Search Results (8,250)

Background/Objectives: Early Childhood Intervention (ECI) services are critical for supporting children with developmental needs and their families. Despite an established legislative framework, challenges related to accessibility, equity, resources, and standardization of practices persist. This study aimed to describe the perspectives of early intervention professionals in Portugal regarding current barriers, facilitators, and priority areas for improvement within the system. Methods: A descriptive study was conducted involving 82 professionals working in early intervention in Portugal. Data were collected using a survey specifically developed by the research team, grounded in a comprehensive literature review and professional expertise. The instrument was validated through a Delphi Panel with two rounds involving six experts in ECI. Data from open-ended questions were analyzed using content analysis, identifying categories and sub-categories to describe the responses, and descriptive statistics for the closed-ended questions. Results: Professionals highlighted the need to update the National ECI System (SNIPI), improve accessibility, and ensure equitable access to early intervention services. Participants reported limited resources, a lack of standardization in practices, and emphasized the importance of professional training and continuous professional development. The findings also pointed to the urgent need for investment and functional and structural restructuring of early intervention services. Various barriers and facilitators were identified. Conclusions: The study provides valuable insights into the perspectives of early intervention professionals, identifying critical areas for policy improvement, resource allocation, and practice standardization. Full article

Hierarchical federated learning (HFL) combined with the Mobile Edge Computing (MEC) paradigm has attracted extensive research interest in the Industrial Internet of Things (IIoT) due to its ability to deploy computational resources near edge devices and effectively reduce communication overhead. However, in real-world applications, the dynamic participation of edge devices and their diverse training objectives can lead to instability in model convergence, affecting overall system performance. To address this challenge, this paper proposes a device selection strategy based on task completion probability to determine participating devices dynamically in each training round. Furthermore, to balance system resource consumption and model performance, we formulate an optimization objective to minimize the loss function under resource constraints. By leveraging theoretical analysis, we reformulate the objective as a loss upper bound minimization problem related to resource allocation, which is subsequently decomposed into multiple subproblems for iterative solving. Simulation results demonstrate that the proposed method achieves superior resource efficiency and training stability. Compared to the state-of-the-art HFL method, DSRA-HFL reduces the average training delay by approximately 18% and energy consumption by 22% under dynamic conditions, while maintaining a competitive model accuracy. This validates the effectiveness of our joint optimization strategy in practical IIoT scenarios. Full article

Croatia’s tourism market is highly exposed to digital platforms and peer-to-peer information flows, yet evidence on how Croatian users differentiate between promotional formats (digital channels, agency websites, traditional media and word-of-mouth) remains fragmented and rarely translated into actionable priorities. This study aims to identify the underlying dimensions of perceived promotional influence and to prioritize promotional formats for destination choice in Croatia by integrating PCA and the Analytic Hierarchy Process (AHP). An online survey (N = 299) was used to extract promotional dimensions via PCA and to test group differences by gender, age and primary information source, while AHP translated expert judgments into a comparative priority structure. Results consistently indicate that word-of-mouth is the most persuasive driver of destination choice, but its perceived importance varies significantly across demographic segments and information-source profiles. Younger respondents place greater emphasis on digital channels (especially social media and travel agency websites), whereas older respondents show higher reliance on traditional formats. The combined PCA–AHP approach provides a structured bridge between user perceptions and managerial prioritization, offering segment-specific guidance for more efficient allocation of promotional resources in Croatian destination marketing. Full article

The deployment of transformer-based language models on resource-constrained edge devices presents fundamental challenges in computational efficiency and memory utilization. We introduce SQ-LoRA (Stable-rank Quantized Low-Rank Adaptation), a theoretically grounded compression framework that achieves unprecedented efficiency through the synergistic integration of adaptive low-rank decomposition, hardware-accelerated structured sparsity, and intelligent hybrid quantization. Our primary contribution establishes the first rigorous mathematical connection between the matrix stable rank and optimal LoRA rank selection, formalized in Theorem I, which provides bounded approximation guarantees. SQ-LoRA implements: (1) adaptive rank allocation via stable-rank analysis to automatically determine layer-wise compression ratios; (2) 4:8 structured sparsity patterns, enabling 2× hardware acceleration on modern edge processors; and (3) a three-tier quantization scheme that combines 4-bit NormalFloat storage with selective 3-bit/8-bit precision to preserve outliers. A comprehensive evaluation on four diverse natural language processing (NLP) benchmarks demonstrates that SQ-LoRA achieves a 320 MB memory footprint (96.7% reduction) and a 10 ms inference latency (91.7% improvement), and maintains 82.0% average accuracy (within 0.15% of the full model). Statistical significance testing (p < 0.001) confirms its superiority over state-of-the-art methods. This framework enables the deployment of sophisticated language models on devices with 2 GB of RAM, advancing practical edge-AI applications. Full article

