Search Results (7,704)

Neonatal infections remain a leading cause of morbidity and mortality worldwide, particularly among preterm and low-birth-weight infants and in low- and middle-income countries. This burden has intensified with the global increase in multidrug-resistant (MDR) bacteria, especially in neonatal intensive care units, where prolonged hospitalization, invasive interventions, and exposure to broad-spectrum antibiotics promote colonization, transmission, and invasive infection. In this narrative review, we explore the epidemiology and microbiological characteristics of MDR bacterial infections in newborns, alongside their associated risk factors, diagnostic challenges, treatment outcomes, and prevention strategies. Across different settings, Gram-negative pathogens, particularly Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii, account for a substantial proportion of severe neonatal infections, whereas methicillin-resistant Staphylococcus aureus remains important in selected units. The risk of MDR infection is driven by a complex interplay of factors, ranging from maternal and perinatal exposures to the inherent immunological vulnerability of newborns, hospital-based transmission, antibiotic selection pressure, and structural deficiencies in healthcare infrastructure. Diagnosis remains challenging because clinical presentations are nonspecific and culture-based methods are constrained by low blood volumes, prior antimicrobial exposure, and delayed turnaround times. Treatment is increasingly complicated due to resistance to standard empirical regimens, substantial regional variation in susceptibility profiles, and limited neonatal pharmacokinetic and safety data for reserve agents. Current evidence mainly supports surveillance-informed empirical therapy, susceptibility-guided treatment adjustment, antimicrobial stewardship, and strict infection prevention measures. Future progress will require neonatal-specific clinical trials, harmonized surveillance systems, stronger molecular epidemiology, and more equitable access to microbiological diagnostics and effective treatment. Full article

This paper addresses the problem of nonparametric regression for functional time series, a setting complicated by the infinite-dimensional nature of the covariates, temporal dependence, and potential for outliers. We propose a new robust estimator that combines three powerful ideas: (i) k-nearest neighbors (kNN) for adaptive localization in the functional space; (ii) local linear smoothing to reduce bias; and (iii) $\mathcal{M}$-estimation to ensure resilience against atypical observations. The key theoretical contribution establishes the almost-complete convergence of the proposed estimator under mild conditions that account for the functional geometry, weak dependence (via quasi-association), and robustness constraints. The obtained rate of convergence explicitly reveals the interplay between the functional concentration, dependence strength, and local smoothness of the model. A simulation study demonstrates that this method offers superior stability and predictive accuracy compared to classical alternatives, particularly under heavy-tailed errors and data contamination. The practical relevance of the approach is further illustrated through a one-step-ahead prediction application to a real-world environmental dataset of hourly NOx measurements. Full article

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

To investigate the influence of ambient temperature variations on the natural vibration characteristics and seismic responses of suspen-dome structures, a 1:20 geometric similarity dynamic scale model was designed using the symmetric suspen-dome roof of the Lanzhou Olympic Sports Center Gymnasium as the prototype. First, white noise excitation tests and seismic simulation tests were performed on the model, and the indoor ambient temperature was measured simultaneously. Subsequently, a corresponding numerical scaled model was developed using the ABAQUS 2024 finite element software, and its temperature was set according to the shaking table test measurements. Modal analysis and seismic time–history analysis were then performed, and the model’s natural frequencies and seismic responses (such as acceleration, displacement, and internal force) were compared with the shaking table test results, thereby validating the accuracy of the numerical model and confirming that the modeling approach reliably reproduces the natural frequencies and seismic responses measured in the tests. Finally, the ambient temperature of the numerical model was set according to the historical temperature data for Lanzhou. A comparative analysis was performed to examine the variations in the natural vibration characteristics and seismic responses of the suspen-dome structure under different temperature conditions. The result shows that, as the ambient temperature increases from −30 °C to 60 °C, the natural frequencies of the suspen-dome structure decrease by up to 21.8% (e.g., the third-order frequency drops from 9.423 Hz to 7.734 Hz), with low-order natural frequencies being the most significantly affected. Furthermore, under both unidirectional and three-dimensional earthquake excitations, the peak seismic responses increase markedly: acceleration increases by up to 35.5%, displacement increases by up to 88.3%, and internal force in critical members increases by up to 68.9%. Notably, structural members experiencing higher internal force responses demonstrate greater sensitivity to ambient temperature changes. These findings indicate that ambient temperature variation significantly reduces structural stiffness and amplifies seismic responses, providing a valuable reference for the seismic performance evaluation and safety design of suspen-dome structures in regions with large annual temperature fluctuations. Full article

