Search Results (2,047)

Office openness comprises two physically distinct dimensions—spatial openness and visual openness—yet studies quantifying their independent contributions to cognitive efficiency at the individual level remain scarce. Prior research has predominantly reported group-mean effects, leaving bidirectional individual responses insufficiently examined. This study independently manipulated both dimensions and measured individual-level EEG responses in 24 adults using a 3 × 3 within-subject factorial design. The beta/alpha ratio change rate was computed as an index of cognitive efficiency. Substantial neurophysiological variation across conditions was confirmed in every participant. The absence of significant group-level effects was interpreted not as a lack of environmental influence but as the result of bidirectional individual responses canceling each other out in group averages. Spatial and visual openness induced response ranges of equivalent magnitude at the individual level, and individually optimal conditions were widely distributed across the nine experimental conditions. The correspondence rate between subjective preferences and EEG-identified optimal conditions did not exceed chance, and this bidirectional cancellation mechanism is proposed as an explanation for the contradictory findings that have long characterized open-office research. These results support design strategies that offer diverse combinations of spatial and visual openness within activity-based working environments, paired with feedback systems grounded in objective cognitive performance data. Full article

Cognitive radar has been developed to use feedback from its operating environment, obtained from a beam, to make resource allocation decisions by solving optimization problems. Previous works focused on target tracking accuracy by designing an evaluation metric for an optimization problem. However, in practical real-world scenarios, both the target tracking performance of cognitive radar and its operational perspective should be considered. In this study, the usage of an operational risk score in the allocation of radar resources is proposed for a cognitive radar framework. Resource allocation regarding radar dwell time is considered to reflect the operational significance of the target’s priority level. The dwell time allocation problem is solved through Second-Order Cone Programming (SOCP). Numerical simulations are performed to verify the effectiveness of the proposed framework. The results show that the proposed SOCP-based algorithm achieves comparable operational risk estimation performance to conventional methods while using fewer time resources, thereby improving overall resource efficiency in resource-constrained environments. Full article

In the analysis of complex engineering systems, managing uncertainty and optimizing information processing structures are critical for reliable state prediction. The Belief Rule Base (BRB) provides a powerful machine learning approach for integrating expert knowledge with uncertain information. However, mitigating the combinatorial complexity of BRBs through conventional structure simplification often causes unintended information loss, introducing systematic prediction biases that undermine reliability. To address the trade-off between system complexity and modeling accuracy, this study proposes an adaptive belief rule base framework integrating sensitivity analysis with posterior consistency calibration (BRB-ARR). First, an information-driven rule screening mechanism is developed to dynamically determine the pruning threshold based on optimized Mean Square Error (MSE) fluctuations. This method effectively filters redundant rules while avoiding the cognitive biases associated with fixed empirical values. Second, a low-dimensional optimization process is employed to readjust the parameter vector, significantly enhancing computational efficiency. Finally, a posterior calibration module is introduced to compensate for the systematic biases caused by dimensionality reduction, strictly preserving the interpretability of the core inference architecture. To validate the effectiveness of the proposed framework, experimental evaluations are conducted on petroleum pipeline networks and liquid propellant launch vehicles. In the petroleum pipeline scenario, the rule base scale is reduced by over 60 percent from 56 to approximately 20 rules, while the parameter dimensionality decreases from 338 to 122. Compared to the conventional model, the mean squared error is reduced from 0.5291 to 0.3619. Furthermore, in the liquid propellant launch vehicle case, the model achieves a prediction accuracy of 98.57 percent with a mean squared error of 0.00029 while reducing the rule scale from 441 to 109. These results demonstrate that the BRB-ARR model effectively balances structural compactness with high precision prediction, offering a novel approach to uncertainty modeling in intelligent systems. Full article

