Search Results (634)

Addressing childhood obesity and premature pubarche together is critical for understanding the systemic and neurodevelopmental consequences of early endocrine stimulation in childhood. In this study, resting-state functional magnetic resonance imaging data from 11 children with premature pubarche (PP) and 7 children with both obesity and premature pubarche (OB + PP) were analyzed. Independent component analysis (ICA) was applied to identify spatially independent networks among participants. Significant components obtained from the ICA were used as seeds in the resting-state functional connectivity analysis (p < 0.001). When we examined our ICA results, we observed significant differences between our groups in 5 ICs. The regions showing differences between the groups largely overlapped with the Visual Network (VN), Dorsal Attention Network (DAN), Default Mode Network (DMN), and Salience Network (SN). In children with premature pubarche accompanied by obesity, increased functional connectivity was observed in the Posterior Cingulate Cortex (PCC) seed in the DMN and in the Anterior Cingulate Cortex (ACC) seed in the SN. Our findings suggest that PP is associated with altered functional connectivity in the DAN and SN. The observed differences in resting-state functional connectivity suggest that obesity may be associated with altered brain network organization within the premature pubarche population. However, these findings should be interpreted with caution, and longitudinal studies are needed to determine their developmental significance. Full article

Background: Integrating human factors into quantitative railway risk assessment remains methodologically unresolved. Failure Mode and Effects Analysis (FMEA) records human contributions as a single occurrence rating and cannot represent their organisational antecedents or their interactions. Purpose: This paper is a methodological proposal. It specifies the Integrated Human–Technical Risk Assessment (IHTRA) framework, which combines FMEA, the Human Factors Analysis and Classification System (HFACS) and Bayesian network modelling within the EN 50126 RAMS lifecycle, and demonstrates what such a specification makes analytically possible. It does not claim to validate the framework empirically. Methods: The Bayesian layer is specified in full as a ten-node network with all conditional probability tables reported. Of its twenty-two endogenous parameters, eight rest on evidence: four are derived from a survey of 89 Hungarian train drivers and four from the published fatigue literature. The remaining fourteen are declared structured assumptions awaiting expert elicitation. A conventional FMEA and the specified framework were both applied to national investigation report 2023-1152-5 (Sáp collision, 2023). Results: Human factors awareness yielded the highest domain mean (M = 4.23, SD = 1.18), with near-unanimous recognition of fatigue (M = 4.90) and workload (M = 4.87). Organisational consideration of human factors scored lowest (M = 2.66). No significant experience-group differences were observed (p > 0.05). The case analysis identified two HFACS levels as confirmed by the investigation findings and two further levels as plausible under the model interpretation. Inference over the specified network gives an illustrative, but not yet empirically calibrated, increase from p = 0.00038 to p = 0.00059 for the unsafe act and from p = 0.00011 to p = 0.00047 for the collision outcome. Conclusions: A specified but uncalibrated framework is a methodological contribution rather than an empirical one and is presented as such. This paper states precisely which parameters would have to be measured, and by what protocol, for the framework to become operational. Full article

The German low-voltage (LV) distribution grid faces stress from the build-out of distributed photovoltaic (PV) units. Battery storage is widely proposed as a solution, yet the literature is partitioned by storage scale, placement, and how ageing is treated. This study introduces a unified cross-class assessment of the three structurally distinct LV storage classes under one methodology: residential PV home storage (Class I, 5 kWh), feeder-level DSO string storage (Class II, 10–50 kWh, one unit per feeder end), and market-coupled district storage (Class III, 50–500 kWh, EPEX-scheduled). All classes share the same 1 min load and PV time series, the same voltage-guided $P_{\mathrm{net}}\left(U\right)$ dispatch with class-specific extensions, and the same Naumann–Wang LFP ageing model with rainflow counting, evaluated to a 20-year state-of-health horizon across representative and limiting multi-feeder German LV networks, with degradation treated as a dispatch outcome. Correctly modelled networks prove largely voltage-robust; on the limiting Kerber feeders studied, storage does not restore grid-code compliance at any tested size. Grid-supportive operation yields little grid benefit and is ageing-neutral only where it is not needed, becoming costly without adequate benefit on the stressed feeders studied. The value of LV storage therefore rests on dispatch-resolved lifetime, self-consumption and curtailment reduction, and market participation rather than grid service. Full article