The development of high-performance copper alloys requires balancing mechanical strength and electrical conductivity, properties that are often inversely correlated due to competing strengthening mechanisms. This study presents a comparative machine learning analysis of Cu-Ni-Si and Cu low alloys using a curated dataset of 1690 entries derived from the Gorsse et al. database, comprising 1507 samples with hardness measurements and 1685 samples with electrical conductivity data. Three ensemble-based regression algorithms, Random Forest, XGBoost, and Gradient Boosting, were trained to predict Vickers hardness (HV) and electrical conductivity (%IACS) from an augmented feature set encompassing alloy composition, thermomechanical processing parameters, missingness indicators, and physics-informed descriptors (valence electron concentration, atomic size mismatch, electronegativity difference, and Ni:Si atomic ratio). XGBoost achieved optimal performance for hardness prediction (R² = 0.8554, RMSE = 29.90 HV), while Gradient Boosting performed best for electrical conductivity (R² = 0.8400, RMSE = 5.96%IACS). Averaged tree-based feature-importance analysis identified valence electron concentration as the most influential predictor for hardness (39.9%), followed by aging temperature (11.2%), while Cu content dominated conductivity prediction (37.7%), followed by aging time (8.9%). Complementary SHAP analysis confirmed these trends while revealing directional relationships and nonlinear feature interaction effects. Composition-grouped cross-validation by unique alloy formula (K = 10) yielded substantially lower performance, with grouped CV R² = 0.438 for hardness and 0.293 for conductivity, indicating that generalization to unseen alloy formulations remains limited. The models were further applied for practical tasks, including property prediction for new alloy compositions, processing parameter optimization via differential evolution with metallurgical constraints (achieving hardness up to 293.9 HV or conductivity up to 45.7%IACS for the same base composition, with prediction intervals reported), and inverse design to identify alloy formulations meeting specified target properties. This work demonstrates the potential of interpretable machine learning to support copper alloy development by enabling rapid computational screening of the compositional and processing parameter space, subject to the generalization limitations identified herein. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

To address the difficulty of detecting demagnetization severity in permanent magnet synchronous motors (PMSMs), this paper proposes a demagnetization severity detection method based on temperature signal and Convolutional Neural Network (CNN). First, the differences between local demagnetization and eccentricity fault in stator current harmonics are analyzed from an electromagnetic perspective, and fast Fourier transform (FFT) is used for frequency-domain analysis of the stator current to identify local demagnetization faults. On this basis, an electromagnetic–thermal coupling model is established by considering motor losses and heat dissipation boundary conditions to obtain the winding temperatures under different demagnetization severities and operating conditions. Furthermore, the temperature time series, together with speed and load torque, is constructed into a three-dimensional state space, and the proposed Conditionally Modulated Multi-Scale Convolutional Neural Network (CMSCNN) is introduced for feature learning to achieve demagnetization severity detection. Experimental results show that the proposed method achieves an average detection accuracy of 98.06% on the simulation test set and outperforms the baseline CNN model. On measured data collected from the faulty prototype, the average detection accuracy reaches 93.34%, verifying the effectiveness of the proposed method for demagnetization severity detection. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

Background: Measles remains a significant occupational hazard in healthcare settings. In the context of the 2017–2018 measles outbreak in Greece and amid an outbreak at the study hospital, this seroprevalence study aimed to identify gaps in measles serologic status among healthcare workers in a tertiary hospital in central Greece. Methods: We conducted a seroprevalence study among hospital employees between February and December 2017. Blood samples and data on sociodemographic and work-related characteristics were collected from a convenience sample of participants. Measles IgG and IgM antibodies were measured using the ELISA method to determine seropositivity. The 95% CIs for measles IgG seronegativity proportions were calculated using the Clopper–Pearson exact method. Associations between participant characteristics and measles antibody status were assessed using Firth’s penalized logistic regression models. Results: A total of 336 healthcare workers participated in the study (response rate: 24.9%). Overall, 5.4% (95% CI: 3.2–8.3) tested negative for measles IgG antibodies. No significant associations were observed between participants’ characteristics and measles IgG antibody status. Male participants had 15.8 times higher adjusted odds of testing positive for measles IgM antibodies compared with female participants (aOR: 15.8; 95% CI: 2.33–107.54; p = 0.005). Conclusions: Our results indicate a low—but not negligible—proportion of IgG measles seronegativity among participants. The detection of seronegative individuals born prior to 1970 challenges the assumption of universal natural immunity based solely on year of birth. Given the recent rise in measles outbreaks and the limited seroprevalence data among healthcare workers in Greece, these findings provide valuable data to support ongoing efforts to achieve full vaccination coverage in this group. Further research is warranted to investigate the observed sex differences in susceptibility to measles infection. Full article

