Advancing Open Science

Acinetobacter baumanni (A. baumannii) is a well-known pathogen associated with antimicrobial-resistant infections. It is a major cause of nosocomial infections and is frequently associated with polymicrobial and antibiotic-resistant infections. This study investigates the frequency of A. baumannii infections, its antimicrobial resistance profile and the main co-pathogens isolated in respiratory samples at the San Giovanni di Dio e Ruggi d’Aragona Hospital in 2015–2019 (pre-COVID-19 pandemic) and 2020–2023 (during/post-COVID-19 pandemic). Bacterial identification and antibiotic susceptibility testing were performed using the VITEK^® 2 system (2015–2019), while identification was carried out with MALDI-TOF MS starting from 2020. A total of 1679 strains were isolated between 2015 and 2019, and 1186 between 2020 and 2023, with significantly higher frequencies in males 61–80 and females 71–80. A. baumannii was isolated predominantly from respiratory specimens, derived predominantly in intensive care units (ICUs). The antimicrobial resistance rates of A. baumannii were above 90% for gentamicin, trimethoprim/sulfamethoxazole, imipenem and ciprofloxacin, while colistin resistance was less than 1% (0.95%) in pre-pandemic and alarmingly increased during/post pandemic period (6.1%). A. baumannii was most frequently associated with Klebsiella pneumoniae, Staphylococcus aureus and Pseudomonas aeruginosa in respiratory tract infections. A. baumannii represents a serious global health threat due to its extensive antimicrobial resistance, highlighting the need for continuous surveillance, detailed strain characterization, and development of new antimicrobial agents.

Accurate subsurface velocity modeling is of fundamental scientific and practical significance for seismic data processing and interpretation. However, conventional depth-domain methods still face limitations in physical consistency and inversion accuracy. To overcome these challenges, this study proposes a deep learning-based seismic velocity modeling approach in the time domain. The method establishes an end-to-end mapping between seismic records and velocity models directly in the time domain, reducing the nonlinear complexity of mapping time-domain data to depth-domain models and improving prediction stability and accuracy. Synthetic aquifer velocity models were constructed from representative stratigraphic features, and multi-shot seismic records were generated through forward modeling. A U-Net network was employed, taking multi-shot seismic records as input and time-domain velocity fields as output, with training guided by a mean squared error (MSE) loss function. Experimental results show that the proposed strategy outperforms conventional depth-domain approaches in aquifer structure identification, velocity recovery, and interlayer contrast depiction. Quantitatively, significant improvements in MSE, peak signal-to-noise ratio, and structural similarity index indicate higher reconstruction reliability. Overall, the results confirm the effectiveness and potential of the proposed time-domain framework for aquifer velocity inversion and its promise for intelligent seismic velocity modeling.

Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements are often found. Limited soil moisture measurements can be employed to develop a numerical model for long-term and accurate soil moisture estimations. A key input variable to the model is precipitation, which is also not easily accessible, particularly at a finer spatial resolution; hence, publicly available remote sensing data can be used as an alternative. This study, therefore, aims to develop a numerical model HYDRUS-1D to estimate soil moisture in the data-scarce state of the Northern Territory, Australia, with a land cover of shrubland and a Tropical-Savannah type climate. The HDYRUS-1D is based on the numerical solution of Richards’ equation of variably saturated flow that relies on information about the soil water retention characteristics. This study utilized the van Genuchten model parameters, which were optimized (against measured soil moisture) through parameter optimization with initial estimates obtained from the HYDRUS catalogue. Initial estimates from different sources can differ for the same soil texture (e.g., loamy sand) and can induce uncertainties in the calibrated model. Therefore, a comprehensive uncertainty analysis was conducted to address potential uncertainties in the calibration process. The HYDRUS-1D was calibrated for a period between March 2012 and February 2013 and was independently validated against three different periods between March 2013 and October 2016. Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), and Mean Absolute Error (MAE) were used to assess the efficiency of the model in simulating the measured soil moisture. The model exhibited good performance in replicating measured soil moisture during calibration (RMSE = 0.00 m³/m³, MAE = 0.005 m³/m³, and R = 0.70), during validation period 1 (RMSE = 0.035 m³/m³ and MAE = 0.023 m³/m³, and R = 0.72), validation period 2 (RMSE = 0.054 m³/m³ and MAE = 0.039 m³/m³, and R = 0.51), and validation period 3 (RMSE = 0.046 m³/m³ and MAE = 0.032 m³/m³, and R = 0.61), respectively. Remotely sensed precipitation data were used from the CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now to assess their capabilities in estimating soil moisture. Efficiency evaluation metrics and visual assessment revealed that these products underestimated the soil moisture. The CHRS-CCS outperformed other products in terms of overall efficiency (average RMSE of 0.040 m³/m³, average MAE of 0.023 m³/m³, and an average R of 0.68, respectively). An integrated approach based on numerical modelling and remote sensing employed in this study can help understand the long-term dynamics of soil moisture and soil water balance in the Northern Territory, Australia.

