Search Results (301)

Background: The aims of this paper are to examine the impact of the COVID-19 pandemic on the non-rational use of antibiotics and potential alterations in the antibiotic resistance profiles of multi-drug resistant (MDR) isolates of Klebsiella pneumoniae (KPN), Pseudomonas aeruginosa (PAE), and Acinetobacter baumannii (ABA). Material and Methods: This study was conducted at the tertiary University Hospital “Dr Dragisa Misovic-Dedinje” (Belgrade, Serbia) and was divided into three periods: pre-pandemic (1.4.2019–31.3.2020, period I), COVID-19 pandemic (1.4.2020–31.3.2021, period II), and COVID-19 pandemic-second phase (1.4.2021–31.3.2022, period III). Cultures were taken from each patient with clinically suspected infection (symptoms, biochemical markers of infection). All departments of the hospital were included in this study. Based on the source, all microbiological specimens were divided into 1° blood, 2° respiratory tract (tracheal aspirate, bronchoalveolar lavage fluid, throat, sputum), 3° central-line catheter, 4° urine, 5° urinary catheter, 6° skin and soft tissue, and 6° other (peritoneal fluid, drainage sample, bioptate, bile, incisions, fistulas, and abscesses). After the isolation of bacterial strains from the samples, an antibiotic sensitivity test was performed for each individual isolate with the automated Vitek^® 2 COMPACT. Antibiotic consumption testing was performed by the WHO guideline equations (ATC/DDD). Results: A total of 2196 strains of KPN, PAE, and ABA from 41,144 hospitalized patients were isolated (23.6% of the number of total isolates). The number of ABA isolates statistically increased (p = 0.021), while the number of PAE isolates statistically decreased (p = 0.003) during the pandemic. An increase in the percentage of MDR strains was observed for KPN (p = 0.028) and PAE (p = 0.027). There has been an increase in the antibiotic resistance of KPN for piperacillin–tazobactam, the third and fourth generations of cephalosporins (ceftriaxone, ceftazidime, and cefepime), all carbapanems (imipenem, meropenem, and ertapenem), and levofloxacin; of PAE for imipenem; and of ABA for amikacin. Total antibiotic consumption increased (from 755 DBD to 1300 DBD, +72%), especially in the watch and reserve group of antibiotics. The highest increases were noted for vancomycin, levofloxacin, azithromycin, and meropenem. MV positively correlated with the increased occurrence of MDR KPN (r = 0.35, p = 0.009) and MDR PAE (r = 0.43, p = 0.009) but not for MDR ABA (r = 0.09, p = 0.614). There has been a statistically significant increase in the Candida sp. isolates, but the prevalence of Clostridium difficile infection remained unchanged. Conclusions: The COVID-19 pandemic has influenced the increase in total and MDR strains of KPN, ABA, and PAE and worsened their antibiotic resistance profiles. An increase in the consumption of both total and specific antibiotics was observed, mostly of fluoroquinolones and carbapenems. A positive correlation between the number of patients on MV and an increase in MDR KPN and MDR PAE strains was noted. It is necessary to adopt and demand the implementation of appropriate antimicrobial stewardship interventions to decrease the resistance of intrahospital pathogens to antibiotics. Full article