The aim of this in vitro study was to evaluate the ability of the new strontium-containing composite, Stela (SDI, Victoria, Australia), to induce hydroxyapatite formation and promote remineralization of demineralized dentin, compared to SDR Flow+ (York, PA, USA). Twenty-four dentin slices (1 mm thick) were obtained from extracted wisdom teeth using a microtome and demineralized with 17% EDTA for 2 h. A layer of either Stela or SDR Flow+ was applied to each slice, allowed to set, and preserved in 0.1% thymol solution. Samples were analyzed at 1, 7, 14 and 28 days (n = 3 per group and time). Measurements were taken at baseline, after demineralization, and after application. Apatite formation was assessed using 'Fourier-transform infrared spectroscopy (FTIR), while changes in the Calcium/Phosphate (Ca/P) ratio were evaluated by Energy Dispersive Spectroscopy (EDX). Statistical comparisons were performed using the Wilcoxon test (p < 0.05). Both materials promoted carbonated hydroxyapatite formation and increases in calcium and phosphate. Stela exhibited an apatite peak (1420 cm⁻¹) as early as 24 h and significant increases in calcium and phosphate from day 7. SDR Flow+ reached its peak at 14 days and showed significant increases in the Ca/P ratio. By 28 days, both materials achieved comparable remineralization, confirming their effectiveness in treating demineralized dentine. Full article

The high complexity of radiation therapy for lung cancer necessitates effective planning of advanced treatments such as Volumetric Modulated Arc Therapy (VMAT) by radiation oncologists. The current VMAT treatment planning process typically involves extensive manual interaction and a time-consuming, trial-and-error, iterative approach that requires planners’ experience. This can lead to varying levels of plan quality. To improve the quality of radiotherapy treatment plans quickly and accurately, this research presents a new architecture, Enhanced UNet3D, to generate three-dimensional (3-D) dose distributions for lung cancer patients. Enhanced UNet3D utilises a symmetric encoder–decoder architecture with residual connections and a target region-attention module to achieve high accuracy in dose shaping within the PTV. A new composite objective function, Enhanced Combined Loss (ECLoss), that includes both SharpLoss, a structure-aware DVH-guided loss, and 3D gradient regularisation, has been developed to address voxel-level class imbalance and achieve realistic spatial dose falloff. This research utilised a retrospective dataset of 170 VMAT plans to train and validate the proposed model. On the test set (n = 14), the model demonstrated exceptional overall accuracy, with a Mean Absolute Error (MAE) of 0.238 ± 0.075 Gy and a structural similarity index measure (SSIM) of 0.970 ± 0.005. Moreover, the model can perform near-real-time inference at approximately 0.5 s per patient, representing a significant reduction in computational resources compared to other architectures. Therefore, these results demonstrate that the Enhanced UNet3D model with ECLoss is a clinically feasible tool for the rapid evaluation and quality assurance of radiotherapy treatment plans and may reduce the need for manual trial-and-error in VMAT workflows. Full article

We develop a structural framework for the three-dimensional incompressible Navier–Stokes equations in which the nonlinear dynamics are reorganized in terms of triadic interactions, dyadic shells, and helical modes. Within this formulation, all interactions are classified into Low–Low, Low–High, and High–High channels, and it is shown that the Low–Low and Low–High contributions are perturbatively controlled through scale-localized estimates without introducing external assumptions. Consequently, potentially non-perturbative contributions are confined, within the present framework, to a class of same-scale High–High interactions. This class is further reduced, through geometric and dynamical constraints, to a coherent core characterized by amplitude activity and low phase drift. The resulting reduced dynamics is expressed in terms of family-level phase variables and associated curvature quantities. The main result establishes a quantitative residence-time compression principle for this coherent regime. Specifically, it is shown that intervals on which both amplitude activity and low phase drift persist must have small total measures, due to an absolute-value coercivity property of the curvature combined with bounded-variation control of the phase dynamics. This implies that coherent same-scale interactions cannot occupy a macroscopic portion of any bounded time interval, even though local re-entry into low-drift configurations is not excluded. Consequently, the nonlinear transfer associated with coherent triads becomes temporally localized and admits a shellwise compressed representation. These results provide a structurally reduced description of a candidate mechanism for cumulative same-scale amplification within the present dyadic–triadic framework. They do not claim a framework-level structural exclusion of the global regularity problem. Rather, they identify and analyze, within an explicit structural setting, a minimal mechanism for non-perturbative amplification, and establish a quantitative constraint on its temporal persistence. Full article

