining more parameter-intensive than highly compact architectures such as MobileNet and EfficientNet, which are designed explicitly for resource-constrained deployment. A comprehensive preprocessing pipeline converts 3D MRI scans into 2D slices through quality control (discarding slices with mean intensity < 5% of the maximum), bilinear resizing to 96 × 96 pixels, normalization using training-set statistics, and data augmentation. Stratified, subject-level data partitioning combined with robust statistical validation via bootstrapping demonstrates superior multiclass classification performance across AD, early and late MCI, and cognitively normal groups compared to state-of-the-art methods. Additionally, Grad-CAM-based interpretability maps were generated to highlight disease-relevant brain regions, confirming consistent activation around the hippocampus and temporal lobe. Full article

Social justice in Islamic theological education represents a concept that is normatively central yet pedagogically underarticulated. Rooted in the Qur’an, the Sunnah, and the broader Islamic intellectual tradition, it carries legal obligation, ethical responsibility, spiritual maturity, and social sensitivity. These sources consistently affirm social justice as a foundational principle of a balanced and equitable social order, but its translation into educational practice remains unabiding. This research examines how social justice is positioned within the higher education syllabus for future Islamic religious education practitioners (teachers and imams) in Bosnia and Herzegovina. Using Mayring’s qualitative content analysis, in this study, written responses collected through an open-ended qualitative expert survey were analysed. Thematic indicators were developed as an outcome and were used as criteria for the subsequent documentary analysis of the official syllabus. Then followed an analytical examination of the syllabus of a study programme leading to the qualification of Islamic religious education practitioners. The findings indicate that social justice is not explicitly articulated within intended learning outcomes as knowledge, attitude, or pedagogical competence. The analysis structured through Bloom’s taxonomy demonstrates the presence of pedagogical and methodological sensitivity across all cognitive levels, from knowledge to evaluation. This reveals a discrepancy between the normative centrality of social justice and its partial pedagogical realisation. This study identifies a persistent tension between theological ideals and educational practice. The potential for rearticulating a theological–pedagogical framework in which social justice becomes an explicit, lived, and transformative category within practitioners’ education was highlighted as something in place of a conclusion. Full article

Background and Aims: Rapid digital transformation has generated pervasive attentional disruption in research and professional settings, raising the question of how the temporal conditions that support deep scientific thinking can be preserved. Our narrative review aimed to (i) synthesize neurobiological evidence on the mechanisms through which task-irrelevant digital interruption impairs deep thinking; (ii) discuss the conditions required for deep thinking and the potential threats posed by contemporary developments, including generative artificial intelligence-related cognitive offloading; and (iii) elaborate evidence-based, multi-level recommendations for research institutions. Methods: Targeted searches of PubMed, Google Scholar, and Web of Science (January 2010–September 2025) were conducted using terms spanning attentional neuroscience, digital distraction, neuroplasticity, and cognitive performance, supplemented by forward and backward citation tracking. Peer-reviewed empirical studies, meta-analyses, and theoretical frameworks addressing neurobiological mechanisms of sustained attention and the cognitive effects of digital interruption in professional and/or research settings were included. Results and Interpretation: Deep thinking and protected thinking time are treated as distinct constructs: the former as a sustained, integrative cognitive process supported by coordinated executive control and default mode network activity, the latter as uninterrupted temporal intervals within which that process can occur. Repeated engagement with task-irrelevant digital stimuli is associated with cortico-striatal strengthening and prefrontal-parietal under-consolidation, producing a plasticity paradox in which attentional fragmentation becomes self-reinforcing. The emergence of generative artificial intelligence introduces a qualitatively distinct threat through voluntary cognitive offloading, which reduces deep engagement independently of attentional distraction. Conclusions: Evidence-based strategies spanning individual, team, organizational, technological, and assessment levels are available to preserve protected thinking time. Direct evidence linking these intervals to specific research-impact outcomes remains limited, and institutional interventions should be prospectively evaluated. Full article

High homocysteine levels are a key risk factor for cognitive impairment, a major public health concern in aging societies. Although betaine is known to reduce Hcy levels, its effects on hyperhomocysteinemia (hHcy)-induced cognitive impairment and the underlying mechanisms remain unclear. Here, we established an hHcy-induced cognitive impairment mouse model by feeding mice a high-methionine diet for 8 weeks, followed by betaine supplementation for 14 days. Betaine treatment attenuated hHcy-induced cognitive impairment. This improvement was accompanied by alleviation of neuropathological alterations and enhancement of antioxidant capacity. Notably, betaine suppressed reactive oxygen species (ROS) accumulation, neuronal apoptosis, and Tau hyperphosphorylation at Ser396 and Thr231 in both mouse hippocampus and HT-22 cells. Mechanistically, betaine-induced activation of the PI3K/AKT/GSK-3β pathway was effectively blocked by the PI3K inhibitor LY294002. Notably, treatment with the ROS scavenger N-acetylcysteine (NAC) alone phenocopied this activation, suggesting that ROS functions as an upstream regulator of this signaling cascade. Collectively, our data demonstrate that betaine attenuates hHcy-induced cognitive impairment by suppressing oxidative stress-driven apoptosis and Tau pathology through modulation of the PI3K/AKT/GSK-3β signaling pathway. These findings suggest that betaine may hold promise for further preclinical and clinical studies, although long-term efficacy and safety evaluations remain necessary. Full article