Glial fibrillary acidic protein (GFAP) has been shown to be a reliable biomarker for detecting neurological disorders. Recently, we developed the Lumipulse G GFAP plasma assay, which is a commercially available tool. Compared to existing assays, the LUMIPLSE G platform offers the high-throughput, rapid, and fully automated quantification of biomarkers, enabling more standardized and accessible clinical study. In this study, we evaluated this assay using cerebrospinal fluid (CSF) samples. Assessing GFAP in CSF may provide more direct insights into central nervous system pathology than plasma and could improve the characterization of Alzheimer’s disease (AD) stages and support treatment monitoring. The LUMIPULSE G system is a chemiluminescent enzyme immunoassay (CLEIA) platform equipped with full automation, utilizing specialized cartridges to process samples within 30 min. The assay, which employs a pair of proprietary monoclonal antibodies targeting GFAP, was evaluated for clinical performance using 30 CSF samples from patients diagnosed with AD, patients with mild cognitive impairment (MCI), and cognitively unimpaired (CU) individuals, with 10 samples from each group. In addition, levels of β-amyloid 1–40 (Aβ40), β-amyloid 1–42 (Aβ42), and pTau181 were simultaneously measured. The Lumipulse G GFAP assay significantly differentiated (p < 0.05) between the amyloid accumulation and non-amyloid accumulation groups, as classified based on the CSF Aβ test. Furthermore, GFAP showed a moderate correlation with pTau181 (r = 0.588), as determined based on Spearman’s rank correlation coefficient. Moreover, receiver operating characteristic (ROC) analysis was performed to determine the performance of GFAP in distinguishing amyloid-positive and amyloid-negative subjects, with an area under the curve (AUC) of 0.72 (0.50–0.93). When stratified by CSF pTau181 positivity, GFAP demonstrated an improved diagnostic accuracy, achieving an AUC of 0.86 (95% CI: 0.68–1.00). This study demonstrates that the Lumipulse G GFAP assay, when applied to CSF samples, has the potential to differentiate AD from non-AD cases, particularly suggesting its utility in detecting tau-related pathology. While GFAP has previously been established as a biomarker for AD, our findings highlight that combining GFAP with other biomarkers such as Aβ40, Aβ42, and pTau181 may enhance the understanding of AD pathogenesis, disease staging, and possibly treatment responses. These findings suggest that GFAP may serve as a complementary biomarker reflecting astroglial reactivity associated with tau positivity, alongside established biomarkers such as Aβ40, Aβ42, and pTau181. However, since GFAP levels may also be elevated in other neurological disorders beyond AD, further investigation into these conditions is required. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

Background/Objectives: To develop and evaluate an automated detection system for necrotizing soft tissue infection (NSTI) features on computed tomography (CT) images using the You Only Look Once version 10 (YOLOv10) model, aiming to improve diagnostic efficiency and surgical planning. Methods: This retrospective study included 31 patients with surgically confirmed NSTIs, spanning 2017–2023, from Chi Mei Medical Center, Taiwan. A total of 9001 CT images were annotated for four NSTI features: soft tissue ectopic gas, fluid accumulation, fascia edematous changes, and soft tissue non-enhancement. Model performance was evaluated using mean Average Precision (mAP), recall, and precision metrics. Results: The model achieved a mAP of 0.75, with recall and precision values of 0.74 and 0.72, respectively. Recall values for individual features were 0.76 for soft tissue ectopic gas, 0.66 for soft tissue non-enhancement, 0.92 for fascia edematous changes, and 0.68 for fluid accumulation. Conclusions: The YOLOv10-based system effectively detects four NSTI features on CT, including soft tissue ectopic gas, fluid accumulation, fascia edematous changes, and soft tissue non-enhancement. Full article

A revolutionary non-invasive method for the thorough evaluation of coronary artery disease (CAD) is fractional flow reserve (FFR) obtained from coronary computed tomography angiography (CCTA). Computed tomography-derived FFR (FFR_CT) assesses both the anatomical and functional significance of coronary lesions simultaneously by utilizing sophisticated computational models, including computational fluid dynamics, machine learning (ML), and Artificial Intelligence (AI) methods. The technological development, validation research, clinical uses, and real-world constraints of FFR_CT are compiled in this review. Large multicenter trials and registries consistently show that FFR_CT is a reliable gatekeeper to invasive coronary angiography (ICA) and increases diagnostic accuracy significantly when compared to coronary Computed Tomography Angiography (CTA) alone, especially in patients with intermediate-risk anatomy. Additionally, FFR_CT has demonstrated benefits in populations with in-stent restenosis (ISR) and in virtual procedural planning. Notwithstanding its advantages, the technique still requires high-quality imaging, and its practical application is constrained by expenses, processing requirements, and image distortions. Continuous developments in automation and deep learning should improve accessibility, effectiveness, and workflow integration in clinical settings. FFR_CT is expected to become more and more important in the individualized treatment of CAD by minimizing unnecessary invasive procedures and improving patient selection for revascularization. Full article