Accurate state-of-health (SOH) prediction of lithium batteries is critical for guaranteeing the long endurance and system safety of autonomous underwater vehicles (AUVs) in marine operations. However, owing to complex underwater-operation conditions, data acquisition in AUVs is typically restricted to rest stages in the communication period, which would be characterized by incomplete data with high-frequency sensor noise. As a result, existing battery SOH prediction approaches would struggle to ensure estimation accuracy and model robustness for within-cell degradation trajectories in AUV applications. This paper proposes a novel heterogeneous dynamic fusion model based on a data-mechanism dual-driven (DMDD) framework for the SOH prediction of lithium batteries in AUV applications, innovatively utilizing features extracted from the rest stage after discharge. At first, a dual-filter strategy based on the interquartile range interception and the Savitzky–Golay algorithms is designed to effectively eliminate transient spikes and high-frequency artifacts of raw data. Furthermore, a two-stage feature-screening architecture is developed, which can not only filter out statistical redundancies but also elucidate the electrochemical mechanisms between extracted features and battery degradation. Moreover, a heterogeneous fusion model comprising random forest, support vector regression, and gated recurrent unit networks is constructed. On this basis, an adaptive dynamic fusion strategy based on the K-nearest neighbor and the minimum-variance unbiased estimation (MVUE) is proposed, which enables locally optimal credit assignments tailored to the specific characteristics of different aging stages. Experimental validations comprehensively demonstrate the superior performance of the heterogeneous dynamic fusion model based on the DMDD framework across the entire battery lifecycle. Specifically, the proposed heterogeneous dynamic fusion model can achieve a coefficient of determination ($R^2$) over 0.99907, while the MAE and the RMSE can be maintained within 0.33958% and 0.56211%, respectively, showing satisfactory accuracy for battery SOH prediction in AUV applications. Full article

The diagnosis of cervical spondylotic myelopathy (CSM) relies mainly on clinical symptoms and structural imaging, highlighting the need for objective functional biomarkers. This study investigated alterations in resting-state magnetoencephalography (MEG) functional connectivity in CSM and evaluated whether multiband weighted phase lag index (wPLI) features could distinguish CSM patients from healthy controls (HCs). Eyes-closed resting-state MEG data were acquired from 31 CSM patients and 32 HCs. Region-of-interest-level wPLI connectivity was calculated in the theta, alpha, beta, and gamma bands and used to train multiple machine learning classifiers. Model performance was assessed using nested group cross-validation, and SHapley Additive exPlanations (SHAP) were used to interpret the best-performing model. Patients with CSM exhibited frequency-specific connectivity alterations, particularly in the theta and gamma bands. Logistic regression achieved the best overall discriminative performance, and SHAP analysis indicated that classification was driven mainly by long-range theta-band connections and gamma-band connections involving the frontal pole. These findings suggest that CSM is associated with measurable reorganization of large-scale cortical networks and that resting-state MEG connectivity combined with explainable machine learning may provide a promising framework for exploring candidate neurophysiological biomarkers of CSM. Full article

