Search Results (406)

Traditional composite indicators for the study of sustainability often obscure the complex network of relationships among individual indicators, functioning as black boxes that fail to diagnose the underlying structural and functional weaknesses of the system. The objective of this research is to develop and apply a complementary approach grounded in network theory to diagnose and evaluate the structural and functional cohesion of environmental indicator systems. We developed a study that combines the Principal Component Analysis (PCA) method with network theory to comprehensively analyze the indicator system. The core of this contribution is the development of the $Mo\left(G\right)$ index, designed to quantify the structural–functional cohesion of an indicator network. This approach is applied to an environmental dataset of 19 indicators for Cruz Grande, Guerrero, Mexico (2010–2023). The results reveal that although the indicator network is relatively dense ($d=0.6199$), its structural–functional cohesion is low ($Mo\left(G\right)=520.68$), placing the region in the Fair category. This result provides an explanation for the sustained decline of the system, as shown by the PCA-based Regional Environmental Sustainability Index . We conclude that this approach is a complementary tool for diagnosing and evaluating environmental systems, enabling the detection of vulnerabilities that remain invisible to conventional aggregation methods. Full article

Background: The hemoglobin–albumin–lymphocyte–platelet (HALP) score integrates the immunonutritional and inflammatory status. We evaluated whether baseline HALP predicts the 3-month response after lumbar medial branch radiofrequency ablation (RFA), defined as a Visual Analogue Scale (VAS) reduction of ≥50% and an Oswestry Disability Index (ODI) reduction of ≥40%, and identified a Youden-optimal cut-off. The discrimination and calibration of multivariable models were also assessed. Methods: This single-center retrospective cohort (N = 120) included rigorously selected patients (≥50% pain relief after two comparative medial branch blocks) undergoing standardized RFA. Multivariable logistic regression was adjusted for age, sex, Body Mass Index (BMI), smoking status, paraspinal tenderness, and baseline scores. We quantified the Area Under the Receiver Operating Characteristic Curve (AUC), Hosmer–Lemeshow (HL) goodness-of-fit, Brier score, and calibration slope; optimism was corrected using a 500-bootstrap method. Results: Responses occurred in 64.2% (VAS) and 65.8% (ODI) of participants. HALP independently predicted ODI (OR = 1.06, 95% CI 1.02–1.09; p < 0.001) and VAS (OR = 1.05, 95% CI 1.02–1.08; p = 0.001). As a single predictor, HALP showed fair discrimination (AUC 0.717 [VAS], 0.731 [ODI]). The Youden cut-off of 39.8 yielded high sensitivity (~0.87) with modest specificity (~0.58–0.61). Multivariable AUCs were 0.744 (VAS) and 0.774 (ODI), optimism-corrected to 0.680 and 0.720; calibration was acceptable (HL p > 0.05; slopes ≈ 0.74–0.78; Brier 0.188/0.179). Conclusions: HALP is a simple, low-cost adjunct that independently predicts short-term pain and functional outcomes after lumbar medial branch RFA. Incorporation into post-block triage may refine selection, especially for functional improvement, pending prospective external validation and recalibration of the cut-off. Full article

Artificial Intelligence (AI) has achieved immense progress in recent years across a wide array of application domains, with biomedical imaging and sensing emerging as particularly impactful areas. However, the integration of AI in safety-critical fields, particularly biomedical domains, continues to face a major challenge of explainability arising from the opacity of complex prediction models. Overcoming this obstacle falls within the realm of eXplainable Artificial Intelligence (XAI), which is widely acknowledged as an essential aspect for successfully implementing and accepting AI techniques in practical applications to ensure transparency, fairness, and accountability in the decision-making processes and mitigate potential biases. This article provides a systematic cross-domain review of XAI techniques applied to quantitative prediction tasks, with a focus on their methodological relevance and potential adaptation to biomedical imaging and sensing. To achieve this, following PRISMA guidelines, we conducted an analysis of 44 Q1 journal articles that utilised XAI techniques for prediction applications across different fields where quantitative databases were used, and their contributions to explaining the predictions were studied. As a result, 13 XAI techniques were identified for prediction tasks. Shapley Additive eXPlanations (SHAP) was identified in 35 out of 44 articles, reflecting its frequent computational use for feature-importance ranking and model interpretation. Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDPs), and Permutation Feature Index (PFI) ranked second, third, and fourth in popularity, respectively. The study also recognises theoretical limitations of SHAP and related model-agnostic methods, such as their additive and causal assumptions, which are particularly critical in heterogeneous biomedical data. Furthermore, a synthesis of the reviewed studies reveals that while many provide computational evaluation of explanations, none include structured human–subject usability validation, underscoring an important research gap for clinical translation. Overall, this study offers an integrated understanding of quantitative XAI techniques, identifies methodological and usability gaps for biomedical adaptation, and provides guidance for future research aimed at safe and interpretable AI deployment in biomedical imaging and sensing. Full article