In this study, a multi-objective shape optimization framework was established for photovoltaic-powered underwater vehicles (PUVs) to systematically investigate multidisciplinary coupled design methodologies. Specifically, a global sensitivity analysis was conducted to identify four critical design parameters with 24 h energy consumption and cabin volume serving as dual optimization objectives. An integrated automated optimization workflow was constructed by incorporating parametric modeling, computational fluid dynamics (CFD) simulations, and dynamic surrogate models. Additionally, a new phased hybrid adaptive lower confidence bound (PHA-LCB) infill criterion was designed under the consideration of error-driven mechanisms, improvement feedback loops, and iterative attenuation factors to develop high-precision dynamic surrogate models. Coupled with the NSGA-II multi-objective genetic algorithm, this framework generated Pareto-optimal front solutions possessing significant engineering value. Furthermore, an optimal design configuration was ultimately determined through multi-criteria decision analysis. Compared to the initial form, it generates an additional 1148.12 Wh of electrical energy within 24 h, with an 22.36% increase in sailing range and a 2.77% improvement in cabin volume capacity. The proposed closed-loop “modeling–simulation–optimization” framework realized multi-objective optimization of PUV shape parameters, providing methodological paradigms and technical foundations for the engineering design of next-generation autonomous underwater vehicles. Full article

This study investigates the thermal dynamics and material behavior involved in the baking process for Lebanese flatbread, focusing on the heat transfer mechanisms, water loss, and dough expansion under high-temperature conditions. Despite previous studies on flatbread baking using impingement or conventional ovens, this work presents the first experimental investigation of the traditional Lebanese flatbread baking process under realistic industrial conditions, specifically using a high-temperature tunnel oven with direct flame heating, extremely short baking times (~10–12 s), and peak temperatures reaching ~650 °C, which are essential to achieving the characteristic pocket formation and texture of Lebanese bread. This experimental study characterizes the baking kinetics of traditional Lebanese flatbread, recording mass loss pre- and post-baking, thermal profiles, and dough expansion through real-time temperature measurements and video recordings, providing insights into the dough’s thermal response and expansion behavior under high-temperature conditions. A custom-designed instrumented oven with a steel conveyor and a direct flame burner was employed. The dough, prepared following a traditional recipe, was analyzed during the baking process using K-type thermocouples and visual monitoring. Results revealed that Lebanese bread undergoes significant water loss due to high baking temperatures (~650 °C), leading to rapid crust formation and pocket development. Empirical equations modeling the relationship between baking time, temperature, and expansion were developed with high predictive accuracy. Additionally, an energy analysis revealed that the total energy required to bake Lebanese bread is approximately 667 kJ/kg, with an overall thermal efficiency of only 21%, dropping to 16% when preheating is included. According to previous CFD (Computational Fluid Dynamics) simulations, most heat loss in similar tunnel ovens occurs via the chimney (50%) and oven walls (29%). These findings contribute to understanding the broader thermophysical principles that can be applied to the development of more efficient baking processes for various types of bread. The empirical models developed in this study can be applied to automating and refining the industrial production of Lebanese flatbread, ensuring consistent product quality across different baking environments. Future studies will extend this work to alternative oven designs and dough formulations. Full article