This study investigates the impact of user workflows and iterative decision-making on task efficiency in 3D Gaussian splatting (3DGS)-based 3D reconstruction. In current 3DGS workflows, the absence of clearly defined stages and structured processes leads users to repeatedly interpret intermediate results and revisit earlier phases, thereby increasing cognitive load and reducing efficiency. To address this issue, data were collected from ten expert users experienced in 3DGS-based workflows through semi-structured interviews and shadowing observations. To systematically decompose complex and iterative user workflows, hierarchical task analysis (HTA) was employed. The results show that the workflow can be organized into six stages: (1) Data Acquisition, (2) Project Setup, (3) 3DGS Training, (4) Result Inspection, (5) Quality Refinement, and (6) Output Utilization. User workload was primarily concentrated in the Result Inspection and Quality Refinement stages, characterized by repeated retraining and decision-making processes. Based on this analysis, four issues were identified: ambiguity in early-stage configuration, limited visibility of progress status, difficulty in identifying the causes of failure, and inefficiencies in editing operations. To address these issues, an interface design consisting of four functional areas is proposed: (1) Mode & Capture Setup, (2) Progress Management, (3) Error Review, and (4) Editing Efficiency. Evaluation by expert users indicates that the proposed interface was rated significantly higher than the existing 3DGS interface across all functional areas (p < 0.01). Higher scores were observed in Error Review (M = 4.05 vs. 1.53) and Editing Efficiency (M = 4.55 vs. 1.88) compared to the existing interface. These findings suggest that interface support for error review and editing tasks plays an important role in improving workflow usability. This study structures 3DGS workflows from a user-centered perspective and identifies the key stages where iterative decision-making is most concentrated. Based on these findings, it proposes directions for 3DGS interface design and empirically demonstrates the effectiveness of the proposed design. Full article

Automatic modulation classification (AMC) is a key component of spectrum awareness, cognitive radio, and signal intelligence, enabling receivers to identify modulation schemes from noisy in-phase and quadrature (IQ) observations. Traditional approaches rely on likelihood-based methods or handcrafted feature extraction, which often struggle under channel impairments and real-world variability. Recent advances in deep learning enable models to learn directly from multiple signal representations, including raw IQ samples, engineered features, and time–frequency or constellation-based encodings, improving adaptability across diverse signal conditions. This paper presents a structured review of deep learning approaches for AMC, including CNNs, RNN/LSTM models, and transformer-based architectures, with a focus on performance, robustness, and system-level trade-offs. We analyze how representation choices, dataset design, and evaluation protocols influence reported results, and highlight key challenges such as domain shift, low-SNR environments, and multi-signal interference. Finally, we outline future directions focused on improving generalization, integrating classical signal processing with learning-based methods, and enabling efficient deployment in real-world and resource-constrained systems. Full article

Financial reporting standards aim to increase transparency; however, the expansion in disclosure volume may also create an information overload paradox for investors, an issue that remains underexplored in the context of institutional investors. Excess information beyond mandatory requirements may complicate decision environments and create cognitive burden. When information exceeds cognitive processing capacities, attention may become fragmented, making it more difficult to distinguish signal from noise and potentially leading to analysis paralysis and changes in risk perception. Drawing on bounded rationality and cognitive load theory, this study conceptualizes information overload as a behavioral constraint associated with perceived limitations in decision quality and speed and, accordingly, examines its influence on institutional investors’ decision processes through a phenomenological approach. The study employs thematic analysis based on in-depth interviews with 19 professionals in institutional investment organizations in Türkiye. The findings suggest that information overload is experienced as cognitive strain that may prolong decision processes, may be associated with analysis paralysis and perceived changes in decision quality, and may be associated with increased uncertainty and potential challenges in interpreting risk. These findings provide exploratory insight into how information density may influence risk interpretation and portfolio assessment, and how institutional investors perceive decision-making efficiency. Full article

