Search Results (890)

The purpose of this research was to investigate whether music empirically associated with therapeutic effects contains intrinsic informational structures that differentiate it from other sound sequences. Drawing on ontology, phenomenology, nonlinear dynamics, and complex systems theory, we hypothesize that therapeutic relevance may be linked to persistent structural patterns embedded in musical signals rather than to stylistic or genre-related attributes. This paper introduces the Discriminating Music Sequences (DiMuSes) method, an unsupervised, structure-oriented analytical framework designed to detect such patterns. The method applies 24 scalar evaluators derived from statistics, fractal geometry, nonlinear physics, and complex systems, transforming sound sequences into multidimensional vectors that characterize their global temporal organization. Principal Component Analysis (PCA) reduces this feature space to three dominant components (PC1–PC3), enabling visualization and comparison in a reduced informational space. Unsupervised k-Means clustering is subsequently applied in the PCA space to identify groups of structurally similar sound sequences, with cluster quality evaluated using Silhouette and Davies–Bouldin indices. Beyond clustering, DiMuSe implements ranking procedures based on relative positions in the PCA space, including distance to cluster centroids, inter-item proximity, and stability across clustering configurations, allowing melodies to be ordered according to their structural proximity to the therapeutic cluster. The method was first validated using synthetically generated nonlinear signals with known properties, confirming its capacity to discriminate structured time series. It was then applied to a dataset of 39 music and sound sequences spanning therapeutic, classical, folk, religious, vocal, natural, and noise categories. The results show that therapeutic music consistently forms a compact and well-separated cluster and ranks highly in structural proximity measures, suggesting shared informational characteristics. Notably, pink noise and ocean sounds also cluster near therapeutic music, aligning with independent evidence of their regulatory and relaxation effects. DiMuSe-derived rankings were consistent with two independent studies that identified the same musical pieces as highly therapeutic.The present research remains at a theoretical stage. Our method has not yet been tested in clinical or experimental therapeutic settings and does not account for individual preference, cultural background, or personal music history, all of which strongly influence therapeutic outcomes. Consequently, DiMuSe does not claim to predict individual efficacy but rather to identify structural potential at the signal level. Future work will focus on clinical validation, integration of biometric feedback, and the development of personalized extensions that combine intrinsic informational structure with listener-specific response data. Full article

Background: Paragangliomas of the head and neck are rare, benign and indolent to slow-growing tumors. Not all tumors require immediate active intervention, and surveillance is a viable management strategy in a large proportion of cases. Treatment decisions are based on several tumor- and patient-related factors, with the tumor progression rate being a predominant determinant. Accurate prediction of tumor progression has the potential to significantly improve treatment decisions by helping to identify patients who are likely to require active treatment in the future. It furthermore enables better-informed timing for follow-up, allowing early intervention for those who will ultimately need it, and optimization of the use of resources (such as MRI scans). Crucial to this is having reliable estimates of the uncertainty associated with a future growth forecast, so that this can be taken into account in the decision-making process. Methods: For various tumor growth prediction models, two methods for uncertainty estimation were compared: a historical-based one and a Bayesian one. We also investigated how incorporating either tumor-specific or general estimates of auto-segmentation uncertainty impacts the results of growth prediction. The performance of the uncertainty estimates was examined both from a technical and a practical perspective. Study design: Method comparison study. Results: Data of 208 patients were used, comprising 311 paragangliomas and 1501 volume measurements, resulting in 2547 tumor growth predictions (a median of 10 predictions per tumor). As expected, the uncertainty increased with the length of the prediction horizon and decreased with the inclusion of more tumor measurement data in the prediction model. The historical method resulted in estimated confidence intervals where the actual value fell within the estimated 95% confidence interval 94% of the time. However, this method resulted in confidence intervals that were too wide to be clinically useful (often over 200% of the predicted volume), and showed poor ability to differentiate growing and stable tumors. The estimated confidence intervals of the Bayesian method were much narrower. However, the 95% credible intervals were too narrow, with the true tumor volume falling within them only 78% of the time, indicating underestimation of uncertainty and insufficient calibration. Despite this, the Bayesian method showed markedly better ability to distinguishing between growing and stable tumors, which has arguably the most practical value. When combining all growth models, the Bayesian method using tumor-specific auto-segmentation uncertainties resulted in an 86% correct classification of growing and non-growing tumors. Conclusions: Of the methods evaluated for predicting paraganglioma progression, the Bayesian method is the most useful in the considered context, because it shows the best ability to discriminate between growing and non-growing tumors. To determine how these methods could be used and what their value is for patients, they should be further evaluated in a clinical setting. Full article

