Search Results (63,799)

Cedrus atlantica wood tar (CAWT) is traditionally used as a medicinal product, especially in low- and middle-income countries. Despite its traditional use, scientific support for its efficacy remains limited. This study evaluated the biological properties of CAWT using an integrated approach that combined qualitative and quantitative phytochemical analysis, disc diffusion and microdilution tests for antimicrobial assays (disc diffusion and microdilution), antioxidant activity (DPPH and ferric-reducing power assays), in silico ADMET/toxicity, docking, and MD/MMGBSA and provided a balanced comparison with reference antioxidants. This study demonstrated that CAWT is rich in secondary metabolites linked to biological activity, including polyphenols (307.39 ± 58.45 mg GAE/g), tannins (124.42 ± 6.14 mg TAE/g), and flavonoids (15.62 ± 2.53 mg QE/g). For free radical scavenging, CAWT inhibited DPPH with an IC50 of 19.781 ± 2.51 µg/mL and showed ferric-reducing activity with an IC50 of 83.7 ± 2.88 µg/mL for its antimicrobial activity against Pseudomonas aeruginosa; inhibition zones reached 35.66 ± 0.58 mm. In silico analysis, Swiss ADMET and pkCSM predicted ≥ 94% intestinal absorption, no cytochrome P450 liabilities, and low acute toxicity for six dominant terpenoids. Docking pinpointed trans-cadina-1(6),4-diene and α/β-himachalene as high-affinity ligands of LasR and gyrase B (ΔG ≈ −8 kcal mol⁻¹). A 100 ns GROMACS run confirmed stable hydrophobic locking of the lead LasR complex (RMSD 0.22 nm), while MM/GBSA calculated a dispersion-dominated binding free energy of −37 kcal mol⁻¹. Overall, CAWT showed in vitro antioxidant activity (DPPH and ferric-reducing assays) and inhibitory effects in disc diffusion assays, while in silico predictions for major terpenoids suggested favorable oral absorption and low acute toxicity. However, chemical composition analysis and bio-guided fractionation are necessary to confirm the antimicrobial activity and to validate the compounds responsible for the observed effects. Full article

Background/Objectives: Female tubal factor infertility is a major clinical challenge. While surgical repair of the fallopian tubes remains the traditional standard, biological fibrin sealants have been proposed to reduce tissue trauma and improve reproductive outcomes. Methods: We conducted database searches of PubMed/MEDLINE, EMBASE and Google Scholar until 31 August 2025, using the keywords “tubal anastomosis”, “tubal reanastomosis,” “tubal reanastomosis”, “uterine horn anastomosis”, “fibrin glue”, “fibrin sealant”, “biological sealant”, “tissue adhesive”, “rabbit”, “rat” and “sterilization reversal.” Reference lists of retrieved articles have been examined to find studies which tested end-to-end tubal (or small-animal uterine horn) anastomosis through biological adhesives with or without additional components to evaluate patency success, fertility results and adhesion formation. Results: Thirteen studies met the inclusion criteria (eleven animal; two human). Rat and rabbit models demonstrated that fibrin sealants with intraluminal splints and one-to-two anchoring sutures produced results comparable to microsutures for patency (tubal patency rates of 75–100%) and pregnancy success (pregnancy rates of 60–83%) while reducing surgical time and decreasing peritubal adhesions. The success rates of the procedures depended on the anastomosis locations. Isthmic–isthmic anastomosis produced better results than ampullary repairs which tended to fail or develop stenosis. Fibrin sealant-only repairs without splinting were associated with lower patency (almost 60%) despite acceptable histologic healing. Human data showed similar pregnancy rates (intrauterine pregnancy in about 40–50% of women) and tubal patency but no consistent decrease in adhesions. Ectopic pregnancy rates ranged from 9 to 11%. Conclusions: Fibrin sealants are useful adjuncts to microsurgical tubal repair, but they should not replace the basic repair procedures. The effectiveness of this procedure is dependent on three critical factors: precise segment alignment, proper use of splints and stents, and selection of segments with comparable caliber. In a personalized-medicine framework, fibrin-assisted reanastomosis may offer a tailored option for selected women who desire natural pregnancy. Modern standardized research is required to define indications and analyze how the adaptation of fibrin sealants in minimally invasive procedures affect reproductive outcomes, ectopic pregnancy rates, and adhesion development. Full article