Stock returns are a key indicator of investor confidence and capital allocation in the tourism sector, particularly during crises that compress demand and elevate liquidity risk. This study investigates firm-level determinants of stock returns among 27 Indonesian listed tourism firms over 2019–2023, covering the COVID-19 disruption and initial recovery. Operational efficiency is estimated using an input-oriented, constant returns to scale (CRS) Data Envelopment Analysis (DEA) model, and stock returns are modeled with Generalized Estimating Equations (GEE) to account for the longitudinal panel structure. The results indicate that higher DEA-based efficiency and a stronger liquidity position (current ratio) are positively and significantly associated with stock returns, whereas profitability (ROA, ROE) is not significant. Leverage, growth, and firm age also show no significant effects. In contrast, higher valuation multiples (price-to-book and price-to-sales ratios) are associated with lower subsequent returns, and larger firms exhibit lower returns over the sample horizon. The findings support signaling and resource-based interpretations, suggesting that in crisis periods investors reward operational efficiency as an indicator of disciplined resource use that helps preserve cash and sustain liquidity, while discounting firms priced at high multiples. Full article

Background: Inguinal hernia repair (IHR) is frequently performed in infants, often under general anesthesia. Preterm infants are routinely monitored for 24 h postoperatively, due to high reported rates of respiratory complications. However, recent data suggest a decline in these events, prompting a reevaluation of the existing monitoring protocols. This study assesses the detection of (possible) anesthesia-related complications within 24 h after IHR in infants under 3 months of age and aims to identify risk factors for these complications. Methods: This retrospective cohort study included consecutive patients aged ≤ 3 months who underwent IHR between November 2015 and August 2023. All underwent IHR under general anesthesia. Subjects were compared based on whether they experienced possible anesthesia-related complications within 24 h after surgery or not. A logistic regression model was constructed and the number needed to monitor was calculated. Results: 306 patients were included, of which 36.3% were prematurely born (gestational age < 37 weeks) and the mean postconceptional age at surgery was 47.7 ± 4.8 weeks. Possible anesthesia-related complications were detected in 10 patients (3.3%), but only 8 (2.6%) were likely attributable to anesthesia. Events included desaturations, convulsions, fever, and a choking incident. Significant differences were found in patients experiencing complications when they had pre-existing respiratory (p = 0.013) or circulatory (p = 0.016) comorbidities. The postconceptional age (PCA) and gestational age (GA) were not significantly different between groups. Univariate logistic regression did not show a significant correlation between anesthesia-related complications and PCA or GA. Conclusions: Our data corroborates the suggestion that prematurity and PCA alone are not the main characteristics upon which postoperative monitoring protocols should be based. We hypothesize that an individualized approach based on comorbidities and clinical history could be more accurate. These findings point toward the necessity of more (prospective) research to support the refinement of postoperative monitoring guidelines to optimize healthcare resource allocation, while maintaining patient safety. Full article

Owing to their high flexibility, autonomous operation, and rapid deployment capability, unmanned aerial vehicles (UAVs) serve as effective aerial platforms for sensing and communication in remote and time-critical scenarios. However, their limited onboard energy budget poses a significant bottleneck for sustained operations. This paper investigates an energy-efficient UAV-assisted integrated sensing and communication (ISAC) system, aiming to maximize the sensing energy efficiency (SEE), defined as the ratio of the total radar estimation rate to the total energy consumption. Unlike prior works focused solely on rate maximization or fairness, our design jointly optimizes the UAV’s 3D trajectory, task scheduling, and power allocation under kinematic and coverage constraints to maximize the SEE. To solve the formulated non-convex fractional programming problem, we propose an efficient iterative algorithm based on the Dinkelbach method and block coordinate descent (BCD). Simulation results demonstrate that the proposed scheme achieves a superior trade-off between sensing performance and energy consumption. Full article