This study employed the Ex-Gaussian distribution model to analyse eye-tracking data, to elucidate the cognitive mechanisms underlying predictive processing during Chinese reading. Using a single-factor, two-level within-subjects design (contextual predictability: high vs. low), data from 32 adult readers were analysed across the pre-target and target word regions. The results revealed that predictive reading follows a three-stage cognitive model. In the expectation generation stage (pre-target region), a significant negative τ effect indicated resource pre-allocation driven by strong contextual constraints, thereby facilitating the construction of predictive lexical representations. In the verification and integration stage (target word region), a significant negative μ effect in the later measurement window indicated that successful prediction–input matching accelerated lexical identification and enhanced integration efficiency; the σ parameter did not reach significance in either measurement window. In the conflict resolution stage (pre-target and target word regions), a significant positive τ effect indicated that verification failure triggered lexical activation competition at the target word, driving regressive fixations to the pre-target region for contextual reanalysis; conflict resolution costs were markedly higher under the low-predictability condition, owing to the absence of a dominant activation anchor. These findings suggest that contextual predictability influences reading through a dual mechanism: the μ parameter modulates the automatic processing speed of lexical identification, whereas the τ parameter regulates the cognitive control processes underlying expectation generation and conflict resolution. Together, these results provide empirical support for the integration of predictive coding theory and cognitive control frameworks. Full article

Efficient spectrum sensing remains a main challenge for Cognitive Radio Networks (CRNs), especially in a wireless environment where methods like energy detection have high uncertainty. This work proposes an entropy-based spectrum-sensing system enhanced with machine-learning algorithms and implemented on a Software-Defined Radio (SDR) platform for real scenario testing. Entropy measures, such as Shannon and Rényi entropies, are used as discriminative features to distinguish occupied and idle frequency bands and release the channel if needed. Machine learning classifiers have achieved good results. In this research, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and Random Forests (RFs) are used with data captured via a GNU Radio and the Universal Software Radio Peripheral (USRP)-based SDR testbed. The experimental results demonstrate a probability of detection (P_d) above 0.9 and a false alarm rate (P_fa) below 0.1, indicating a substantial improvement over the classical energy detector of more than 20% for some signal-to-noise ratio (SNR) values. The integration of entropy metrics with machine learning (ML) models enables a dynamic detection in variable spectral environments, providing a practical framework for CRNs. Full article

This study investigated the cortical activation patterns and functional connectivity underlying human decision-making by comparing two distinct purchasing orientations: other-oriented consumption (OOC) and self-oriented consumption (SOC), using functional near-infrared spectroscopy (fNIRS) as a wearable neuroimaging modality. The results revealed significant temporal concentration differences in $∆\mathrm{H}\mathrm{b}\mathrm{O}$ under the OOC condition in Ch06 ($p$< 0.05). The 15 fNIRS channels were mapped to seven anatomically defined regions of interest ($\mathrm{ROIs}$) to better capture regional activation patterns and functional network properties. While global network metrics showed no significant differences, seed-based connectivity analysis revealed that the OOC condition elicited significantly stronger functional connectivity between the medial prefrontal cortex (${\mathrm{R}\mathrm{O}\mathrm{I}}_4$) and the left lower PFC (${\mathrm{R}\mathrm{O}\mathrm{I}}_6$, $p$ < 0.05, $d$ = 0.45). In summary, while the overall network efficiency remained stable across conditions, our findings highlight a spatially specific enhancement in functional connectivity centered on the PFC, indicating an increased cognitive load from engaging in complex social cognitive processes. These findings advance the understanding of neural correlates underlying human decision-making and demonstrate the utility of wearable monitoring using fNIRS for capturing cognitive state differences in human-centered decision contexts. Full article