The COVID-19 pandemic necessitated the transition of physiotherapy programmes from traditional face-to-face anatomy teaching to fully online delivery. Studies conducted during this period have documented students’ positive adaptation to online anatomy learning, highlighting benefits such as increased flexibility and reduced stress, while also noting challenges, including diminished practical exposure. Although much of the existing research centres on medical and dental education, there is a relative paucity of studies examining physiotherapy students’ perspectives. In this study, a purposive sample of 53 entry-level physiotherapy students (Years 1–3) completed a questionnaire evaluating their experiences with anatomy education during the pandemic. Responses were compared between groups (Years 1 and 2 versus Year 3) using the Mann–Whitney U test and effect-size calculations. Year 3 students who experienced both face-to-face and online learning preferred practical sessions with silent mentors in the anatomy hall, citing greater active engagement and deeper learning. Conversely, all year groups reported that online assessments, such as the online Objective Structured Practical Examination, were less stressful than traditional formats. Year 1 and 2 students, lacking prior face-to-face practical experience, expressed neutral views regarding online practical components. Overall, while online theory and assessment components were well received, hands-on practical experience remains highly valued for promoting student engagement. These findings support the development of blended anatomy pedagogy that leverages the strengths of both modalities to enhance resilience and adaptability in the face of future educational disruptions. Full article

Cornus officinalis is a valuable traditional Chinese medicinal (TCM) plant species with both therapeutic and ornamental attributes. It is widely used in TCM prescriptions to nourish the liver and kidneys and constitutes a critical component of numerous classical formulas. In recent years, the large-scale cultivation of this medicinal plant has been expanded in Xixia County, Henan Province, China. Field investigations have revealed widespread brown leaf spot, accompanied by reductions in yield and quality. In this study, symptomatic leaves were collected for pathogen isolation. Tissue isolations consistently yielded a Colletotrichum fungus, and morphology combined with multi-locus phylogenetic analyses (the internal transcribed spacer, glyceraldehyde-3-phosphate dehydrogenase, chitin synthase, actin, and β-tubulin) identified the pathogen as Colletotrichum siamense. Pathogenicity assays (conducted by either wounding and inoculating detached leaves with a mycelium plug or spraying a conidium suspension on healthy potted plants) reproduced field symptoms, and the pathogen was re-isolated, thereby fulfilling Koch’s postulates. In vitro fungicide assays showed that carbendazim, tebuconazole, and prochloraz were highly effective against the pathogen, providing preliminary information for chemical management. This is the first documentation of C. siamense causing leaf anthracnose on C. officinalis and provides a basis for developing targeted control strategies to mitigate disease impacts and preserve yield and quality. Full article

Substance-based medical devices (SBMDs) are increasingly used in wound care due to their favorable safety profile, physicochemical mechanisms of action, and therapeutic effectiveness. These products often incorporate biopolymers such as hyaluronic acid or chitosan, alone or in combination with antimicrobial agents like silver nanoparticles (AgNPs) or silver sulfadiazine (SSD), offering hydration, tissue protection, and control of microbial burden in both acute and chronic wounds. Despite their widespread clinical use, the regulatory classification of SBMDs under Regulation (EU) 2017/745 (MDR) remains one of the most challenging and debated areas within the current European framework. This review analyzes the scientific and regulatory context of topical SBMDs, with particular emphasis on borderline products that share similarities with medicinal products in terms of formulation, composition, or claimed effects. The discussion focuses on the application of MDR Annex VIII, specifically Rule 21 for substance-based devices and Rule 14 for devices incorporating medicinal substances with ancillary action, together with interpretative guidance provided by MDCG 2022-5 Rev.1 and the Association of the European Self-Care Industry (AESGP) Position Paper. Particular attention is given to the identification of the critical role of the primary mode of action (MoA) as the determining criterion for regulatory qualification, especially for products containing antimicrobial substances. Through selected examples and case analyses, the review highlights inconsistencies in classification across Member States and underscores the need for a more harmonized, evidence-based, and proportionate regulatory approach. Overall, SBMDs challenge traditional regulatory boundaries and call for a framework capable of accommodating complex, multifunctional products while ensuring patient safety and regulatory coherence. Full article

