Search Results (3,795)

A comprehensive bibliometric analysis of literature is imperative to elucidate current research landscapes and hotspots in the interplay between gut microbiota and hypertension, identify knowledge gaps, and establish theoretical foundations for the future. We used publications retrieved from the Web of Science Core Collection (WoSCC) and SCOPUS databases (January 2001–December 2024) to analyze the annual publication trends with GraphPad Prism 9.5.1, to evaluate co-authorship, keywords clusters, and co-citation patterns with VOSviewer 1.6.20, and conducted keyword burst detection and keyword co-occurrence utilizing CiteSpace v6.4.1. We have retrieved 2485 relevant publications published over the past 24 years. A 481-fold increase in global annual publications in this field was observed. China was identified as the most productive country, while the United States demonstrated the highest research impact. For the contributor, Yang Tao (University of Toledo, USA) and the University of Florida (USA) have emerged as the most influential contributors. Among journals, the highest number of articles was published in Nutrients (n = 135), which also achieved the highest citation count (n = 5397). The emergence of novel research hotspots was indicated by high-frequency keywords, mainly “hypertensive disorders of pregnancy”, “mendelian randomization”, “gut-heart axis”, and “hepatitis B virus”. “Trimethylamine N-oxide (TMAO)” and “receptor” may represent promising new research frontiers in the gut microbiota–hypertension nexus. The current research trends are shifting from exploring the factors influencing gut microbiota and hypertension to understanding the underlying mechanisms of these factors and the potential therapeutic applications of microbial modulation for hypertension management. Full article

Microplastics (MPs) present in farmland soils, where urban compost has been distributed since 2005, were extracted using a device based on elutriation, a method developed for marine sediments but not yet used in soil. Since (i) fine earth (diameter < 2 mm) is the standard fraction used for soil analysis and (ii) the size of MPs contained in urban compost may exceed that value, MP were recovered from both the entire soil and fine earth. The recovered MPs pieces were weighed, counted, and characterized using FTIR photoacoustic spectroscopy (FTIR-PAS). Both the mass and number of recovered MPs pieces (>34 µm) were comparable to those reported in the literature for soils. Polystyrene, polyethylene, and polypropylene are the primary polymers. Nevertheless, some issues were highlighted: (i) the importance of sampling the soil by volume, and (ii) the need of analyzing the entire soil sample rather than just the fraction below 2 mm, commonly used in soil analysis; (iii) the necessity of breaking up (i.e., by ultrasonication and/or dispersion) soil aggregates that may withstand the elutriation process. Full article

Background and Objectives: Three-dimensional (3D) scanning is increasingly utilized in medical practice, from orthotics to surgical planning. However, traditional hand measurement techniques remain inconsistent and prone to human error and are often time-consuming. This research evaluates the practicality of a commercial 3D scanning method by comparing the accuracy of scans conducted by two user groups. Materials and Methods: This study evaluated the following two groups: an experimental group (n = 45) and a control group (n = 42). A total of 261 hand scans were captured using the Structure Sensor Pro 3D scanner for iPad (Structure, Boulder, CO, USA). The scans were then evaluated using Meshmixer software (version 3.5.474), analyzing key parameters, such as surface area, volume, number of vertices, and triangles, etc. Furthermore, a digital literacy test and a user experience survey were conducted to support a more comprehensive evaluation of participant performance within the study. Results: The experimental group outperformed the control group on all measured parameters, including surface area, volume, vertices, triangle, and gap count, with large effect sizes observed. User experience data revealed that participants in the experimental group rated the 3D scanner significantly higher across all dimensions, particularly in ease of use, excitement, supportiveness, and practicality. Conclusions: A short 15 min training session can promote scan reliability, demonstrating that even minimal instruction improves users’ proficiency in 3D scanning, fundamental for supporting clinical accuracy in diagnosis, surgical planning, and personalized device manufacturing Full article

