In this paper, a novel fluorescent detection method for glucose and lactic acid was developed based on fluorescent iron nanoclusters (Fe NCs). The Fe NCs prepared using hemin as the main raw material exhibited excellent water solubility, bright red fluorescence, and super sensitive response to hydrogen peroxide (H₂O₂). This paper demonstrates that Fe NCs exhibit excellent peroxide-like activity, catalyzing H₂O₂ to produce hydroxyl radicals (^•OH) that can quench the red fluorescence of Fe NCs. In this paper, a new type of glucose sensor was established by combining Fe NCs with glucose oxidase (GluOx). With the increase in glucose content, the fluorescence of Fe NCs decreases correspondingly, and the glucose content can be detected in the scope of 0–200 μmol·L⁻¹ (μM). Similarly, the lactic acid sensor can also be established by combining Fe NCs with lactate oxidase (LacOx). With the increase in lactic acid concentration, the fluorescence of Fe NCs decreases correspondingly, and the lactic acid content can be detected in the range of 0–100 μM. Furthermore, Fe NCs were used in the preparation of gel test strip, which can be used to detect H₂O₂, glucose and lactic acid successfully by the changes of fluorescent intensity. Full article

This paper presents an in-depth analysis of the Kopčić House, a significant example of modernist architecture in Sarajevo, Bosnia and Herzegovina, focusing on its structural-specific features and seismic performance. The Kopčić House embodies a confined masonry structure with innovative construction features, combining load-bearing masonry walls with reinforced concrete elements. This architectural approach was pioneering for its time, combining traditional construction methods with innovative materials and techniques. Detailed analysis using numerical modeling techniques, specifically 3D modeling with the 3Muri software (Vers.14.2.0.4), was conducted to assess the seismic resilience of the structure. The analysis considered different load distributions and eccentricities to comprehensively evaluate the building’s response to lateral forces. The findings of this research reveal the structural capacity and potential vulnerabilities of the Kopčić House when subjected to seismic events. While the building demonstrates inherent strength due to its confined masonry design, areas requiring structural strengthening were identified through numerical simulations. This study contributes to the broader understanding of confined masonry construction within the context of modernist architecture. By integrating historical research with advanced structural analysis, this work aims to bridge the gap between architectural heritage and contemporary engineering practices. Full article

From the start of river incision onward, the abandoned terrace surface is only reached by floods during peak discharges. Two main flood facies are distinguished: a relatively high-energetic, coarse-grained facies and a relatively low-energetic, fine-grained facies. In general, the flood deposits become gradually finer-grained and the finer-grained facies relatively more prominent when the river incises progressively deeper. This signifies a delayed and prolonged effect of channel incision and flood deposition compared with the climate changes that initiated the incision. However, these long-term trends may be interrupted by shorter-term events of flooding or non-deposition. Those short events are expressed by cycles of coarse-grained deposits from small/shallow flooding channels due to short peak discharges or fine-grained suspended sediment and incipient soils during periods of low flow. These short events may be attributed to short climatic episodes or intermittent intrinsic river evolution. Full article

The aim of this study was to evaluate the effect of the combination of neovestitol–vestitol (CNV) compounds obtained from Brazilian red propolis on the microbiological profile of a mature multispecies subgingival biofilm. The biofilm with 32 bacterial species associated with periodontitis was formed for seven days using a Calgary device. Treatment with CNV (1600, 800, 400, and 200 μg/mL), amoxicillin (54 μg/mL), and vehicle control was performed for 24 h on the last day of biofilm formation. Biofilm metabolic activity and DNA–DNA hybridization (checkerboard) assays were performed. The groups treated with CNV 1600 and amoxicillin reduced 25 and 13 species, respectively, compared to the control vehicle treatment (p ≤ 0.05); both reduced P. gingivalis, while only CNV reduced T. forsythia. When the data from the two treatments (CNV and AMOXI) were compared, a statistically significant difference was observed in 13 species, particularly members of Socransky’s orange complex. Our results showed that CNV at 1600 μg/mL showed the best results regarding the metabolic activity of mature biofilms and obtained a reduction in species associated with the disease, such as T. forsythia, showing a better reduction than amoxicillin. Therefore, CNV seems to be a promising alternative to eradicate biofilms and reduce their pathogenicity. Full article

Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts. Full article

Hyperspectral imaging systems frequently rely on spectral rather than spatial resolving power for identifying objects within a scene. A hyperspectral imaging system’s response to point targets under flight conditions provides a novel technique for extracting system-level radiometric performance that is comparable to spatially unresolved objects.The system-level analysis not only provides a method for verifying radiometric calibration during flight but also allows for the exploration of the impacts on small target radiometry, post orthorectification. Standard Lambertian panels do not provide similar insight due to the insensitivity of orthorectification over a uniform area. In this paper, we utilize a fixed mounted hyperspectral imaging system (radiometrically calibrated) to assess eight individual point targets over 18 drone flight overpasses. Of the 144 total observations, only 18.1% or 26 instances are estimated to be within the uncertainty of the predicted entrance aperture-reaching radiance signal. For completeness, the repeatability of Lambertian and point targets are compared over the 18 overpasses, where the effects of orthorectification drastically impact the radiometric estimate of point targets. The unique characteristic that point targets offer, being both a known spatial and radiometric source, is that they are the only field-deployable method for understanding the small target radiometric performance of drone-based hyperspectral imaging systems. Full article

The aim of the paper is to present two results concerning real hypersurfaces in the six-dimensional sphere $S^6\left(1\right)$. More precisely, we prove that real hypersurfaces with the Lie-parallel shape operator A must be totally geodesic hyperspheres. Additionally, we classify real hypersurfaces in a nearly Kähler sphere $S^6\left(1\right)$ whose Lie derivative of the shape operator coincides with its covariant derivative. Full article

Background/Objectives: Cervical spine fractures in ankylosing spondylitis (AS) are characterized as highly unstable fractures posing an elevated risk of neurological deficit and a significantly elevated mortality rate. This study assesses the efficacy and safety of single-stage plate stabilization with ventral cement augmentation in treating subaxial cervical spine fractures in patients with AS. Methods: Over 86 months, 38 patients diagnosed with AS received ventral plate stabilization with cement augmentation after suffering unstable subaxial cervical fractures. No additional dorsal stabilization was used in any of these surgeries. Results: There were no complications as a result of cement leakage. During the follow-up period, screw loosening and implant displacement were documented in two out of 38 cases. At the time of data analysis, 17 patients who had undergone treatment had died, representing 44.7% of the total cases. Seven patients died within 1 month, two patients died within 6 months, four patients died within 1 year, and four patients died after 1 year. Conclusions: Our study shows that a single-stage anterior screw and plate fixation of the cervical spine with cement augmentation could be a feasible and effective method to treat cervical spine fractures in patients with AS. Full article

The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented. Full article

Glioblastoma (GBM) cells are highly invasive, infiltrating the surrounding normal brain tissue, thereby limiting the efficacy of surgical resection and focal radiotherapy. Cysteamine, a small aminothiol molecule that is orally bioavailable and approved for cystinosis, has potential as a cancer treatment by inhibiting tumor cell invasion and metastasis. Here we demonstrate that these potential therapeutic effects of cysteamine are likely due to the inhibition of matrix metalloproteinases (MMPs) in GBM. In vitro assays confirmed that micromolar concentrations of cysteamine were not cytotoxic, enabling the interrogation of the cellular effects without confounding tumor cell loss. Cysteamine’s inhibition of MMP activity, especially the targeting of MMP2, MMP9, and MMP14, was observed at micromolar concentrations, suggesting the mechanism of action in suppressing invasion and cell migration is by inhibition of these MMPs. These findings suggest that achievable micromolar concentrations of cysteamine effectively inhibit cancer cell invasion and migration in GBM, supporting the potential for use as an adjunct cancer treatment. Full article

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs. Full article

This paper examines the impact of a multilayered gradient coating, applied via plasma-activated chemical vapor deposition (PACVD), on the structural and mechanical attributes of nanostructured WC-Co cemented carbides. WC-Co samples containing 5 and 15 wt.% Co were synthesized through a hot isostatic pressing (HIP) process using nanoparticle powders and coated with two distinct multilayer coatings: titanium nitride (TiN) and titanium carbonitride (TiCN). Nanosized grain formation without microstructural defects of the substrates, prior to coating, was confirmed by magnetic saturation and coercivity testing, microstructural analysis, and field emission scanning electron microscope (FESEM). Nanoindentation, fracture toughness and hardness testing were conducted for uncoated samples. After coatings deposition, characterizations including microscopy, surface roughness determination, adhesion testing, coating thickness measurement, and microhardness examination were conducted. The impact of deposited coatings on wear resistance of produced hardmetals was analyzed via scratch test and dry sliding wear test. Samples with higher Co content exhibited improved adhesion, facilitating surface cleaning and activation before coating. TiN and TiCN coatings demonstrated similar roughness on substrates of identical composition, suggesting Co content’s minimal influence on layer growth. Results of the mechanical tests showed higher microhardness, higher elastic modulus, better adhesion, and overall superior tribological properties of the TiCN coating. Full article