Intellectual property (IP) is a cornerstone of sustainable industrial growth, yet unpredictable fluctuations in patent application filings pose a challenge to the administrative efficiency and sustainable governance of patent offices. This study aims to enhance strategic R&D governance by analyzing the seasonality of patent application filings using monthly data from the Republic of Korea (January 2001 to July 2024) and proposing a time series forecasting model that reflects this seasonality. To verify seasonal patterns, visual analyses (graphs, time series decomposition, and autocorrelation function plots) and the Kruskal–Wallis test were conducted. The results confirmed a clear 12-month seasonal pattern, characterized by a distinct “December Rush” at the end of each year. Based on these findings, we compared the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models, demonstrating that the SARIMA model offers superior predictive performance by effectively capturing these cyclical fluctuations. Furthermore, by segmenting data into private and public R&D sectors, we observed that private R&D exhibits more pronounced seasonal volatility, necessitating differentiated management strategies. This study highlights the critical role of seasonality in forecasting patent volumes and provides a data-driven framework for sustainable governance, offering actionable insights for optimizing resource allocation and policy support in the innovation ecosystem. Full article

Background/Objectives: Hip fractures in older adults are associated with substantial morbidity, functional decline, and high in-hospital mortality. Early identification of patients at increased risk of death may improve clinical decision-making and resource allocation. This study aimed to develop and internally validate supervised machine learning models to predict in-hospital mortality among older adults hospitalized for hip fracture using nationwide administrative data from Chile. Methods: A retrospective cohort study was conducted using anonymized hospital discharge records from the Chilean National Health Fund (FONASA), covering admissions between 1 January 2019 and 31 December 2024, across 72 public hospitals. Demographic, clinical, and care-related variables were included as predictors. Multiple supervised machine learning algorithms were trained and evaluated using stratified train–test partitioning. Model performance was assessed using AUC-ROC, precision, recall, and F1-score. Model interpretability was explored using SHapley Additive exPlanations (SHAP). Results: A total of 40,253 hospitalization episodes were analyzed. The Gradient Boosting model achieved the best overall performance, with an AUC-ROC of 0.885 and a favorable balance between precision and recall. SHAP analysis identified age, comorbidity burden, and surgical treatment as the most influential predictors, revealing nonlinear and clinically meaningful contributions to mortality risk. Conclusions: Supervised machine learning models based on routinely collected administrative data demonstrated strong predictive performance for in-hospital mortality after hip fracture. Interpretable models may support early risk stratification and clinical decision-making at a national healthcare level. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

The fast-expanding digital economy is reshaping the resource-allocation system and green-governance system, yet its contribution to ecological efficiency within the regional economic system remains insufficiently quantified. Using provincial panel data from China over 2011–2023, we establish a fixed-effects specification to examine how digital economy development affects ecological efficiency and examine potential mechanisms. We find that digital economy development significantly improves ecological efficiency, and this result remains robust across a wide range of alternative specifications and sensitivity tests. The positive effect operates primarily through higher green innovation output and industrial upgrading. The above relationship exhibits a clear threshold with respect to environmental regulation: when regulation is relatively weak, the estimated impact of digital economy on ecological efficiency is statistically indistinguishable from zero, whereas once regulation exceeds the threshold, the positive effect becomes substantially stronger, consistent with complementarity between regulation and digitalization. Moreover, heterogeneity analyses further indicate larger gains in provinces with higher economic development and human capital. Our evidence underscores that aligning digital transformation with appropriately designed regulatory institutions can enhance ecological efficiency and support the innovation and management of a more sustainable and competitive economic system in the digital era. Full article

The Cloud–Edge Continuum (CEC) has emerged as a paradigm for distributing computational resources across cloud, fog, and edge layers, enabling latency-sensitive applications to operate efficiently. However, ensuring the quality of service (QoS) brokerage in such environments remains a challenge. Existing frameworks primarily focus on resource management techniques such as allocation, scheduling, and offloading but fail to address the quality assurance of the brokerage process itself. This paper introduces SLA governance as a means of ensuring the quality of service brokerage by validating—through automated reasoning—Service Level Agreements (SLAs) against meta-quality constraints—high-level policies that define permissible QoS conditions. We propose an ontology-driven approach leveraging the ODRL ontology for SLA representation and capturing meta-quality constraints. Our method also enables introspective reasoning ensuring internal SLA consistency. Additionally, we integrate SLA governance with a real-time monitoring framework, the Event Management System (EMS), to continuously track workload performance and trigger SLA adaptation when necessary. This integration ensures that SLA-based brokerage decisions remain dynamic and context-aware. Full article