The accurate identification of seismic faults, which serve as crucial fluid migration pathways in hydrocarbon reservoirs, is of paramount importance for reservoir characterization. Traditional interpretation is inefficient. It also struggles with complex geometries, failing to meet the current exploration demands. Deep learning boosts fault identification significantly but struggles with edge accuracy and noise robustness. To overcome these limitations, this research introduces SwiftSeis-AWNet, a novel lightweight and high-precision network. The network is based on an optimized MedNeXt architecture for better fault edge detection. To address the noise from simple feature fusion, a Semantics and Detail Infusion (SDI) module is integrated. Since the Hadamard product in SDI can cause information loss, we engineer an Attention-Weighted Semantics and Detail Infusion (AWSDI) module that uses dynamic multi-scale feature fusion to preserve details. Validation on field seismic datasets from the Netherlands F3 and New Zealand Kerry blocks shows that SwiftSeis-AWNet mitigates challenges like the loss of small-scale fault features and misidentification of fault intersection zones, enhancing the accuracy and geological reliability of automated fault identification. Full article

Deep drilling and horizontal wells, as important means of unconventional oil and gas development, face problems with the high energy consumption but low removal efficiency of traditional well washing equipment, the uneven cleaning of horizontal well intervals, and an insufficient degree of automation. This paper proposes a novel hydraulic drive well washing device which consists of two main units. The wellbore cleaning unit comprises a hydraulic drive cutting–flushing module, a well cleaning mode-switching module, and a filter storage module. The unit uses hydraulic and mechanical forces to perform combined cleaning to prevent mud and sand from settling. By controlling the flow direction of the well washing fluid, it can directly switch between normal and reverse washing modes in the downhole area, and at the same time, it can control the working state of corresponding modules. The assembly control unit includes the chain lifting module and the arm assembly module, which can lift and move the device through the chain structure, allow for the rapid assembly of equipment through the use of a mechanical arm, and protect the reliability of equipment through the use of a centering structure. The device converts some of the hydraulic power into mechanical force, effectively improving cleaning and plugging removal efficiency, prolonging the downhole continuous working time of equipment, reducing manual operation requirements, and comprehensively improving cleaning efficiency and energy utilization efficiency. Full article

This study presents the design and implementation of a novel, sensor-based falling-sphere viscometer specifically tailored for measuring the viscosity of engine oil. The equipment utilizes a metallic sphere and two strategically placed sensors to determine the travel time over a predetermined distance within an oil-filled tube. By applying fundamental principles of fluid dynamics, including Stokes’ law, the system accurately calculates the dynamic viscosity based on the sphere’s velocity and the oil’s density. Experimental validation at particular temperature demonstrates the device’s sensitivity and reliability, which are critical for assessing oil degradation and engine performance. The simplicity and low cost of the design make it an attractive alternative to conventional, more complex viscometers. Furthermore, the automated data acquisition system reduces human error and enhances reproducibility of results. Overall, the developed instrument shows great promise for both laboratory research and practical maintenance applications in the automotive industry. Full article

Background: Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. Methods: This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. Results: The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. Conclusions: This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting. Full article

Despite decades of development, micro air vehicles (MAVs) still face challenges related to endurance. While solar power has been successfully implemented in larger aircraft as a clean and renewable source of energy, its adaptation to MAVs presents unique challenges due to payload constraints and complex surface geometries. To address this, this work proposes an automated algorithm for optimal solar panel arrangement on complex upper surfaces of the MAV. In addition to that, the aerodynamic performance is evaluated through computational fluid dynamics (CFD) simulations based on the Reynolds-Averaged Navier–Stokes (RANS) method. A multi-objective optimization approach simultaneously considers photovoltaic energy generation and aerodynamic efficiency. Wind tunnel validation and stability analysis of flight dynamics confirm the advantages of our optimized design. To our knowledge, this represents the first systematic framework for the energy–aerodynamic co-optimization of solar-powered MAVs (SMAVs). Flight tests of a $500\mathrm{mm}$-span tailless prototype demonstrate the practical feasibility of our approach with maximum solar cell deployment. Full article