Renewable energy and energy storage stations require frequent updates of monitoring and control logic across heterogeneous devices and changing operating strategies. This paper proposes a rule graph-based reference architecture that combines low-code logic configuration, graph–model semantic binding, and microservice-oriented functional decomposition. A component status matrix separates the target architecture from the implemented subset. The runnable subset comprises a minimal FastAPI backend, REST/WebSocket telemetry interfaces, an in-process queue, and stateful rule evaluators; gateway, authentication, external message bus, time-series database, visual editor, and industrial protocol services remain design-level elements. Beyond the original single-rule example, a priority-ordered multi-device rule is implemented for cooperative BESS dispatch, communication/topology blocking, low-SOC protection, frequency-based load shedding, backup request, and five-sample recovery release. Existing local network benchmarks are complemented by a 600-step software-in-the-loop trace with scripted telemetry fluctuations and communication quality faults and by 500 in-process ASGI timing samples at each of the four point levels. The trace produced no safety dispatch or protected device violations. P99 application path latency ranged from 1.1962 to 5.5287 ms, but one 75.3065 ms outlier exceeded a 50 ms reference deadline, demonstrating that the Windows/FastAPI path is not deterministic. No industrial controller, hardware-in-the-loop facility, field data, or engineer usability study was used. Accordingly, the paper makes no claim of industrial real-time readiness or measured development effort reduction. Full article

Background: The risk of violence is elevated in patients with Schizophrenia Spectrum Disorders (SSD). However, current violence risk assessment approaches rely predominantly on clinical and historical factors and remain limited in predictive accuracy, highlighting the need for more objective complementary markers. Methods: The present study examined the neurobiological differences between violent (n = 66) and non-violent (n = 166) SSD patients, and compared the predictive utility of these neurobiological markers and clinical risk factors, including childhood trauma and psychopathology, in distinguishing violent from non-violent individuals. We further aimed to construct a multimodal predictive model using the strongest predictors from both domains, evaluating whether combining neural and clinical variables improves predictive performance beyond either single domain. Structural and resting-state MRI scans were acquired for patients and healthy controls (n = 504), who were used to calibrate the normative model. Neuroimaging data were analyzed within a normative modelling framework, and univariate associations between neurobiological and clinical factors and violence were examined. Results: A multimodal model integrating neurobiological and clinical measures achieved moderate classification performance (74.7% balanced accuracy), outperforming all individual predictors. The strongest predictors included the medial orbitofrontal cortex, caudal middle frontal gyrus, limbic-default mode network functional connectivity, and childhood sexual abuse. Conclusions: Our findings suggest that alterations in fronto-limbic systems involved in goal-directed decision-making and emotion regulation may contribute to violent behaviour in SSD. Furthermore, the improved performance of the multimodal model supports the potential added utility of integrating neuroimaging markers with established clinical risk factors in violence risk assessment. Full article

Objective: Depressive symptoms exist on a continuum in the general population, yet the underlying neurobiological mechanisms, particularly the interplay between resting-state networks and task-evoked social cognitive responses, remain elusive. Methods: Leveraging the Human Connectome Project (HCP) dataset, we included 867 participants. With depression scores as the independent variable and age/sex as covariates, we systematically examined associations with sleep quality, negative emotions, sensory scores, gray matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), multi-seed resting-state functional connectivity (rsFC), as well as brain activation and behavioral performance during working memory, emotion recognition, social cognition, relational reasoning, language comprehension, and gambling tasks. The statistical threshold was set at voxel-level p < 0.001 (uncorrected) combined with cluster-level FWE correction at p < 0.05. Results: (1) Depression scores were positively correlated with sleep disturbances, negative emotions (anger/fear), and pain. (2) In resting-state, depression scores negatively correlated with ventral striatum (VS)–cerebellum/parahippocampal gyrus/fusiform rsFC, yet positively correlated with pregenual anterior cingulate cortex (preACC)–supplementary motor area (SMA) rsFC. (3) In task-fMRI, only the social task showed a positive association with task accuracy and regional activation in bilateral pre/postcentral gyri, superior temporal gyri, left middle frontal gyrus, and SMA/paracentral lobule. Conclusions: Elevated depression scores are linked to a pattern that may reflect relative decoupling between reward and perceptual systems, along with enhanced connectivity in cognitive control circuits. Socially, high scorers exhibit a pattern suggestive of compensatory hypervigilance, accompanied by enhanced behavioral performance. This study provides multidimensional evidence for the dimensional neural representation of depressive symptoms. Full article