City-scale forecasting is essential for both electric-vehicle (EV) charging operations (e.g., peak management and resource allocation) and urban mobility management (e.g., road-network flow monitoring and incident response). Spatio-temporal graph neural networks (STGNNs) are a natural candidate for these problems, yet their performance often critically depends on the choice of a predefined graph prior and the availability/quality of exogenous signals. Importantly, we do not intentionally construct a poor graph; rather, we treat any predefined adjacency as a testable hypothesis and verify its alignment with the forecasting target via no-graph ablations and lightweight diagnostics ($\Delta corr$, $E_D$). In this work, we present a unified experimental pipeline based on a spatio-temporal graph convolutional network (STGCN) backbone and conduct systematic ablations on (i) whether and how a predefined static graph is used and (ii) how feature sets influence multi-step forecasting accuracy. We evaluate on two city-scale hourly datasets with heterogeneous node counts (UrbanEV: 275 nodes; CHARGED-AMS_remove_zero: 1388 nodes) and a 24 h input/6 h output setting. Across datasets, we find that a static graph can be beneficial only when it matches the true dependency structure; otherwise, it may degrade accuracy substantially. On UrbanEV, removing the graph component improves overall MAE from 116.21 ± 5.43 to 66.53 ± 1.71 (S = 5 seeds, 0–4), outperforming a persistence baseline (MAE 94.16). Feature ablations further analyze how occupancy and price signals affect UrbanEV accuracy (e.g., MAE 87.32 with all features under the evaluated feature setting). On CHARGED, the volume-only setting performs best among tested feature combinations (MAE 0.127), closely tracking a persistence baseline (MAE 0.139), while additional covariates may introduce noise under static modeling. We provide detailed multi-horizon results and discuss practical implications for when graph priors help or hurt in real deployments. Full article

Background: Kidney biopsy is the diagnostic gold standard for characterizing glomerular disease and other intrarenal pathologies. Despite its clinical importance, epidemiological trends in kidney biopsy incidence remain poorly understood in many developed healthcare systems. This study characterizes temporal and demographic trends in kidney biopsy utilization in Germany between 2006 and 2023, providing crucial data for resource allocation in renal pathology services. Methods: Data on all kidney biopsies (OPS code 1-465.0) performed in German hospitals were extracted from the Federal Statistical Office database and stratified by age and sex. Population denominators were obtained from national census data. Incidence rates per 100,000 inhabitants per year were calculated, and temporal trends were analyzed using Poisson regression with year as a continuous predictor variable. Separate models were fitted for overall population incidence, age-stratified incidence, and sex-stratified incidence. Results: The incidence of kidney biopsies increased 96.6% over 18 years, from 8.59 per 100,000 inhabitants in 2006 to 16.89 per 100,000 in 2023 (IRR: 1.0296 per year, 95% CI: 1.0287–1.0305; p < 0.0001). Age-stratified analysis revealed pronounced heterogeneity, with the oldest patients (>80 years) experiencing the steepest increase of 7.74% annually, while the youngest age group (<20 years) showed no significant temporal change. Sex-stratified analysis demonstrated similar increases in both males and females (3.36% and 3.04% annually, respectively). Conclusion: The substantial increase in kidney biopsy utilization in Germany over nearly two decades mirrors international patterns and suggests a global shift toward more liberal biopsy utilization in aging populations. Multiple factors likely contributed to this increase, including demographic aging, improved procedural safety and accessibility, evolving diagnostic guidelines, and expanding therapeutic options for glomerular disease. These findings underscore the need for national registry systems to optimize resource allocation for renal pathology and ensure equitable diagnostic access across healthcare systems. Full article

The efficient operation of the maintenance service system is key to achieving sustainable operations, with its core lying in the coordinated scheduling of spare parts production and maintenance personnel, as well as the holistic management of in-warranty and out-of-warranty demands. This approach optimizes resource allocation and enhances long-term service value. This paper investigates the integrated scheduling of distributed spare parts production and maintenance personnel with differentiated in-warranty and out-of-warranty demands (ISSPD). To solve the ISSPD, an improved non-dominated sorting genetic algorithm-II that uses Q-learning to adaptively select local search strategies (QLNSGA) is proposed, which incorporates a decoding strategy for differentiated order types, eight knowledge-driven local search strategies, and a Q-learning mechanism for the adaptive selection of key local search operators. Compared to random local search operators, the Q-learning mechanism achieves a 55% decrease in IGD metric and a 65% increase in HV metric. Through comparative experiments with four mainstream algorithms, QLNSGA outperforms RIPG by 58% in terms of the IGD index, and its HV index is generally superior to that of comparative algorithms such as MOEA/D. This indicates that QLNSGA exhibits superior performance in both computational efficiency and solution quality, effectively enhancing service levels and significantly reducing operational costs, thereby providing scientific decision support for service-oriented manufacturing enterprises. Full article