The rapid growth of PV generation in the distribution grid has necessitated PV curtailment to prevent overvoltage violations, and this has raised fairness issues as some are curtailed disproportionately to others. This paper proposes an adaptive PV curtailment scheme that balances fairness with energy sales using a Curtailment Index (CI) employed to reallocate curtailed energy between PV systems. The CI-based approach dynamically adapts each inverter’s output in real time to provide voltage compliance while ensuring that no individual PV system experiences an overburden of curtailment. The method is evaluated through MATLAB simulations on a three-PV test distribution network and validated experimentally on the PAGLIA ORBA solar microgrid, where its performance is compared to equal-curtailment and unfair strategies. The findings indicate that the adaptive method helps integrate high PV penetration more equitably and efficiently, ensuring stable grid operation while minimizing financial losses for PV owners. Full article

Flood events, intensified by climate change, pose significant threats to both human settlements and ecological systems. This study presents an integrated approach to evaluate flood impacts on ecosystem carbon dynamics using remote sensing and machine learning techniques. The case of the Emilia-Romagna region in Italy is presented, which experienced intense flooding in 2023. To understand flood-induced changes in the short term, we quantified the differences in net primary productivity (NPP) and above-ground biomass (AGB) before and after flood events. Short-term analysis of NPP and AGB revealed substantial localized losses within flood-affected areas. NPP showed a net deficit of 7.0 × 10³ g C yr⁻¹, and AGB a net deficit of 0.5 × 10³ Mg C. While the wider region gained NPP (6.7 × 10⁵ g C yr⁻¹), it suffered a major AGB loss (3.3 × 10⁵ Mg C), indicating widespread biomass decline beyond the flood zone. Long-term ecological assessment using the Remote Sensing Ecological Index (RSEI) showed accelerating degradation, with the “Fair” ecological class shrinking from 90% in 2014 to just over 50% in 2024, and the “Poor” class expanding. “Good” and “Very Good” classes nearly disappeared after 2019. High-hazard flood zones were found to contain 9.0 × 10⁶ Mg C in AGB and 1.1 × 10⁷ Mg C in soil organic carbon, highlighting the vulnerability of carbon stocks. This study underscores the importance of integrating flood modeling with ecosystem monitoring to inform climate-adaptive land management and carbon conservation strategies. It represents a clear, quantifiable carbon loss that should be factored into regional carbon budgets and post-flood ecosystem assessments. Full article

Hospital fraud detection has often relied on periodic audits that miss evolving, internet-mediated patterns in electronic claims. An artificial intelligence and machine learning pipeline is being developed that is leakage-safe, imbalance aware, and aligned with operational capacity for large healthcare datasets. The preprocessing stack integrates four tables, engineers 13 features, applies imputation, categorical encoding, Power transformation, Boruta selection, and denoising autoencoder representations, with class balancing via SMOTE-ENN evaluated inside cross-validation folds. Eight algorithms are compared under a fraud-oriented composite productivity index that weighs recall, precision, MCC, F1, ROC-AUC, and G-Mean, with per-fold threshold calibration and explicit reporting of Type I and Type II errors. Multilayer perceptron attains the highest composite index, while CatBoost offers the strongest control of false positives with high accuracy. SMOTE-ENN provides limited gains once representations regularize class geometry. The calibrated scores support prepayment triage, postpayment audit, and provider-level profiling, linking alert volume to expected recovery and protecting investigator workload. Situated in the Future Internet context, this work targets internet-mediated claim flows and web-accessible provider registries. Governance procedures for drift monitoring, fairness assessment, and change control complete an internet-ready deployment path. The results indicate that disciplined preprocessing and evaluation, more than classifier choice alone, translate AI improvements into measurable economic value and sustainable fraud prevention in digital health ecosystems. Full article

Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are graded per the World Health Organization (WHO) using mitotic count and the Ki-67 index. There is an ongoing debate regarding the concordance between these parameters and their ability to predict metastatic disease. Objective: The objective is to assess concordance between the mitotic count and the Ki-67 index in grading GEP-NETs and to determine which parameter more accurately relates to metastatic disease and local tumor behavior. Methods: We conducted a single-center retrospective cohort study of adults with GEP-NETs managed between January 2006 and February 2024. Tumors were staged according to the TNM system. Grading followed WHO criteria using mitotic count and the Ki-67 index; when discordant, the higher grade was assigned. Results: Concordance between mitotic count- and Ki-67-based grading was 76.5% (78/102) with Cohen’s κ = 0.36, indicating fair-to-moderate agreement. More tumors were classified as G1 by mitotic count (86.3%) than by the Ki-67 index (68.6%). Neither mitotic count nor the Ki-67 index (numerical values or grades) showed a significant association with metastatic disease (all p > 0.05). Mitotic count (as a numerical continuous values) correlated with tumor invasion (T1 vs. T3, p = 0.035; T1 vs. T4, p = 0.036), whereas the Ki-67 index did not (p = 0.11). Tumor size was the strongest predictor of metastases (lymph-node p = 0.028; distant p < 0.001; any p < 0.001). Conclusions: Mitotic count and the Ki-67 index show only 76.5% concordance. Neither marker predicted metastatic disease in this cohort, while tumor size was the most robust predictor. These findings support giving greater weight to tumor size within prognostic algorithms while recognizing the limitations of proliferation-based grading for predicting metastasis. Full article

Halcyon days are characterized by periods of one to three or more consecutive, typically sunny and mild days, occurring during winter (from 15 December to 15 February) in Attica, the region where Athens is located. We examined meteorological data from the station of the National Observatory of Athens in Thission, over a 54-year period, applying criteria that include daily maximum temperatures equal to or greater than 12 °C, minimum temperatures equal to or greater than 4 °C, wind speeds equal to or less than 6 knots, and mean total cloudiness (between 06:00 and 18:00 GMT) equal to or less than 3.2 oktas. This analysis identified all Halcyon days that occurred during this period. Our statistical study revealed the annual evolution of these days and any possible relationship with climate change. We focused on understanding the dynamics of the atmospheric circulation processes associated with the occurrence of Halcyon days. The primary atmospheric circulation feature responsible for the generation of Halcyon days appears to be the establishment of a subtropical anticyclone over Greek territory. Full article

(1) Background: The impact of different drying methods on the functional properties and microstructure of milk powders was analyzed in this study. (2) Methods: Whole milk, skim milk, and buttermilk powders were obtained by freeze drying, spray drying, and roller drying. (3) Results: The examined powders differed in chemical composition, and these differences were attributed mainly to their fat content. The functional properties of the studied powders were determined mainly by the drying method and were less influenced by their composition. Loose and tapped bulk density was highest in roller-dried powders and lowest in freeze-dried powders. The flowability of milk powders was determined by calculating the Carr index and the Hausner ratio, and the results were used to classify the analyzed powders into the following groups: poorly flowing and cohesive (spray-dried samples), passable (roller-dried samples), and fair (freeze-dried samples). The volume of insoluble particles was highest in roller-dried powders and much lower in spray-dried powders, whereas freeze-dried powders were 99.8–99.9% soluble in water. Whole milk powder was characterized by low wettability (>180 s) regardless of the drying method. Powder morphology was influenced mainly by the drying method. (4) Conclusions: The fractal analysis demonstrated that spray-dried powders had the smallest fractal dimensions, which implies that their surface was least complex (most uniform). Regardless of the drying method, fractal dimensions were highest in whole milk powder, which could suggest that fat affects the microstructure of powders. The color parameters of milk powders were determined mainly by the drying method and were less influenced by the type of raw material used in powder production. Full article