Organoid models have become an integral part of the research methodology in the lung field. These systems allow for the study of progenitor and stem cell self-renewal, self-organization, and differentiation. Distinct models of lung organoids mimicking various anatomical regions of mature lungs have emerged in parallel to the increased gain of knowledge regarding epithelial stem and progenitor cell populations and the corresponding mesenchymal cells that populate the in vivo niche. In the distal lung, type 2 alveolar epithelial cells (AEC2s) represent a stem cell population that is engaged in regenerative mechanisms in response to various insults. These cells self-renew and give rise to AEC1s that carry out gas exchange. Multiple experimental protocols allowing the generation of alveolar organoids, or alveolospheres, from murine lungs have been described. Among the drawbacks have been the requirement of transgenic mice allowing the isolation of AEC2s with high viability and purity, and the occasional emergence of bronchiolar and bronchioalveolar organoids. Here, we provide a refined gating strategy and an optimized protocol for the generation of alveolospheres from wild-type mice. Our approach not only overcomes the need for transgenic mice to generate such organoids, but also yields a pure culture of alveolospheres that is devoid of bronchiolar and bronchioalveolar organoids. Our protocol contributes to the standardization of this important research tool. Full article

To better simulate the prices of underlying assets and improve the accuracy of pricing financial derivatives, an increasing number of new models are being proposed. Among them, the Lévy process with jumps has received increasing attention because of its capacity to model sudden movements in asset prices. This paper explores the Hamilton–Jacobi–Bellman (HJB) equation with a fractional derivative and an integro-differential operator, which arise in the valuation of American options and stock loans based on the Lévy-$\alpha$-stable process with jumps model. We design a fast solution strategy that includes the policy iteration method, Krylov subspace method, and banded preconditioner, aiming to solve this equation rapidly. To solve the resulting HJB equation, a finite difference method including an upwind scheme, shifted Grünwald approximation, and trapezoidal method is developed with stability and convergence analysis. Then, an algorithmic framework involving the policy iteration method and the Krylov subspace method is employed. To improve the performance of the above solver, a banded preconditioner is proposed with condition number analysis. Finally, two examples, sugar option pricing and stock loan valuation, are provided to illustrate the effectiveness of the considered model and the efficiency of the proposed preconditioned policy–Krylov subspace method. Full article

This paper introduces an innovative method for load frequency control (LFC) in multi-area interconnected power systems vulnerable to denial-of-service (DoS) attacks. The system is modeled as a switching system with two subsystems, and an adaptive control algorithm is developed. Initially, a dynamic linear data model is used to model each subsystem. Next, a model-free adaptive control strategy is introduced to maintain frequency stability in the multi-area interconnected power system, even during DoS attacks. A rigorous stability analysis of the power system is performed, and the effectiveness of the proposed approach is demonstrated by applying it to a three-area interconnected power system. Full article

This paper focuses on improving automated approaches to glaucoma diagnosis, a severe disease that leads to gradually narrowing vision and potentially blindness due to optic nerve damage occurring without the patient’s awareness. Early diagnosis is crucial. By utilizing advanced deep learning technologies and robust image processing capabilities, this study employed four types of input data (retina fundus image, region of interest (ROI), vascular region of interest (VROI), and color palette images) to reflect structural issues. We addressed the issue of data imbalance with a modified loss function and proposed an ensemble model based on the vision large language model (VLLM), which improved the accuracy of glaucoma classification. The results showed that the models developed for each dataset achieved 1% to 10% higher accuracy and 8% to 29% improved sensitivity compared to conventional single-image analysis. On the REFUGE dataset, we achieved a high accuracy of 0.9875 and a sensitivity of 0.9. Particularly in the ORIGA dataset, which is challenging in terms of achieving high accuracy, we confirmed a significant increase, with an 11% improvement in accuracy and a 29% increase in sensitivity. This research can significantly contribute to the early detection and management of glaucoma, indicating potential clinical applications. These advancements will not only further the development of glaucoma diagnostic technologies but also play a vital role in improving patients’ quality of life. Full article