Ecdysterone controls moulting and reproduction in insects, crustaceans, and helminths. It is also produced by many plants, probably as an insect deterrent. The steroid is not made by vertebrates but has anabolic effects in mammals and could be useful for treating sarcopenia in aged horses. However, ecdysterone is banned in horseracing and equestrian sports, and with no limit of reporting, the risk of unintended exposure to this naturally occurring prohibited substance is a concern. To explore this risk, pasture plants and hay samples were analysed for ecdysterone content, as well as samples of blood, faeces, and intestinal mucosa from horses (euthanized for non-research purposes) with varying degrees of endo-parasite infestation. The variability in serum ecdysterone concentrations between different horses after administering a fixed dose was also examined. Ecdysterone was detected in 24 hay samples (0.09 to 3.74 µg/g) and several weeds, with particularly high concentrations in Chenopodium album (244 µg/g) and Solanum nigrum (233 µg/g). There was a positive correlation between faecal ecdysterone and faecal egg counts, but no effect of anthelmintic treatment and no relation to the number of encysted cyathostome larvae in the large intestine mucosa. Certain horses maintained an unusually high serum ecdysterone concentration over several weeks and/or displayed an abnormally large response to oral ecdysterone administration. Thus, the risk of environmental exposure to ecdysterone is apparent, and several factors must be considered when determining an appropriate dosage for clinical studies or setting a reporting threshold for equine sports. Full article

In recent years, passive acoustic monitoring (PAM) technology has significantly contributed to advancements in aquaculture techniques, system iterations, and increased production yields within intelligent feeding systems for Penaeus vannamei. However, current PAM-based intelligent feeding systems do not incorporate environmental factors into the decision process, limiting the improvement of monitoring accuracy in complex environments such as ponds. To establish a connection between environmental factors and the feeding acoustics of P. vannamei, this study utilized PAM technology combined with video analysis to investigate the effects of three key environmental factors—temperature, ammonia nitrogen, and nitrite nitrogen—on the feeding behavioral characteristics of shrimp, with a specific focus on acoustic signals “clicks”. The results demonstrated a significant correlation between the number of clicks and feed consumption in shrimp across different treatments, establishing this stable relationship as a reliable indicator for assessing shrimp feeding status. When water temperature increased from 20 °C to 32 °C, shrimp feed consumption showed an elevation from 0.46 g to 0.95 g per 30 min, with the average number of clicks increasing from 388 to 2947.58 and sound pressure levels rising accordingly. Conversely, ammonia nitrogen at 12 mg/L reduced feed consumption by 0.15 g and decreased click counts by 911.75 pulses compared to controls, while nitrite nitrogen at 40 mg/L similarly suppressed feed consumption by 0.15 g and the average number of clicks by 304.75. A rise in water temperature stimulated shrimp behaviors such as feeding, swimming, and foraging, while elevated concentrations of ammonia nitrogen and nitrite nitrogen significantly inhibited shrimp activity. Redundancy analysis revealed that temperature was the most prominent factor among the three environmental factors influencing shrimp feeding. This study is the first to quantify the specific effects of common environmental factors on the acoustic feeding signals and feeding behavior of P. vannamei using PAM technology. It confirms the feasibility of using PAM technology to assess shrimp feeding conditions under diverse environmental conditions and the necessity of integrating environmental monitoring modules into future feeding systems. This study provides behavioral evidence for the development of precise feeding technologies and the upgrade of intelligent feeding systems for P. vannamei. Full article

This study employs a deep learning-based object detection model, GoogleNet, to identify cracks in cultural heritage images. Subsequently, a semantic segmentation model, SegNet, is utilized to determine the location and extent of the cracks. To establish a scale ratio between image pixels and real-world dimensions, a parallel laser-based measurement approach is applied, enabling precise crack length calculations. The results indicate that the percentage error between crack lengths estimated using deep learning and those measured with a caliper is approximately 3%, demonstrating the feasibility and reliability of the proposed method. Additionally, the study examines the impact of iteration count, image quantity, and image category on the performance of GoogleNet and SegNet. While increasing the number of iterations significantly improves the models’ learning performance in the early stages, excessive iterations lead to overfitting. The optimal performance for GoogleNet was achieved at 75 iterations, whereas SegNet reached its best performance after 45,000 iterations. Similarly, while expanding the training dataset enhances model generalization, an excessive number of images may also contribute to overfitting. GoogleNet exhibited optimal performance with a training set of 66 images, while SegNet achieved the best segmentation accuracy when trained with 300 images. Furthermore, the study investigates the effect of different crack image categories by classifying datasets into four groups: general cracks, plain wall cracks, mottled wall cracks, and brick wall cracks. The findings reveal that training GoogleNet and SegNet with general crack images yielded the highest model performance, whereas training with a single crack category substantially reduced generalization capability. Full article