The Camino de Santiago, a UNESCO-listed pilgrimage route, has experienced sustained growth in visitor numbers, challenging municipalities to preserve cultural integrity while ensuring service quality. This study reviews people-counting technologies and proposes a smart pilgrim management framework grounded in flux measurement systems to support data-driven and sustainable decision-making. Drawing on the smart tourism literature, the conceptual framework integrates infrared counters, mobile tracking solutions, and GPS/Wi-Fi data to generate real-time insights into pilgrim flows. A pilot simulation illustrates how these data can inform operational and strategic planning. The framework enables local authorities to monitor pedestrian movements, anticipate service demands (sanitation, accommodation, and safety), and detect overcrowding in sensitive heritage areas. By incorporating technological solutions into traditionally low-tech pilgrimage settings, municipalities can transition from reactive to proactive management approaches. The paper contributes a scalable and ethically grounded framework tailored to heritage pilgrimage routes, advancing smart tourism applications in culturally significant contexts. Full article

In the construction industry, it is necessary to know the soil type and its water content to ensure compliance with required specifications. Existing solutions involve expensive equipment and require significant time to deliver reliable results. This article focuses on the application of modular neural networks to automate the analysis of measurement data for five soil types. The data analyzed were obtained using resistive and capacitive sensors, as well as the bulk volume mass of materials. A modular architecture consisting of 14 neural networks was designed. One sequential network specialized in material classification with an Adam optimizer. The other 13 neural networks were trained using evolutionary strategies by material type and water content range. The results show that modular architecture improves response time and reliability for individual network models, achieving an accuracy of 94.69%. The modular system was validated using 20% of the database and 10-fold cross-validation. For water content determination, the accuracy in the material with the highest variability was −0.1770% with a standard deviation of 0.6239%. The use of this modular system reduces operating and analysis times in material classification and water content determination through its real-time application. It validates its use in soil analysis processes for construction and can be used in educational settings. Full article

Background: Gastric cancer survivors frequently encounter a “care gap” after discharge because of complex postgastrectomy syndromes. We evaluated “WECARE,” a bidirectional digital health platform designed to provide real-time symptom monitoring and multidisciplinary support. The primary goal of this study was to assess the efficacy of the platform by measuring the change in the Korean Quality of Life Questionnaire for Gastric Cancer Survivors (KOQUSS-40) total score over a six-month recovery period. Methods: This nationwide, multicenter pilot randomized controlled trial was conducted by the Korean Quality of Life in Stomach Cancer Patients Study Group (KOQUSS) across nine tertiary centers in Korea. A total of 88 patients who underwent curative gastrectomy were enrolled. Following an initial optimization phase involving 22 patients, the remaining 66 patients were randomized at a 1:1 ratio to the WECARE group or the control group. The WECARE group used a platform integrating the KOQUSS-40 algorithm for structured symptom reporting, real-time feedback on nutrition and exercise, and educational content on meal planning, symptom coping, and recovery. Assessments were performed at baseline and at 1, 3, and 6 months after discharge. Results: The WECARE group showed high feasibility and acceptability, with an adherence rate of 86.7% and an 82% satisfaction rate. At 6 months, the KOQUSS-40 total score (primary endpoint) did not differ significantly between the WECARE and control groups (85.3 ± 1.6 vs. 83.8 ± 1.6, p = 0.603). However, the WECARE group showed a numerically favorable recovery trajectory from the acute postoperative phase. Subgroup analysis revealed a positive trend in reflux symptom management in the WECARE group (p = 0.0856). In addition, more than 77% of users reported that the platform improved their self-management capabilities. Conclusions: The WECARE platform is a feasible and acceptable digital intervention for gastric cancer survivors. Although the primary endpoint was not significantly different, the favorable recovery trajectory, high adherence, and patient engagement support further evaluation in larger studies with longer follow-up and broader healthcare settings. Full article