With the explosive growth of global data traffic, long-distance fiber optic transmission systems are continuously evolving towards higher capacity and longer distances. However, to overcome the high complexity of fiber dispersion compensation algorithms, various dispersion compensation techniques have emerged. This paper aims to systematically review and summarize dispersion compensation algorithms in long-distance fiber optic transmission. First, we briefly introduce the physical mechanism of fiber dispersion. Then, this paper focuses on digital domain compensation algorithms, dividing them into two major categories: compensation algorithms without penalty and with penalty. For compensation algorithms without penalty, we elaborate on traditional block processing strategies such as Overlap-Save (OLS), and various enhanced strategies combining intelligent filter segmentation and optimized frequency domain workflows. For compensation algorithms with penalty, we focus on analyzing a scheme that redesigns chromatic dispersion compensation (CDC) algorithm into a hardware-friendly structure using geometric clustering of taps, and finite-impulse-response (FIR) filters based on frequency response approximating the ideal inverse chromatic dispersion (CD) transfer function. By numerical simulation, we analyze the core principles, computational complexity, and compensation performance of each type of algorithm. Finally, this paper summarizes the limitations and development trends of existing dispersion compensation algorithms, pointing out that low-complexity and small-scale deployment algorithm structures will be an important research direction in the future. Full article

The interaction between the Photovoltaic station and the weak grid can easily trigger sub- or super-synchronous oscillation (SSO). In this article, the equivalent impedance model of the photovoltaic grid-connected system is built, and the mechanism of SSO is analyzed based on the global admittance criterion (GA). To mitigate the SSO, a Distribution Static Synchronous Compensator (D-STATCOM) supplementary damping control (SDC) strategy is proposed, which uses a three-parameter notch filter to extract the sub- or super-synchronous harmonic component without a phase shift. The component is superimposed on the modulated wave of the D-STATCOM through the gain link to obtain the modulation instruction. At the sub- or super-synchronous frequency, the D-STATCOM can be equivalent to the parallel impedance in the system and play a role in suppressing the sub- or super-synchronous oscillation. Compared to the complex combination filters in the traditional SDC, which require phase compensation and have poor adaptability, the three-parameter notch filter used in this SDC does not need a phase compensation stage and can effectively cope with the presence of oscillation frequencies on both sides of the fundamental frequency with a simpler design. Simulation results prove that the proposed scheme effectively improves the stability of photovoltaic generation under different short-circuit ratios, irradiance levels, and fault conditions. The proposed solution can be applied to photovoltaic generation equipped with D-STATCOM. Full article

With the increasing demand for high efficiency and high power density in photovoltaic power generation, triangular current mode (TCM) control has garnered significant attention due to its capability to achieve zero voltage switching (ZVS) for switches. However, TCM is inherently a variable-frequency control method. Traditional modeling approaches based on fixed-frequency assumptions neglect the non-linear characteristics and sideband effects introduced by frequency variations, failing to accurately describe the dynamic behavior of the system. This paper proposes a multi-frequency small-signal modeling method tailored for TCM inverters. Small-signal models characterizing the impact of duty cycle perturbations and frequency modulation perturbations on the output voltage are derived, and the joint effect of both the duty cycle and switching frequency is analyzed. On this basis, a loop gain expression incorporating sideband frequency components is derived using Mason’s gain formula. Finally, the proposed model is verified through simulation. The results demonstrate that, compared with the multi-frequency model, which only considers the effect of duty cycle control, the proposed multi-frequency model can more accurately predict the dynamic response of TCM inverters across a wide frequency range, providing a precise theoretical basis for the control system design of variable-frequency inverters. Full article

Background and Objectives: Hospital length of stay (LOS) after breast cancer surgery is a key indicator of postoperative recovery, healthcare quality, and hospital resource utilization. Traditional statistical approaches have identified general correlates of LOS but remain limited in predictive accuracy, particularly in heterogeneous real-world surgical populations. Machine learning (ML) models may offer improved performance by capturing nonlinear interactions among clinical, pathological, and operative factors. This study aimed to evaluate ML algorithms for LOS prediction and to identify determinants of prolonged hospitalization in a contemporary breast cancer cohort. Materials and Methods: We conducted a retrospective cross-sectional study of 198 consecutive breast cancer patients who underwent surgery between January 2022 and December 2023 at a single tertiary care center. Clinical, pathological, and surgical data were extracted from electronic medical records. Three regression models—multiple linear regression, Random Forest, and Gradient Boosting—were trained to predict continuous LOS, and three classification models were applied to prolonged LOS (≥10 days). Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and area under the curve (AUC). Feature importance was analyzed for the best-performing model. Results: The median LOS was 7 days (IQR 5–10), ranging from 1 to 26 days. Breast-conserving surgery showed the shortest LOS (median 3 days), while mastectomy with immediate reconstruction resulted in the longest stays (median 8 days). Random Forest regression achieved the lowest prediction error (MAE 2.31 days; RMSE 2.82; R² = 0.37), outperforming Gradient Boosting and substantially surpassing linear regression (MAE 8.63 days; R² = –8.17). Key predictors included age, surgical complexity, reconstruction modality, BMI, implant capacity, and tumor burden. Classification models yielded modest AUCs (0.545–0.589) with low sensitivity, indicating limited discriminative performance for dichotomized LOS outcomes. Conclusions: Machine-learning models, particularly Random Forest, substantially improve LOS prediction compared with classical regression and provide clinically meaningful insights into the drivers of hospitalization after breast cancer surgery. Continuous LOS modeling is more informative than binary thresholds. These findings support integrating ML-based tools into perioperative planning, resource allocation, and patient counseling in breast surgical care. Full article