Amid the expansive evolution of the digital economy and the emergence of enhanced productivity paradigms, exploring the ways in which digital technology affordance propels corporate digital innovation via multifaceted cooperative routes is essential for reconfiguring industrial ecosystems, securing digital market advantages, and promoting superior advancement. This investigation employs the TOE model, merging fuzzy-set qualitative comparative analysis (fsQCA) with regression analysis. Using data from 2206 listed manufacturing companies from the A-share exchanges (2010–2023), it identifies multiple antecedent configuration pathways of digital technology affordance and examines their differential impacts on enterprise digital innovation. Key findings include the following: (1) no solitary factor serves as an obligatory prerequisite for high-quality digital technology affordance. (2) Four configuration pathways were identified: technology-organization-environment tripartite-propelled, technology-organization collaborative-propelled, technology-environment collaborative-propelled, and organization-environment collaborative-propelled variants. (3) The influence of digital technology affordance on digital innovation shows conditional dependence. Under the ternary-driven “technology-organization-environment” or synergy-driven “technology-organization” configurations, and absent conflicting enterprise goals, digital technology affordance promotes digital product innovation. Supported by collaborative configurations of technological investment, digital infrastructure, highly educated talent, institutional measures, and public service efficiency, it fosters digital process innovation. However, isolated technological investment, employees’ educational attainment, and institutional measures inhibit business model innovation. Other configurations lack significant impacts on digital business model innovation. This study elucidates the generation mechanism of digital technology affordance using configuration theory, offering empirical insights for managers to enhance digital innovation and drive high-quality economic development. The study enhances the theoretical depth by exploring technological foundations of digital technologies and addressing generalizability through framework adaptations for global contexts. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

Visual attention mechanisms are modulated by chromatic properties of the environment, with significant implications for human–computer interaction, interface design, and cognitive ergonomics. Despite extensive research on color perception, a critical gap remains in understanding how background colors differentially affect initial attentional capture versus sustained processing efficiency during text reading. This study investigates how seven different background colors (yellow, orange, red, green, blue, purple, and black) influence visual attention and cognitive load during standardized reading tasks with white text, revealing a fundamental asymmetry in chromatic processing stages. Using high-frequency eye-tracking at 120 Hz with 30 participants in a within-subjects design, we measured time-to-first fixation, total viewing duration, fixation count, and revisitation frequency across chromatic conditions. Non-parametric statistical analyses (Friedman test for omnibus comparisons, Wilcoxon signed-rank test for pairwise comparisons) revealed a systematic dissociation between preattentive capture and sustained processing. Yellow backgrounds enabled the fastest initial attentional capture ($0.65$ s), while black backgrounds produced the slowest detection ($1.75$ s). However, this pattern reversed during sustained processing: black backgrounds enabled the shortest total viewing time ($0.88$ s) through efficient information sampling (median $5.0$ fixations), while yellow required the longest viewing duration ($1.75$ s) with fewer fixations (median $3.0$). Statistical comparisons confirmed significant differences across conditions (Friedman test: ${\chi }^2\left(6\right)=138.4$–$154.2$, all $p<0.001$; pairwise comparisons with Bonferroni correction: $\alpha =0.0024$). We note that luminance and chromatic contrast were not independently controlled, as colors inherently vary in both dimensions in realistic interface design. Consequently, the observed effects reflect the combined influence of hue, saturation, and luminance contrast as they naturally co-occur. These findings reveal a descriptive pattern consistent with functionally distinct mechanisms, where chromatic salience appears to facilitate preattentive capture while luminance contrast appears to determine sustained processing efficiency, with optimal colors for one stage being suboptimal for the other under the present experimental conditions. This observed chromatic asymmetry suggests potential implications for interface design: warm colors like yellow may optimize rapid attention capture for alerts and warnings, while high-contrast combinations like white-on-black may optimize sustained reading efficiency, though these preliminary patterns require validation across diverse contexts. Green and purple backgrounds offer balanced performance across both processing stages, representing near-symmetric solutions suitable for mixed-task interfaces. Given the controlled laboratory setting, university student sample, and 15 s exposure duration, design recommendations should be considered preliminary and validated in diverse real-world contexts. Full article