The satellite Internet of Things (SatIoT) enables real-time acquisition and large-scale coverage of hyperspectral imagery, providing essential data support for decision-making in domains such as geological exploration, environmental monitoring, and urban management. Hyperspectral remote sensing classification constitutes a critical component of intelligent applications driven by the SatIoT, yet it faces two major challenges: the massive data volume imposes heavy storage and processing burdens on conventional satellite systems, while dimensionality reduction often compromises classification accuracy; furthermore, mainstream neural network models are constrained by insufficient labeled data and spectral shifts, frequently leading to misclassification of unknown categories and degradation of cross-regional performance. To address these issues, this study proposes an open-set hyperspectral classification method with dual branches of reconstruction and prototype-based classification. Specifically, we build upon an autoencoder. We design a spectral–spatial attention module and an information residual connection module. These modules accurately capture spectral–spatial features. This improves the reconstruction accuracy of known classes. It also adapts to the high-dimensional characteristics of satellite data. Prototype representations of unknown classes are constructed by incorporating classification confidence, enabling effective separation in the feature space and targeted recognition of unknown categories in complex scenarios. By jointly leveraging prototype distance and reconstruction error, the proposed method achieves synergistic improvement in both accurate classification of known classes and reliable detection of unknown ones. Comparative experiments and visualization analyses on three publicly available datasets: Salinas-A, PaviaU, and Dioni-demonstrate that the proposed approach significantly outperforms baseline methods such as MDL4OW and IADMRN in terms of unknown detection rate (UDR), open-set overall accuracy (OpenOA), and open-set F1 score, while on the Salinas-A dataset, the performance gap between closed-set and open-set classification is as small as 1.82%, highlighting superior robustness.

Background/Aim: Social networks have transformed the traditional dynamics of identity construction in adolescence, allowing users to select content and interact with others who share similar views, thereby reinforcing a sense of belonging to homogeneous groups. Given the growing influence of digital interaction on social identity among youth, psychometrically sound instruments are needed to measure this process. This study aimed to evaluate the psychometric properties of both the 9-item (IBRS-9) and 6-item (IBRS-6) versions of the Identity Bubble Reinforcement Scale in a large sample of Chilean adolescent students. Methods: A cross-sectional design was used with 4096 participants (50.8% male, 47.8% female, 1.4% other; M = 15.82, SD = 1.30) from 41 secondary schools across Chile. Confirmatory factor analyses (CFAs) tested factorial validity, and internal consistency and external criterion validity were examined. Measurement invariance was assessed across sex, social media use, internet use, and age. Analyses were conducted using the WLSMV (Weighted Least Squares Mean and Variance Adjusted), and model evaluation was based on conventional goodness-of-fit indices. Results: CFAs supported the factorial validity of both IBRS versions, showing reliability and external criterion validity. Model fit indices indicated good fit for both scales. Invariance analyses confirmed factorial stability up to the strict level across all subgroups, indicating consistent psychometric performance. Conclusions: The IBRS-9 and IBRS-6 are valid and reliable instruments for assessing identity bubble reinforcement among Chilean adolescents, providing evidence of factorial stability and applicability for research and educational and psychosocial interventions. Their validated structure provides a consistent basis for examining social identity processes related to digital interaction.