Objective: This study aimed to study the effectiveness of a smartphone application compared to traditional verbal motivation in improving oral hygiene among fixed orthodontic patients. Methods: Sixty patients were categorized by oral hygiene status using the simplified oral hygiene index (OHI-S) and randomly assigned to either the Dentabuddy group (smartphone application) or the assistant-based training (ABT) group (conventional oral hygiene motivation). Gingival index (GI), plaque index (PI), and gingival bleeding index (GBI) values were recorded at baseline, one month, and three months. Toothbrushing technique was assessed at the three-month follow-up. Results: After three months, the Dentabuddy group exhibited significant GI reductions in participants with fair and poor oral hygiene, whereas the ABT group improved only in those with poor hygiene (p < 0.05). PI values decreased significantly in both groups, except in the ABT group with good and fair hygiene. GBI values improved in both groups, except in the ABT group with fair and poor hygiene (p < 0.05). Toothbrushing demonstrations showed superior technique in the Dentabuddy group (p < 0.05). Conclusions: The Dentabuddy application positively influenced oral hygiene, particularly in individuals with fair and poor hygiene, compared to ABT. This study underscores the potential of smartphone applications in enhancing periodontal health outcomes beyond traditional oral hygiene methods in orthodontic patients with fair or poor hygiene. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the unique conflict between the preservation of well-known historical and cultural cities and the development of greening. It does this by integrating streetscape big data (2925 sampling points) and point of interest (POI) density data (57,266 records) and using the DeepLab-ResNeSt269 semantic segmentation model in conjunction with spatial statistical techniques (Moran’s Index, Locational Entropy and Theil Index Decomposition) to quantitatively analyze the spatial equity of the green view index (GVI) in Kaifeng City. The results of the study show that (1) The Theil Index reveals that the primary contradiction in Kaifeng City’s distribution pattern—low GVI in the center and high in the periphery—is the micro-street scale difference, suggesting that the spatial imbalance of the GVI is primarily reflected at the micro level rather than the macro urban area difference. (2) The distribution of the GVI in Kaifeng City exhibits a significant spatial polarization phenomenon, with the proportion of low-value area (35.40%) being significantly higher than that of high-value area (25.10%) and the spatial clustering being evident (Moran’s Index 0.3824). Additionally, the ancient city area and the new city area exhibit distinct spatial organization patterns. (3) POI density and GVI had a substantial negative correlation (r = −0.085), suggesting a complicated process of interaction between green space and urban functions. The study reveals that the fairness of green visibility in historical and cultural cities presents the characteristics of differentiated distribution in different spatial scales, which provides a scientific basis for the optimization of greening spatial layouts in historical and cultural cities while preserving the traditional landscape. Full article