Traditional vibration-based methods for bearing fault diagnosis, while prevalent, often require contact measurement, and sound signal is a broadband signal relative to the vibration signal. To overcome these limitations, this paper explores the advantages of acoustic signals, non-contact sensing, and rich broadband information and proposes a fault diagnosis framework based on acoustic features and deep learning. The core of our method is a CNN–attention mechanism–LSTM model, specifically designed to process one-dimensional sequential features: the 1D-CNN extracts local features from Mel frequency cepstral coefficient (MFCC) features, the attention mechanism (selecting ECA as the optimal solution) selectively enhances features, and the LSTM captures temporal dependencies, collectively enabling effective classification of fault types. Furthermore, to enhance model efficiency, a ReliefF-based feature selection algorithm is employed to identify and retain only the most discriminative acoustic features. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 99.90% in distinguishing normal, inner-ring, outer-ring, and mixed-defect bearings. Notably, results show that after using the feature selection algorithm, the number of parameters and the estimated total size are significantly reduced while ensuring that the accuracy remains basically unchanged. This work validates the effectiveness of non-contact solutions for bearing fault diagnosis using acoustic features and has enormous potential for industrial applications. Full article

The fanasváhter is a special tool used by boatbuilders to determine how steeply the boatboards should be positioned. Sámi mathematics teacher educators, together with Sea Sámi boatbuilders and a pre-service mathematics teacher, present a descriptive case study of a Sea Sámi boatbuilder’s use of this tool. The aim is to reveal mathematical understanding that might be relevant for vocational school teaching. Firstly, we analyze a semi-structured interview with a skilled boatbuilder with respect to knowledge and values embedded in the use of the fanasváhter. Because the Sea Sámi boatbuilding tradition was almost extinct after the Nazis’ devastation in World War II, there is a need for some creativity in the boatbuilders’ regeneration of Sea Sámi boatbuilding. An analysis of the use of the fanasváhter with respect to creativity reveals how creativity is important in Sea Sámi boatbuilding. The analysis further reveals that Sea Sámi boatbuilders compare angles, but they do not refer to any angles measured in degrees. This contrasts with traditional school mathematics. Thus, the Sea Sámi boatbuilders’ mathematics is less abstract and more intuitive than traditional school mathematics. Full article

Oversampling is common and effective in resolving the classification problem of imbalanced data. Traditional oversampling methods are prone to generating overlapping or noisy samples. Clustering can effectively alleviate the above problems to a certain extent. However, the quality of clustering results has a significant impact on the final classification performance. To address this problem, an oversampling algorithm based on the Gaussian distribution oversampling algorithm and the K-means clustering algorithm combining compactness and separateness (CSKGO) is proposed in this paper. The algorithm first uses the K-means clustering algorithm, combining compactness and separateness to cluster the minority samples, constructs the cluster compactness index and inter-cluster separateness index to obtain the optimal number of clusters and the clustering results, and obtains the local distribution characteristics of the minority samples through clustering. Secondly, the sampling ratio for each cluster is assigned based on the compactness of the clustering results to determine the number of samples for each cluster in the minority class. Then, the mean vectors and covariance matrices of each cluster are calculated, and the Gaussian distribution oversampling algorithm is used to generate new samples that match the distribution of characteristics of the real minority samples, which are combined with the majority samples to form balanced data. To verify the effectiveness of the proposed algorithm, 24 datasets were selected from the University of California Irvine (UCI) Repository, and they were oversampled using the CSKGO algorithm proposed in this paper and other oversampling algorithms, respectively. Finally, these datasets were classified using Random Forest, Support Vector Machine, and K-Nearest Neighbor Classifiers. The results indicate that the algorithm proposed in this paper has higher accuracy, F-measure, G-mean, and AUC values, which can effectively improve the classification performance of the imbalanced datasets. Full article