Background/Objectives: Auditory verbal hallucinations (AVH) are clinically heterogeneous experiences that may occur across psychiatric, neurological, sensory, and non-clinical contexts. This controlled pilot study investigated multimodal structural and functional MRI features associated with persistent AVH in a psychiatric clinical population, recruited from outpatient psychiatric clinics and diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder with psychotic features. Neurological, sensory, and non-clinical presentations of AVH were not included. Methods: This observational controlled pilot study included 14 participants with persistent auditory verbal hallucinations and 15 age- and sex-matched healthy controls. Participants with AVH had experienced the current persistent hallucinatory phase for a mean of 3.1 ± 1.6 years (range of 1–7), with a mean overall psychiatric illness duration of 12.4 ± 4.8 years. Independent component analysis assessed resting-state functional connectivity, BrainVoyager QX measured cortical thickness, and diffusion tensor imaging (DTI) evaluated white matter microstructure. Multiple comparisons were controlled using false discovery rate correction followed by 5000-iteration Monte Carlo cluster-extent correction for fMRI and Monte Carlo cluster correction for whole-brain structural metrics. Results: Participants with AVH demonstrated increased functional connectivity across default mode network (DMN) hubs (precuneus, inferior frontal, and parahippocampal gyri) and superior temporal regions. Whole-brain cortical thickness analysis revealed no significant group differences; however, secondary exploratory analyses of six regions previously implicated in AVH showed cortical thinning in participants with AVH relative to the controls after FDR correction. DTI revealed no group differences surviving whole-brain permutation correction (TFCE, FWE-corrected p < 0.05); exploratory uncorrected findings are reported as hypothesis-generating. Conclusions: This pilot study identifies structural and functional network differences between medicated individuals with persistent AVH and healthy controls, centred on frontotemporal and default mode networks. Because no psychiatric control group without AVH was included, these differences cannot be attributed specifically to AVH as opposed to the underlying psychiatric disorders or their treatment. No diffusion findings survived whole-brain permutation correction; exploratory uncorrected results are reported but are not incorporated into these conclusions. Collectively, these findings identify candidate imaging markers that require validation against psychiatric control groups. Full article

EEG-based motor imagery (MI) decoding is commonly evaluated in open loop, but strong offline performance does not necessarily translate into stable real-time control once decoder outputs drive a physical device. This issue is especially relevant for lower-limb exoskeletons, where initiating movement, sustaining movement, stopping movement, and maintaining rest can impose different controller-level demands. This study presents a proof-of-concept controller-level validation of an EEG-driven ankle exoskeleton framework, using a staged design that links open-loop decoder development to real-time closed-loop controller testing. Open-loop EEG data were collected from nine able-bodied participants during static and dynamic ankle MI using an eight-channel g.tec Unicorn Hybrid Black system, with matched PA-SEMI outputs available for eight participants in the primary open-loop comparison. We used a hybrid feature representation combining spectral, spatial covariance, and temporal complexity descriptors to compare a supervised Passive–Aggressive (PA) classifier with a Semi-supervised latent learning network (SEMI). Performance was assessed using epoch-level and persistence-based event metrics intended to reflect controller triggering. Under the evaluated model-specific protocols, SEMI produced higher open-loop accuracy and lower false-trigger rates than PA. The reported SEMI analysis was transductive: feature windows from the target-participant evaluation runs were available without labels during consistency training, and their labels were withheld until final evaluation. However, we selected PA for the primary matched closed-loop validation because it could be retrained, checked, and deployed within the same-day workflow. Since SEMI was not evaluated in a balanced matched closed-loop comparison, this study does not determine whether PA or SEMI provides superior real-time controller performance. Deployment-oriented PA updates were audited using limited same-day calibration data and evaluated in matched PA-based closed-loop trials with three participants, based on online controller logs. The closed-loop experiments were conducted on RehAnkle, a pre-commercial robotic ankle rehabilitation device operated here as a single-active-DoF ankle platform for dorsiflexion-oriented EEG control. In closed-loop trials, start and stop commands were generally reliable, whereas sustained movement and sustained rest were less stable. These proof-of-concept results indicate that command generation and state maintenance should be evaluated as separate controller-level problems, and that open-loop accuracy alone is insufficient to characterize real-time exoskeleton control. Full article