A conditional framework of Conditional Cosmological Recurrence (CCR) is introduced, as follows: if a causal patch admits a finite operational Hilbert space dimension D (as motivated by holographic and entropy bounds), then unitary quantum dynamics guarantee almost-periodic evolution, leading to recurrences. The central contribution is the explicit formulation of a micro-to-macro bridge, as follows: (i) finite regions discretize field modes; (ii) gravitational bounds cap entropy and energy; and (iii) the number of accessible states is finite, yielding CCR. The analysis differentiates global microstate recurrences (with double-exponential timescales in $S_{max}$) from operationally relevant coarse-grained returns (exponential in subsystem entropy), with conservative timescale estimates. For predictivity in eternally inflating settings, a causal-diamond measure with xerographic typicality and a single no-Boltzmann-brain constraint is employed, thereby avoiding volume-weighting pathologies. The scope is explicitly conditional: if future quantum gravity demonstrates $D=\infty$ for causal patches, CCR is falsified. Full article

In this study, the authors wish to introduce an unsupervised procedure designed for real-time generation of maps depicting advected aerosols, specifically focusing on desert dust and smoke originating from biomass combustion. This innovative approach leverages the high-resolution capabilities provided by Sentinel-2 imagery, operating at a 10 m scale, which is particularly advantageous for urban settings. Concurrently, it takes advantage of the near-daily revisit frequency afforded by Sentinel-3. The methodology involves generating aerosol maps at a 10 m resolution using bands 2, 3, 4, and 5 of Sentinel-2, available in L1C and L2A formats, conducted every five days, contingent upon the absence of cloud cover. Subsequently, this map is enhanced every two days through spatial modulation, utilizing a similar map derived from the visible and near-infrared observations (VNIR) captured by the OLCI instrument aboard Sentinel-3, which is accessible at a 300 m scale. Data from the two satellites undergo independent processing, with integration at the feature level. This process combines Sentinel-3 and Sentinel-2 maps to update aerosol concentrations in each 300 m × 300 m grid every two days or more frequently. For the dates when Sentinel-2 data is unavailable, the spatial texture or the aerosol distribution within these grid cells is extrapolated. This spatial index represents an advancement over prior studies that focused on differentiating between dust and smoke based on their scattering and absorption characteristics. The entire process is rigorously validated by comparing it with point measurements of fine- and coarse-mode Aerosol Optical Depth (AOD) obtained from AERONET stations situated at the test sites, ensuring the reliability and accuracy of the generated maps. Full article

Background/Objectives: Hypoaldosteronism is classified into “aldosterone deficit” (Aldo-D) and “aldosterone/mineralocorticoid resistance” (Aldo-R) based on etiopathogenic mechanisms. This distinction could be useful for guiding the treatment. However, no reliable methods have been established to differentiate these subtypes. We first aimed to assess whether aldosterone levels could help identify them when assessed in the setting of hyperkalemia or hyperreninemia. Methods: We conducted a retrospective analysis of eighty-four cases of hypoaldosteronism. Aldo-D and Aldo-R classification was based on the presence of clinical factors associated with aldosterone deficit and mineralocorticoid resistance, respectively. The accuracy of plasma aldosterone (PAC) to identify each type of hypoaldosteronism individually was evaluated using AUC-ROC analysis. Results: Aldo-D was identified in 66 (78.6%), and Aldo-R in 41 (48.8%) cases. Factors related to both subtypes were observed in forty-seven (56%) cases. AUC-ROC analysis of PAC measured during hyperkalemia showed low accuracy for detecting either subtype. During hyperreninemia, PAC accuracy was adequate for identifying Aldo-D but unsatisfactory for Aldo-R. Nevertheless, a PAC ≤ 60 pg/mL (6 ng/dL, ~166 pmol/L) during hyperkalemia and hyperreninemia yielded positive predictive values (PPV) of 94% and 100%, respectively, for Aldo-D, while a PAC value > 160 pg/mL (~443 pmol/L), particularly ≥ 200 pg/mL (20 ng/dL, ~550 pmol/L) in either condition had a PPV of 100% for Aldo-R. Conclusions: Although overall diagnostic accuracy was limited, extreme low and high PAC values (≤ 60 pg/mL or ≥ 200 pg/mL) may be suggestive of Aldo-D or Aldo-R, respectively, while intermediate values remain difficult to interpret due to substantial overlap. Full article