This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in blade number, expanded area ratio (EAR), pitch-to-diameter ratio (P/D), FCR, and cupping percentage. A multi-layer artificial neural network (ANN) is trained to predict thrust, torque, and open-water efficiency (η_o) with a high coefficient of determination (R²), greater than 0.9999. The ANN is integrated into an optimization algorithm to identify optimal propeller designs for the KRISO Container Ship (KCS) using empirical constraints for cavitation and tip speed. Unlike prior studies that rely on boundary element method (BEM)-ML hybrids or multi-fidelity simulations, this study introduces a geometry-coupled analysis of FCR and cupping—parameters often treated independently—and applies empirical cavitation and acoustic (tip speed) limits to guide the design process. The results indicate that incorporating 1.0–1.5% cupping leads to a significant improvement in efficiency, up to 9.3% above the reference propeller, while maintaining cavitation safety margins and acoustic limits. Conversely, designs with non-zero FCR values (0.5–1.5%) show a modest efficiency penalty (up to 4.3%), although some configurations remain competitive when compensated by higher EAR, P/D, or blade count. The study confirms that the combination of cupping with optimized geometric parameters yields high-efficiency, cavitation-safe propellers. Furthermore, the ML-based framework demonstrates excellent potential for rapid, accurate, and scalable propeller design optimization that meets both performance and regulatory constraints. Full article

A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: A. organic system (control)—without the use of chemical plant protection products and NPK mineral fertilization; B. conventional system—the use of plant protection products and NPK fertilization in the range and doses recommended for spring wheat; C. integrated system—use of plant protection products and NPK fertilization in an “economical” way—doses reduced by 50%. The second experimental factor was irrigation strategy: 1. no irrigation—control; 2. double irrigation; 3. multiple irrigation The aim of the research was to determine the physical, chemical, and enzymatic properties of loess soil under spring wheat crops as influenced by the factors listed above. The highest organic C content of the soil (1.11%) was determined in the integrated system with multiple irrigation of spring wheat, whereas the lowest one (0.77%)—in the conventional system without irrigation. In the conventional system, the highest contents of total N (0.15%), P (131.4 mg kg⁻¹), and K (269.6 mg kg⁻¹) in the soil were determined under conditions of multiple irrigation. In turn, the organic system facilitated the highest contents of Mg, B, Cu, Mn, and Zn in the soil, especially upon multiple irrigation of crops. It also had the most beneficial effect on the evaluated physical parameters of the soil. In each farming system, the multiple irrigation of spring wheat significantly increased moisture content, density, and compaction of the soil and also improved its total sorption capacity (particularly in the integrated system). The highest count of beneficial fungi, the lowest population number of pathogenic fungi, and the highest count of actinobacteria were recorded in the soil from the organic system. Activity of soil enzymes was the highest in the integrated system, followed by the organic system—particularly upon multiple irrigation of crops. Summing up, the present study results demonstrate varied effects of the farming systems on the quality and health of loess soil. From a scientific point of view, the integrated farming system ensures the most stable and balanced physicochemical and biological parameters of the soil due to the sufficient amount of nutrients supplied to the soil and the minimized impact of chemical plant protection products on the soil. The multiple irrigation of crops resulting from indications of soil moisture sensors mounted on plots (indicating the real need for irrigation) contributed to the improvement of almost all analyzed soil quality indices. Multiple irrigation generated high costs, but in combination with fertilization and chemical crop protection (conventional and integrated system), it influenced the high productivity of spring wheat and compensated for the incurred costs (the greatest profit). Full article