Background/Objectives: Cognitive impairment and neurological dysfunction in Multiple Sclerosis are increasingly understood as consequences of altered brain network connectivity. Magnetoencephalography offers high temporal resolution for investigating functional connectivity patterns, which may underlie various clinical symptoms. This exploratory pilot study aims to characterize preliminary FC patterns at rest and during cognitive processing in MS patients with a high fatigue burden compared with healthy controls using MEG, complemented by a systematic review of existing MEG literature in MS. Methods: In this pilot study, MEG was utilized to investigate connectivity in MS patients and HCs. Data were acquired during resting-state and a digitized version of the Symbol Digit Modalities Test. A systematic review was also conducted following PRISMA guidelines to synthesize the current evidence on frequency-specific MEG connectivity in MS. Results: MS patients exhibited a connectivity pattern paralleling the directionality of neuronal slowing, described in the spectral power MS literature, during resting-state, characterized by reduced interhemispheric coherence in alpha and beta bands alongside region-specific increases in frontal–parietal and parietal–occipital alpha coherence, reflecting a bidirectional spatial reorganization. Conversely, during the SDMT task, the MS group demonstrated significant compensatory recruitment with increased coherence in theta and gamma bands (specifically in frontal and striatal circuits). The systematic review of 44 MEG studies corroborated these findings, highlighting a consistent trend of frequency-dependent dysconnectivity that aligns with clinical load and disease pathology. Conclusions: We found that in MS, network alterations are state-dependent, shifting from reduced connectivity at rest to increased coherence during cognitive effort. These preliminary findings suggest that MEG-based connectivity may serve as a functional neuroimaging biomarker of underlying network reorganization, for monitoring early cognitive disease progression in MS. Full article

Background/Objectives: Low back pain (LBP) is associated with central alterations, but convergence across structural, functional, and network measures remains unclear. We characterized multimodal differences. Methods: Resting-state fMRI included 69 participants (37 patients, 32 controls); VBM retained 23 patients and 32 controls after structural-image quality control. Structural, local functional, and thalamic-connectivity measures were assessed. Motion control used realignment-based exclusion and Friston-24 nuisance regression. Mean Power framewise displacement was additionally calculated for all participants and compared between groups, and VBM-retained and excluded patients were compared. Models adjusted for age and sex, plus total intracranial volume for VBM. Directional maps underwent one-tailed Gaussian random field correction (voxel p < 0.001; cluster p < 0.05), without cross-direction or cross-family correction. Extracted-value and imaging–clinical analyses were exploratory. Results: Mean framewise displacement did not differ between groups. Patients showed lower gray matter volume in thalamic–hippocampal regions and right cerebellar lobule VIII and lower local functional measures in thalamic, orbitofrontal–striatal, and right temporal regions. Thalamic connectivity with sensorimotor, parietal, supplementary motor, and middle cingulate regions was higher. Nominal analyses showed positive left thalamic connectivity cluster 1–VAS and negative left cluster 1–JOA and right clusters 1/2–JOA; none survived false-discovery-rate correction. Conclusions: Multimodal MRI delineated lower thalamic structural and local functional measures alongside higher thalamo-sensorimotor and thalamo-parietal connectivity. Nominal, directionally coherent clinical associations remain hypothesis-generating. This thalamus-centered pattern warrants replication in prospectively matched, clinically stratified cohorts. Full article