Mitochondrial dysfunction is a key contributor to cognitive impairment, directly affecting neuronal viability, synaptic function, and energy metabolism. In the central nervous system, where energy demand is particularly high, disturbances in mitochondrial dynamics, including impaired oxidative phosphorylation (OxPhos), increased reactive oxygen species (ROS) production, and reduced ATP availability, can compromise synaptic transmission and accelerate cognitive decline. These alterations are commonly observed in neurodegenerative diseases such as Alzheimer’s (AD) and Parkinson’s (PD), in which mitochondrial dysfunction is closely associated with oxidative stress and neuroinflammatory processes. This review aims to investigate the role of mitochondrial dysfunction in cognitive impairment and the effects of physical exercise as a non-pharmacological strategy to mitigate these alterations. Current evidence indicates that exercise promotes mitochondrial biogenesis through activation of the AMPK/PGC-1α pathway, enhances oxidative metabolism, and improves mitochondrial efficiency. Furthermore, exercise reduces oxidative stress and inflammation while stimulating the release of neurotrophic factors, such as brain-derived neurotrophic factor which support neurogenesis, synaptic plasticity, and neuronal survival. Overall, these findings reinforce the importance of mitochondrial integrity in maintaining cognitive function and highlight physical exercise as a promising strategy to counteract mitochondrial dysfunction and delay the progression of neurodegenerative diseases. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline. Its main pathological features are extracellular plaques composed of aggregated amyloid-β (Aβ) peptides and intracellular neurofibrillary tangles formed by hyperphosphorylated tau. The Aβ hypothesis proposes that Aβ accumulation is a key driver of AD, influencing tau pathology, neuroinflammation, and neurodegeneration. However, therapies that reduce Aβ have shown limited clinical benefits. This suggests that the mechanisms underlying peptide-mediated modulation of AD pathology are much more complex. Both Aβ and tau undergo various post-translational modifications (PTMs) that affect their structure, aggregation, and toxicity. In addition, these abnormal proteins are not efficiently cleared in AD, indicating dysfunction of the protein quality control (PQC) system that maintains proteostasis. Such abnormal PTMs and impaired PQC likely work together to drive disease progression, which may explain the limited success of Aβ-reduction therapies. In this review, we describe how major PTMs, including phosphorylation, ubiquitination, acetylation, glycosylation, and oxidation, regulate the pathological behavior of Aβ and tau. We also discuss the role of the PQC systems in the pathology of AD. We propose that dysregulation of PTMs and PQC constitutes a convergent mechanism underlying AD pathogenesis. Therapeutic strategies targeting these processes may provide more effective and sustained disease modification than approaches focused solely on Aβ reduction. Full article

Background/Objectives: Visuospatial impairments are among the earliest cognitive symptoms in Alzheimer’s disease (AD) and related dementias (ADRD), yet standard assessments often lack ecological validity and focus on isolated domains. This study examines whether integrating linguistic, behavioral, and eye-tracking measures provides a more comprehensive characterization of cognitive deficits within a multimodal, exploratory framework. Methods: Twenty older adults (10 with mild to moderate dementia, including AD/ADRD, and 10 age-matched controls) completed three tasks: (1) oral narrative production, (2) visuospatial behavioral tasks (manipulation, recognition, reproduction), and (3) free-viewing eye-tracking. Linguistic, behavioral (time, errors), and fixation-based measures were analyzed using non-parametric statistics, with emphasis on effect sizes and cross-domain patterns. Results: The clinical group differed consistently from controls across domains. Linguistic measures showed increased output but reduced quality, including lower syntactic complexity, more grammatical errors, greater pragmatic deviations, and reduced gist comprehension. Behavioral tasks revealed slower performance and more frequent failures. Eye-tracking differences were less pronounced, showing a tendency toward longer fixations and less efficient visual exploration. A composite multimodal index showed clear separation between groups, indicating a consistent pattern of impairment across measures. Conclusions: Cognitive differences in dementia are expressed across multiple domains, with the strongest effects in linguistic and behavioral measures. These findings highlight the value of multimodal profiles for capturing multidimensional impairment. Results should be interpreted as exploratory and require confirmation in larger, confirmatory studies. Full article