With the penetration of renewable power generation (RPG) in the distribution network (DN), power quality issues caused by RPG fluctuations have become more prominent than ever, let alone the integration of new types of power loads like electrified trains and electric vehicles that are major harmonic sources. Traditional power quality enhancement approaches are mostly dedicated to the smoothing of RPG power output or active compensation of harmonics, but fail to incorporate both routines into one single power quality enhancement scheme. Out of this research motivation, this paper aims to propose a synergetic allocation scheme for the hybrid energy storage system (HESS) and the unified power quality conditioner (UPQC) to achieve superior power quality enhancement. Firstly, a novel comprehensive vulnerability index of the DN suited for the power quality issues is presented to reflect the key factors that may impact the bus voltage security. Afterwards, the capacity specifications of HESS and UPQC for power smoothing and load side harmonic compensation are deduced with variational mode decomposition and inverter capacity configurations. Subsequently, the synergetic allocation method of HESS and UPQC are proposed by formulating an optimization problem, with the former obtained capacity specifications acting as the main constraints. After that, a dynamic hourly network reconfiguration approach is proposed to enhance the vulnerability level of the DN by dynamically changing its topology, and ensuring better power quality with the optimally allocated HESS and UPQC. Finally, simulation tests and comparative studies are conducted to evaluate the effectiveness and performance of the proposed scheme by comparing with existing methods. The comparative study has shown that the proposed method can reduce bus voltage deviation by 2.63%; meanwhile, it can reduce the total harmonic distortion by 1.83%.

Background/Objectives: Parotid incidentalomas are increasingly detected during brain MRI and PET-CT, particularly in patients with serious diseases such as cancer. This study aimed to evaluate the clinical significance of incidentally identified parotid lesions. Methods: We retrospectively reviewed the records of 44,952 patients (≥19 years) who underwent brain MRI and 10,957 who underwent PET-CT between January 2014 and December 2023. The incidence, imaging findings, and pathological results of parotid incidentalomas were analyzed. Results: Among 44,952 brain MRIs, 100 incidental parotid lesions (0.22%) were detected, compared with 92 lesions (0.84%) among 10,957 PET-CT scans. The mean patient age was slightly higher in the PET-CT group. Of the MRI-detected lesions, 35 patients underwent further evaluation and 14 underwent surgery, with final pathology confirming only benign tumors, including pleomorphic adenomas, Warthin tumors, and basal cell adenomas. In contrast, among 23 PET-CT patients who underwent additional evaluation, 7 had surgery, and final pathology revealed both benign and malignant tumors. Malignant cases included mucoepidermoid carcinoma, metastatic Merkel cell carcinoma, metastatic sebaceous carcinoma, and adenoid cystic carcinoma. Notably, two patients with initially benign cytology and negative PET-CT findings were later confirmed to have malignancies after surgery, Primary sites of metastatic disease included the thyroid, cervix, head and neck, and skin. Conclusions: Most parotid incidentalomas detected on brain MRI are benign and may be managed conservatively. However, incidentalomas identified on PET-CT require thorough evaluation, as they may indicate metastatic disease or a second primary malignancy, particularly in patients with head and neck or skin cancers.

This study examined the effects of phosphoric acid (H₃PO₄), polyimide (PI), and lubricants (MoS₂, graphite) on the phase stability, microstructure, and magnetic performance of Fe-5.0 wt.%Si soft magnetic composites (SMCs). Warm compaction (≤550 °C) and annealing at 700 °C were applied to samples prepared under a full factorial design. X-ray diffraction confirmed stable α-Fe(Si) phases without secondary phases. SEM and TEM–EDS revealed interfacial insulating layers mainly composed of Si-O, with localized phosphorus and carbon. Additive composition strongly influenced magnetic and physical properties. Increasing H₃PO₄ and PI reduced the density from 7.50 to 7.27 g/cm³ and lowered the permeability (from 189 at 1 kHz to 156), due to thicker interparticle layers that restricted metallic contact and domain wall motion. In contrast, Q-values rose significantly with frequency: for H₃PO₄ 0.25 wt.% + PI 0.25 wt.% + graphite 0.3 wt.%, Q increased from 0.39 (1 kHz) to 2.91 (10 kHz), reflecting effective eddy current suppression. Lubricant type further influenced performance: graphite consistently outperformed MoS₂, with 0.3 wt.% graphite providing the best balance of high density, permeability, and a frequency-stable Q-value. Overall, Fe-5.0 wt.%Si performance is governed not by bulk phase changes but by the trade-off between densification and insulation at particle interfaces. The optimal combination of low H₃PO₄ and PI with 0.3 wt.% graphite offers practical guidelines for designing high-frequency, high-efficiency motor materials.