Mobile Mapping Systems (MMSs) have emerged as indispensable instruments for producing high-precision road maps in recent years. Despite incorporating modern devices, their efficacy may be influenced by operational variables such as vehicle speed or the type of GNSS (Global Navigation Satellite System) differential correction employed. This study assesses the impact of varying vehicle speeds and differential correction settings on the accuracy of point grids acquired with an MMS on a two-lane rural road. The experiment was performed across a 7 km distance, incorporating two speeds (40 and 60 km/h) and two travel directions. Three correction methodologies were examined: a proximate local base (MBS), a network station solution of the National Geographic Institute (NET), and virtual reference stations (VRSs). The methodology encompassed normality analysis, descriptive statistics, mean comparisons, one- and two-factor analysis of variance (ANOVA), and the computation of the root mean square error (RMSE) as a measure of accuracy. The findings indicate that horizontal discrepancies remain steady and unaffected by the correction technique; however, notable changes are seen in the vertical component, with the NET option proving to be the most effective. The acquisition rate is the primary determinant, exacerbating errors at 60 km/h. In conclusion, the dependability of MMS surveys is contingent upon the correction approach and operational conditions, and it is advisable to sustain moderate speeds to guarantee precise three-dimensional models. Full article

Platform-enabled governance is reshaping destination management, yet subnational destinations still lack replicable dashboards that combine key performance indicators (KPIs) with efficiency analysis. This study examines whether a compact KPI stack coupled with longitudinal Data Envelopment Analysis (DEA) can provide actionable targets for destination development management and marketing organizations (DDMMOs). Using 2020–2024 administrative data for five regional units of the Peloponnese, an output-oriented CRS DEA model is specified with one input (room capacity) and two outputs (tourism revenue and overnight stays), complemented by a VRS specification that decomposes Overall Technical Efficiency into Pure Technical and Scale Efficiency. The results show a clear differentiation in trajectories: one regional unit remains consistently on the efficiency frontier, and others exhibit gradual convergence towards best practice, while at least one unit displays persistent underperformance that is driven primarily by scale rather than managerial inefficiency. These distances to frontier are transformed into proportional, output-specific targets and dynamically updated peer sets, which are integrated into a KPI dashboard to support a continuous measure–act–learn loop on pricing, promotion, and capacity allocation. Overall, the article proposes a transparent, reproducible template that links destination competitiveness frameworks with a multi-input, multi-output efficiency lens and embeds KPIs and dynamic DEA insights in a continuous governance loop for destination management. Full article

Background: Wearable EEG devices offer an accessible and unobtrusive system for regular brain health monitoring outside clinical settings. However, due to the current lack of data available from wearable low-density EEG devices, we need to anticipate the extraction of biomarkers for brain health evaluation from available clinical datasets. Methods: This study evaluates multiclass dementia classification of Alzheimer’s disease, frontotemporal dementia, and healthy controls using features derived from low-density temporal EEG electrodes as a proxy for wearable EEG setups. The feature set comprises power-based metrics, including the 1/f spectral slope, and complexity metrics such as Hjorth parameters and multiscale sample entropy. Results: Our results show that multiclass differentiation of dementia, using low-density electrode configurations restricted to temporal regions, can achieve results comparable to a full-scalp configuration. Notably, electrode T5, positioned over the left temporo-posterior region, consistently outperformed other configurations, achieving a subject-level accuracy of 83.3% and an F1 score of 82.4%. Conclusions: These findings highlight the potential of single-site EEG measurement for wearable brain health devices. Full article