Manual assembly remains critical in Industry 5.0 high-mix/low-volume manufacturing, but it introduces resilience challenges due to cognitive load, training demands and frequent product changes. While AI-based supervision can mitigate errors, deploying such systems is often hindered by the cost of collecting and labelling thousands of real images for each product variant. This paper presents a Human-in-the-Loop semantic-twin pipeline that generates approximately 45,000 labelled synthetic images from a single CAD-based configuration and uses them to train an object detection model for real-time assembly supervision. Experiments on seven virtual environment configurations show that removing realistic lighting or camera motion reduces F1-score on real images from 0.87 to 0.46, confirming their critical role for synthetic-to-real transfer. A controlled laboratory study on a single bicycle chainring assembly task with 10 participants and 100 monitored cycles demonstrates the feasibility of automatic KPI extraction, with error events associated with a 25.6% increase in average cycle time (from 58.4 s to 73.3 s) under the tested conditions. Compared to manual annotation, where labelling 3000 images required approximately 4 h, the semantic-twin configuration takes around 4 to 6 h including image generation that enables rapid creation of large labelled datasets for new product variants without additional human annotation. These results provide a proof-of-concept foundation for resilient, data-efficient supervision of high-mix manual workstations, with full industrial validation across multiple products, stations and operator demographics identified as the necessary next step. Full article

Perceptual–cognitive abilities are essential components of performance in volleyball, where players must quickly interpret visual information and respond effectively to rapidly changing game situations. The present study aimed to examine perceptual–cognitive abilities and reaction performance in competitive female volleyball players and to explore how these abilities may contribute to athlete development and training design. Thirty-nine young female volleyball athletes participated in the study and underwent an evaluation of perceptual–cognitive abilities considered critical for volleyball performance. These abilities were assessed through specially designed computer-based tasks delivered via dedicated experimental software, enabling the measurement of reaction time and response accuracy during perceptual–motor processing. Group comparisons did not reveal significant differences between playing positions or competitive levels in the measured perceptual–cognitive abilities. Multivariate and clustering analyses suggested the presence of potential performance patterns characterized by different combinations of reaction speed, response accuracy, and perceptual–cognitive processing. However, these patterns should be interpreted with caution, as the clustering solution showed limited separation (silhouette score = 0.02), indicating an exploratory and non-definitive structure. Overall, the findings highlight the multidimensional nature of perceptual–cognitive performance in volleyball and suggest that athletes may rely on different perceptual–motor strategies when responding to game-related stimuli. From an applied perspective, integrating perceptual–cognitive challenges into training environments may support athlete development and improve decision-making efficiency in dynamic game situations. Full article

Early mild cognitive impairment (EMCI) is the earliest intervenable stage of Alzheimer’s disease (AD). Although graph neural networks (GNNs) have begun to exploit brain network topology, traditional fMRI-based diagnostic methods often neglect these structural patterns by relying on vectorized features. Furthermore, existing GNNs frequently disregard inter-regional functional heterogeneity and group-level discriminative patterns, leading to limited accuracy and biomarker interpretability. To address these challenges, we propose HF-BrainGNN, an end-to-end hierarchical graph learning framework for EMCI identification. Our method introduces a functional affinity region convolution (FAR-Conv) layer to learn region-adaptive kernels, a Differential Focus Pooling (DF-Pool) module to identify disease-salient brain regions by maximizing inter-group distinctiveness, and a hierarchical integration classifier (HIC) to fuse multi-level graph representations. The framework is optimized using classification, focus separation, and consistency regularization losses. Experiments on the ADNI dataset (104 EMCI, 114 Cognitively Normal) show that HF-BrainGNN achieves 86.78% accuracy, outperforming the best baseline (Hi-GCN) by 4.64%. Furthermore, the automatically identified regions, such as the bilateral hippocampus and default mode network hubs, align with established EMCI biomarkers. Ultimately, HF-BrainGNN provides an efficient, interpretable artificial intelligence tool for precise brain network characterization and early AD intervention. Full article