This study evaluates the network-forming and gelation capabilities of quinoa protein (QP) nanocomplexes formed with alginate (QP–Al) and chitosan (QP–C) for the development of structured food emulsion gels and their application in reduced-fat food dressings. Rheological and nanometric characterisation revealed that QP–C complexes exhibited strong shear-thinning behaviour and particle-size instability with increasing concentration, indicating the breakdown of an organised internal network at rest. Conversely, QP–Al showed Newtonian behaviour, smaller particle sizes (~100–250 nm) and high surface charge stability. Upon oil incorporation, the chitosan-based systems underwent an abrupt, concentration-dependent transition from a liquid-like state to a solid-like gel network between 1.2% and 1.6% w/v chitosan. The EQP–C8 gel network exhibited severe structural fragility, degrading into a purely viscous fluid over 28 days. Conversely, the alginate-based system (EQP–AL8) formed a weak physical hydrogel network characterised by a progressive build-up of structure that resisted creaming and maintained structural integrity across temperature changes. EQP–AL8 was successfully used to develop a plant-based, reduced-fat dressing with high organoleptic acceptance; 98% of participants were willing to purchase the product. These findings demonstrate that QP–AL8 provides a clean-label technological path to designing tunable, highly stable food emulsion gels. Full article

Objective: Post-traumatic stress disorder (PTSD) has been linked to reduced functional brain connectivity, but it is unclear if altered local network properties contribute to these disruptions in functional coupling. Computational studies have demonstrated that dynamical states characterized by scale-free local activity optimize the long-range functional coupling in neuronal networks. Our study uses resting-state magnetoencephalographic recordings to investigate if characteristics of scale-free dynamics of local cortical activity are associated with alterations in long-range functional connectivity in PTSD. Methods: Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We estimated the amplitude envelopes of beta-band cortical activity in 134 cortical regions and we characterized their scale-free dynamics using the power-law scaling exponent determined by detrended fluctuation analysis. We assessed the correlation between PTSS, scaling exponents, and functional coupling between homologous anatomical areas of the two hemispheres. Results: We found negative correlations between PTSS and scaling exponents as well as between PTSS and inter-hemispheric coupling (IHC) predominantly in prefrontal and temporal regions. Positive correlations between scaling exponents and IHC were present in a large number of regions, with high correlations across primary and unimodal association cortices and lower correlations across high-order heteromodal processing areas. Conclusions: Our findings suggest that mechanisms that regulate scale-free dynamics of local cortical activity may contribute to alterations of long-range functional connectivity in PTSD. Significance: Therapies with the potential to normalize local scale-free brain activity may lead to the restoration of functional brain connectivity, offering a promising avenue for PTSD treatment. Full article

Background/Objectives: Migraine and tension-type headache (TTH) are common primary headache disorders, but their underlying network-level EEG patterns remain incompletely characterized. We examined whether resting-state functional connectivity measured with complementary estimators, predefined sensor-level regions of interest (ROIs), graph features, and machine-learning models could distinguish migraine, TTH, and healthy controls. Methods: The study included 150 participants (61 migraine, 47 TTH, and 42 controls). Connectivity was calculated in the delta, theta, alpha, beta, and gamma bands using coherence, imaginary coherence, weighted phase-lag index (wPLI), and debiased wPLI. We evaluated global, regional, topographic, and graph-theoretical features, performed ROC-AUC analyses, and tested classification models with nested cross-validation. To limit the multiple-testing burden, false discovery rate correction was applied to a prespecified, hypothesis-driven ROI set. Results: Eight candidate ROI features remained significant after correction. TTH showed the highest gamma temporo-parietal coherence, whereas migraine showed higher gamma temporo-parietal wPLI and debiased wPLI and a higher exploratory migraine probability-like score. Controls had higher delta fronto-temporal and gamma fronto-temporal/temporo-parietal imaginary coherence. Single-feature ROC-AUC values were moderate, and the nested machine-learning models showed only modest classification performance. Conclusions: The results point to method-dependent, region-specific sensor-level EEG differences across migraine, TTH, and control groups. They are best viewed as candidate neurophysiological signatures rather than clinically ready biomarkers. External validation, fuller clinical covariate assessment, and source-level analyses are needed before diagnostic use can be considered. Future studies should also examine whether these candidate signatures differ according to aura status and episodic or chronic headache subtype. Full article