With the growing use of steel-fiber-reinforced-concrete (SFRC) beams in environmentally friendly and rapid construction, it is essential to assess their impact performance. These beams may encounter unexpected impact loadings from accidents or terrorist attacks during service life. This study explored the impact of steel fiber content and drop hammer height on the impact load testing of corrosion-treated SFRC beams. Experiments were conducted with varying steel fiber contents (0%, 0.25%, 0.5%, 0.75%, and 1.0%), and drop hammer height (1 m, 2 m, and 3 m). The corrosion test demonstrates that SFRC beams supplemented with steel fibers showcase a diminished surface rust spot area in comparison to those lacking fibers. This improvement is ascribed to the bonding between fibers and the concrete matrix, along with their current-sharing properties. SFRC beams, subjected to impact testing, exhibit concrete crushing at the top without spalling, showcasing improved impact resistance due to increased fiber content, which reduces crack formation. Additionally, different fiber contents yield varied responses to impact loads, with higher fiber content notably enhancing overall beam performance and energy dissipation capacity. Energy dissipation analysis shows a moderate increase with higher fiber contents, and impulse impact force generally rises with fiber content, indicating improved impact resistance. Full article

This study aimed to compare remotely sensed data with in situ data using the AquaCrop simulation model for accurately monitoring growth conditions and predict grassland biomass in the north-eastern and central-western regions of Poland from 2020 to 2022. The model was calibrated using input data, including daily climate parameters from the ERA5-Land Daily Aggregated dataset, crop characteristics (initial canopy cover, maximum canopy cover, and harvest index), and soil characteristics. Additionally, parameters such as the leaf area index (LAI), soil texture classes, and plant growth stages were obtained through field campaigns. The grassland’s biomass simulation results indicate that the root mean square error (RMSE) values for the north-eastern region ranged from 0.12 to 0.35 t·ha⁻¹, while for the central-western region, they ranged from 0.07 to 0.12 t·ha⁻¹. Overall, the outcomes obtained from Sentinel-2 data perform comparably to the in situ measurements, and in some instances, even yield superior results. This study contributes valuable insights into grass production management on farms, providing essential information and tools for managers to better understand grass growth and development. Full article

The purpose of the study was to identify the durations of maintaining apnea, in different static positions, with and without the use of a nose clip, in girls aged between 7 and 14 years,. The study included a total number of 92 girls, grouped by age into four groups of 2-year spans (7–8, 9–10, 11–12, 13–14 years), and depending on the experience of practicing synchronized swimming (6–42 months). In the study we applied five physical tests where apnea maintenance times were recorded in different static positions: Apnea Test of Facial floatation with and without nose clip, Apnea Test of Front tuck with and without nose clip and Apnea Test of Front layout with support to scull. The statistical analysis was performed with SPSS-24. During the study, a program of specific exercises to learn/consolidate the apneic breathing specific to artistic swimming was implemented, for a time interval of 3 months. The results were recorded at the beginning of the study (TI) and at the end of the study (TF). Analyzing the results of the study, we found positive and statistically significant improvements, related to age and experience. The most significant progress, taking into account the averages between the final and initial tests, was recorded in relation to Facial Flotation for 1.301 s for the 7–8-year-old group and 1.110 s for the 9–10-year-old group; the 11–12-year-old group recorded the most positive effect in the Facial Flotation test with a nose clip, with a result of 0.853 s, and in the 13–14-year-old group in the front tuck with nose clip test, a result of 0.807 s was reached. In all tests of the study, the Cohen’s values in all groups fell between 0.184 and 0.478, the size of the effect being small and medium. The ANOVA analysis of variance showed that the differences were statistically significant for p < 0.05 between the arithmetic means of the four groups according to age and sport experiences. For all groups, the value of Wilks’ Lambda was 0.009 (p < 0.01) for age and 0 (p < 0.01) for sports experience, highlighting large differences between groups. We conclude that the development of the ability to maintain apnea specific to synchronized swimming shows an upward trajectory, being conditioned by the training methodology, the age of the subjects and the sports experience. The small and medium values of the effect size highlight the fact that the improvement in apnea maintenance time is dependent on the duration and frequency of the apnea exercises performed in technical conditions specific to synchronized swimming. The training methodology must be adapted to the particularities of age, sports experience and the characteristics of synchronized swimming. Full article

In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate. Full article

The Guangdong historical trail comprises two ancient trails, known as Yile (宜乐) and Xijing (西京); can be found within the borders of Lechang (乐昌); and is an integral part of the northern section of the Guangdong historical trail, connecting Hunan and Guangdong. Along these routes, there are various types of ancestral halls. The objectives of this study include analyzing various levels of information about ancestral hall architecture, determining their respective importance through AHP hierarchical analysis, classifying architectural features via cluster analysis using SPSS, and exploring the geographical distribution trajectories of ancestral halls along the ancient paths using GIS technology. Ultimately, this study seeks to shed light on the influence of different cultures brought by the ancient road on the distribution of architectural types and identify three distinct types of ancestral halls. Full article