Background: Hospitalized COVID-19 patients are particularly vulnerable to secondary bacterial infections, which can significantly worsen clinical outcomes. The aim of the study was to identify the cause of bacteremia in a group of hospitalized COVID-19 patients and find out the source of the outbreak to prevent further spread. Methods: Pathogen identification in blood cultures and sensitivity testing were carried out using the automated VITEK2 system. A total of 110 samples were tested; these were collected from patients’ colonization sites and from surfaces, materials and fluids used in the setting. Furthermore, multilocus sequence typing (MLST) and next-generation sequencing (NGS) were employed to characterize the isolates. Results: Achromobacter xylosoxidans was detected in the blood of nine hospitalized patients and in cotton used for disinfection; all isolates presented an identical antibiotic resistance pattern, and all carried the bla_OXA-114 gene which is intrinsic to this species. Infection control measures were implemented promptly. With one exception, all patients recovered and were discharged in good health. Conclusions: This outbreak underscores the urgent need for investigation and control of hospital infections, as bacteremia is associated with increased morbidity, mortality, hospitalization time, and cost. It also highlights the importance of close collaboration among healthcare professionals. Full article

This paper introduces an automated calibration system for color filters used in quantitative schlieren imaging, developed in response to prior findings highlighting the need for automation to reduce calibration time, minimize human error, and improve data accuracy and repeatability. Drawing from the authors’ experimental experience and practical application, the system demonstrates a significant enhancement in calibration efficiency—reducing the process from 2–5 h manually to just 15–30 min, representing time savings of up to 90%. Positioning accuracy improves from ±50–100 μm in manual setups to ±1–10 μm through precision-controlled automation, substantially lowering variability and increasing the reliability of pixel calibration curves. While calibration accuracy remains dependent on flow characteristics and post-processing capabilities, the system’s use of larger color filters—validated analytically and experimentally—further increases contrast sensitivity by 10–20%, enhancing the extraction of physical parameters such as velocity, temperature, and pressure fields. The setup features a modular, scalable architecture with a user-friendly interface, making it adaptable to diverse experimental environments and suitable for users at varying levels of expertise. Its iterative optimization and high-throughput capabilities position this system as a robust, flexible solution for advancing schlieren imaging techniques and enabling next-generation optical diagnostics in fluid dynamics research. Full article

Glioblastoma multiforme (GBM) is the most aggressive and common primary brain tumor. Magnetic resonance imaging (MRI) provides detailed visualization of tumor morphology, edema, and necrosis. However, manually segmenting GBM from MRI scans is time-consuming, subjective, and prone to inter-observer variability. Therefore, automated and reliable segmentation methods are crucial for improving diagnostic accuracy. This study employs an image semantic segmentation model to segment brain tumors in MRI scans of GBM patients. The MRI recall images include T1-weighted imaging (T1WI) and fluid-attenuated inversion recovery (FLAIR) sequences. To enhance the performance of the semantic segmentation model, image preprocessing techniques were applied before analyzing and comparing commonly used segmentation models. Additionally, a survival model was constructed using discrete genotype attributes of GBM patients. The results indicate that the DeepLabV3+ model achieved the highest accuracy for semantic segmentation, with an accuracy of 77.9% on T1WI image sequences, while the U-Net model achieved 80.1% accuracy on FLAIR image sequences. Furthermore, in constructing the survival model using a discrete attribute dataset, the dataset was divided into three subsets based on different missing value handling strategies. This study found that replacing missing values with 1 resulted in the highest accuracy, with the Bernoulli Bayesian model and the multinomial Bayesian model achieving an accuracy of 94.74%. This study integrates image preprocessing techniques and semantic segmentation models to improve the accuracy and efficiency of brain tumor segmentation while also developing a highly accurate survival model. The findings aim to assist physicians in saving time and facilitating preliminary diagnosis and analysis. Full article