The Chang–Zhu–Tan Metropolitan Area, the first national-level metropolitan region in central China, faces a prominent conflict between urban expansion and the quality of the ecological environment (EEQ) amid rapid urbanization. Investigating the ecological evolution of this area holds both significant scientific and practical value. This study leverages the Google Earth Engine (GEE) platform and long-term Landsat remote sensing imagery to explore the spatiotemporal variations in EEQ in the Chang–Zhu–Tan Metropolitan Area from 2002 to 2022. A modified remote sensing ecological index (MRSEI) was developed by incorporating the Air Quality Difference Index (DI), and changes in EEQ were analyzed using Sen slope estimation and the Mann–Kendall test. Apart from that, using 2022 data as an example, the Optimal Parameter Geodetector (OPGD) was employed to evaluate the impacts of multifarious driving factors on EEQ. The main findings of the study are as follows: (1) In comparison with the traditional remote sensing ecological index (RSEI), MRSEI can more effectively reflect regional differences in EEQ. (2) The overall EEQ in the region is relatively good, with over 60% of the area classified as “excellent” or “good”. The spatial distribution follows a pattern of “higher at the edges, lower in the center”. (3) The EEQ trend in the study area generally suggests reinforcement, though central areas such as Kaifu District and Tianxin District exhibit varying degrees of degradation. (4) Human factors have a greater impact on EEQ than natural factors. Land Use and Land Cover Change (LUCC) is the primary driver of the spatial differentiation in the regional ecological environment, with the interaction of these factors producing synergistic effects. The results of this study strongly support the need for ecological protection and green development in the Chang–Zhu–Tan Metropolitan Area, offering valuable insights for the sustainable development of other domestic metropolitan regions. Full article

This study re-examines generational differences in media literacy and news consumption within the evolving digital landscape. It expands on the well-known dichotomy of Digital Natives and Digital Immigrants by proposing a new conceptual framework that introduces the terms Analog Anchors and Digital Floaters. These terms aim to reflect the heterogeneity and fluidity more accurately, the adaptive nature of users’ engagement with digital media. A quantitative survey was conducted using an online questionnaire distributed to Greek participants (N = 1020) through a non-probability convenience sampling method. The analysis revealed significant variations in digital literacy, news consumption habits, and skepticism toward the media across generations. Findings indicate that the relationships with technology and information are not linear or age-bound but are shaped by cultural, cognitive, and social parameters. High levels of media skepticism observed across all age groups further challenge traditional divides. As a result, this study argues for a paradigm shift that captures the complexity of media literacy in the platform era, moving from static generational labels towards a more dynamic understanding of users as Analog Anchors and Digital Floaters. Full article

Deep eutectic solvents (DESs) have attracted significant attention as eco-friendly and sustainable alternatives to conventional, often toxic, organic solvents. They are easy to synthesize, and their tunable physicochemical properties enable their application in microextraction techniques for a wide range of analytes. However, some DESs may exhibit thermal instability, and their high viscosity or solubility can influence the extraction efficiency. Despite these limitations, in recent years, DESs have been successfully used in multiple roles in solid-phase microextraction (SPME). They may be used to functionalize or modify sorbent materials, thereby forming composite sorbents with enhanced performance. Moreover, DESs can be combined with polymers to produce hybrid materials with improved extraction capabilities. Additionally, DESs can act as porogens within SPME sorbents, increasing sorption capacity and, consequently, extraction efficiency. They can also serve as green desorption solvents, replacing traditional volatile organic solvents during the recovery of analytes from sorbent materials. This review synthesizes current knowledge on the implementation of DESs in SPME techniques, critically evaluating their primary advantages and inherent limitations. The novelty of this review lies in the assessment of DES-based SPME through the metrics of greenness and sustainable chemistry. Furthermore, the review identifies research perspectives and priorities to advance DES-based SPME, including: the integration of predictive modeling (COSMO-RS, machine learning) to elucidate DES-analytes interactions; the adoption of 3D printing for the precision fabrication of DES-based sorbents; the standardization of DES-based SPME performance; and the exploration of natural DESs for in vivo SPME in biomedical applications. Full article