The aim of this work is to implement decision tree classifiers (DTCs) capable of distinguishing bee honey by geographical origin. The case study focuses on honeys from the lowland and highland regions of Chiriquí, Panama. Characterization was conducted by analyzing their typical physicochemical and aromatic profiles using AOAC, IHC, and e-Nose methodologies, respectively. Data mining provided insights into the most relevant features, enabling the reduction of an otherwise extensive and resource-intensive dataset. The critical markers identified include reducing sugars, ash, antioxidant capacity, HMF, as well as aromatic, aliphatic, hydrocarbon, and sulfur compounds. This simplified set of features produced an intuitive classification scheme, achieving up to 86% accuracy. This proof-of-concept demonstrates that interpretable models can effectively leverage easily measurable characteristics for regional differentiation, offering a valuable tool for traceability in the Panamanian honey industry. Full article

Ultrasound is the primary, non-invasive imaging modality for evaluating renal anatomy and function in both acute and chronic settings. Familiarity with normal kidney morphology, cortical and parenchymal thickness, echogenicity, and Doppler parameters is essential for differentiating normal findings from early manifestations of disease. This review summarizes established reference ranges and anatomical variants from the 1950s to 2025, highlighting differences related to age, sex, body habitus, and ethnicity. Practical emphasis is placed on the interpretation of renal size, cortical thickness, echogenicity, and resistive indices in clinical scenarios such as chronic kidney disease, renovascular hypertension, acute obstruction, and renal transplantation. By integrating sonographic measurements with clinical and laboratory findings, clinicians can achieve timely diagnosis, monitor disease progression, and guide therapeutic decisions while minimizing the need for invasive or radiation-based imaging. Full article

We study statistical dependence in the frequency domain using the integral probability metric (IPM) framework. We propose the uniform Fourier dependence measure (UFDM) defined as the uniform norm of the difference between the joint and product-marginal characteristic functions. We provide a theoretical analysis, highlighting key properties, such as invariances, monotonicity in linear dimension reduction, and a concentration bound. For the estimation of the UFDM, we propose a gradient-based algorithm with singular value decomposition (SVD) warm-up and show that this warm-up is essential for stable performance. The empirical estimator of UFDM is differentiable, and it can be integrated into modern machine learning pipelines. In experiments with synthetic and real-world data, we compare UFDM with distance correlation (DCOR), Hilbert–Schmidt independence criterion (HSIC), and matrix-based Rényi’s $\alpha$-entropy functional (MEF) in permutation-based statistical independence testing and supervised feature extraction. Independence test experiments showed the effectiveness of UFDM at detecting some sparse geometric dependencies in a diverse set of patterns that span different linear and nonlinear interactions, including copulas and geometric structures. In feature extraction experiments across 16 OpenML datasets, we conducted 160 pairwise comparisons: UFDM statistically significantly outperformed other baselines in 20 cases and was outperformed in 13. Full article

A modified Enzyme Action Optimizer (mEAO) is proposed to tune a Fractional-Order Proportional–Integral–Derivative (FOPID) controller for precise temperature regulation of a nonlinear continuous stirred tank reactor (CSTR). The nonlinear reactor model, adopted from a standard benchmark formulation widely used in CSTR control studies, is employed as the simulation reference. The tuning framework operates in a simulation-based manner, as the optimizer relies solely on the time-domain responses to evaluate a composite cost function combining overshoot, settling time, rise time, and steady-state error. Comparative simulations involving EAO, Starfish Optimization Algorithm (SFOA), Success History-based Adaptive Differential Evolution with Linear population size reduction (L-SHADE), and Particle Swarm Optimization (PSO) demonstrate that the proposed mEAO achieves the lowest cost value, the fastest convergence, and superior transient performance. Further comparisons with classical tuning methods, Rovira 2DOF-PID, Ziegler–Nichols PID, and Cohen–Coon PI, confirm improved tracking accuracy and smoother actuator behavior. Robustness analyses under varying set-points, feed-temperature disturbances, and measurement noise confirm stable temperature regulation without retuning. These findings demonstrate that the mEAO-based FOPID controller provides an efficient and reliable optimization framework for a nonlinear thermal-process control, with strong potential for future real-time and multi-reactor applications. Full article