Background: Alterations in peripheral red blood cell (RBC) indices have been proposed as potential biomarkers for Parkinson’s disease (PD), but their diagnostic utility and relation to clinical features remain uncertain. Methods: We conducted a pilot case–control study involving 70 PD patients and 122 controls from two neurology centers in Mexico. Standardized hematology analyses provided RBC indices, and neuropsychiatric assessments included the Hamilton Depression Rating Scale (HAM-D) and Mini-Mental State Examination (MMSE). Associations between RBC indices and PD were tested using multivariable logistic regression adjusted for age, sex, and smoking. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. Subgroup analyses stratified PD patients by age at onset, disease duration, and Hoehn and Yahr (HY) stage. Results: PD patients exhibited significantly higher mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) than controls. Elevated MCHC was independently associated with PD (OR = 1.68, 95% CI 1.35–2.09; p < 0.001). Sex-stratified models confirmed consistent associations in women (OR = 1.57) and men (OR = 1.79). ROC analysis demonstrated fair diagnostic accuracy for MCHC (AUC 0.72, 95% CI 0.65–0.80; cutoff 33.9 g/dL, sensitivity 62.9%, specificity 72.1%). Sex-specific thresholds improved sensitivity in women (90.6%) and specificity in men (74.6%). Within the PD group, MCHC did not differ by HY stage or disease duration, and showed no correlation with UPDRS, HAM-D, or MMSE scores. Early-onset cases (<50 years) showed numerically higher MCHC, though numbers were limited. Conclusions: This pilot study confirms that an elevated MCHC is independently associated with PD, a finding consistent across both sexes and independent of disease severity. MCHC demonstrates fair diagnostic performance, supporting its potential as a low-cost, accessible biomarker. Larger longitudinal studies integrating RBC indices with inflammatory and iron-regulatory markers are warranted to establish their role in the diagnosis and differential diagnosis of PD. Elevated MCHC was associated with PD, and an MCHC-based index (cutoff 33.9 g/dL; AUC 0.72, sensitivity 62.9%, specificity 72.1%) showed potential as a simple diagnostic marker. Full article

The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G Cognitive Radio Networks (CRNs). This paper introduces a novel hybrid framework combing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Proximal Policy Optimisation (PPO) for multi-objective optimisation in spectrum management. The proposed model balances spectrum efficiency, interference mitigation, energy conservation, collision rate reduction, and QoS maintenance. Evaluation on synthetic and ns-3 datasets shows that the NSGA-II and PPO hybrid consistently outperforms the random, greedy, and stand-alone PPO strategies, achieving higher cumulative reward, perfect fairness (Jain’s Fairness Index = 1.0), robust hypervolume convergence (65.1%), up to 12% reduction in PU collision rate, 20% lower interference, and approximately 40% improvement in energy efficiency. These findings validate the framework’s effectiveness in promoting fairness, reliability, and efficiency in 6G wireless communication systems. Full article

Background/Objectives: In Spain, a high proportion of children do not meet the recommended daily levels of physical activity (PA), which highlights the urgent need to understand the motivational factors that could influence PA behavior. Self-Determination Theory is a widely used approach for assessing motivation toward exercise, employing instruments such as the Behavioral Regulation in Exercise Questionnaire (BREQ-3). However, despite the cognitive and linguistic differences that limit its direct application, this tool has not yet been adapted for children aged 6–12 years. This study aimed to adapt the BREQ-3 for use with Spanish schoolchildren and to evaluate its validity and reliability in this age group. Methods: The BREQ-3 for children (BREQ-3C) was linguistically and culturally adapted. Comprehension was tested through cognitive interviews, and reliability was assessed via a test–retest with 125 Spanish schoolchildren. Statistical analyses: Confirmatory factor analysis (CFA), Cronbach’s alpha, and the intraclass correlation coefficient (ICC) were used to evaluate validity and reliability. Results: CFA supported the factorial structure of the adapted BREQ-3 for primary schoolchildren, showing acceptable model fit indices (chi-square minimum discrepancy/degrees of freedom (CMIN/df) = 1.552, root mean square error of approximation (RMSEA) = 0.053, comparative fit index (CFI) = 0.891, Tucker-Lewis index (TLI) = 0.870). Internal consistency ranged from poor to excellent for all items and the total score of the questionnaire (Cronbach’s alpha (α): 0.535 to 0.911), except for items 3, 13, 20, and 21, where the internal consistency was unacceptable. Test–retest reliability was generally satisfactory, with ICC values indicating fair to excellent temporal stability (ICC: 0.248 to 0.911). The measurement error indicators (standard error of measurement percentage (SEM%) and minimal detectable change percentage (MDC%)) varied widely, particularly for the less reliable items. Most item scores were not significantly different between the test and retest groups, although items 2, 3, 5, 9, 17, 19, and 20 were significantly different. Conclusions: The BREQ-3C has promising psychometric properties for assessing exercise motivation in children aged 6–12 years. This tool shows potential for use in research, education, and health interventions to understand and promote physical activity motivation in primary schools. Full article