The co-rearing model for young silkworms (Bombyx mori) utilizing artificial feed is currently undergoing significant promotion within the sericulture industry in China. Within this model, accurately counting the number of young silkworms serves as a crucial foundation for achieving precision rearing and high-quality breeding. Currently, manual counting remains the prevalent method for enumerating young silkworms, yet it is highly subjective. A dataset of young silkworm bodies has been constructed, and the Young Silkworm Counting (YSC) method has been proposed. This method combines an improved detector, incorporating an optimized multi-scale feature fusion module and the Efficient Multi-Scale Attention Fusion Cross Stage Partial (EMA-CSP) mechanism, with an optimized tracker (based on ByteTrack with improved detection box matching), alongside the implementation of a ‘detection line’ approach. The experimental results demonstrate that the recall, precision, and average precision (AP_50:95) of the improved detection algorithm are 87.9%, 91.3% and 72.7%, respectively. Additionally, the enhanced ByteTrack method attains a multiple-object tracking accuracy (MOTA) of 88.3%, an IDF1 of 90.2%, and a higher-order tracking accuracy (HOTA) of 78.1%. Experimental validation demonstrates a counting accuracy exceeding 90%. The present study achieves precise counting of young silkworms in complex environments through an improved detection-tracking method combined with a detection line approach. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article

Due to the limitations of the sowing machine performance and rice seed germination rates, missing seedlings inevitably occur after rice is sown in large fields. This phenomenon has a direct impact on the rice yield. In the field environment, the existing methods for detecting missing seedlings based on unmanned aerial vehicle (UAV) remote sensing images often have unsatisfactory effects. Therefore, to enable the fast and accurate detection of missing rice seedlings and facilitate subsequent reseeding, this study proposes a UAV remote-sensing-based method for detecting missing rice seedlings in large fields. The proposed method uses an improved PCERT-DETR model to detect rice seedlings and missing seedlings in UAV remote sensing images of large fields. The experimental results show that PCERT-DETR achieves an optimal performance on the self-constructed dataset, with an mean average precision (mAP) of 81.2%, precision (P) of 82.8%, recall (R) of 78.3%, and F₁-score (F₁) of 80.5%. The model’s parameter count is only 21.4 M and its FLOPs reach 66.6 G, meeting real-time detection requirements. Compared to the baseline network models, PCERT-DETR improves the P, R, F₁, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively. Furthermore, the performance evaluation experiments were carried out through ablation experiments, comparative detection model experiments and heat map visualization analysis, indicating that the model has a strong detection performance on the test set. The results confirm that the proposed model can accurately detect the number of missing rice seedlings. This study provides accurate information on the number of missing seedlings for subsequent reseeding operations, thus contributing to the improvement of precision farming practices. Full article

Background: The yearly number of left ventricular assist device (LVAD) implantations is approximately twice the number of heart transplantations (HTX) in Germany. As the number of patients with an LVAD installed grows, so does the likelihood of their presentation to the emergency room (ER). Due to uneasiness in identifying their primary complaint, ER personnel are often likely to overlook important clues in the treatment of patients with an LVAD. Methods: To assess the urgency of patients’ conditions and their relationship with LVADs, we retrospectively examined the ER admissions of patients with HeartMate 3^TM (HM 3) LVADs installed between 2014 and 2024 at our university medical center. We counted referrals to the peripheral ward (minor) and to the intensive care unit (ICU, major). Relation to LVAD relation was also recorded. The survival was analyzed with respect to the severity of the cause of admission (minor/major) and the relationship to the LVAD therapy. Results: We observed 100 presentations to the emergency department. Of these, 77 were minor and 23 were major. The majority (92) was not related to the LVAD. Of the eight admissions related to the LVAD, two were major adverse events, accounting only for 2% of the total cases. Conclusions: An ER presentation of a patient with an HM 3 is very likely to have a medical cause not related to the LVAD. LVAD-related causes were mostly minor and could be treated on the ward. Full article

Accurate seismic performance assessment of flexure-shear critical reinforced concrete (RC) columns necessitates precise hysteresis modeling that captures their distinct cyclic characteristics—particularly pronounced strength degradation, stiffness deterioration, and pinching effects. However, existing hysteresis models for such circular RC columns fail to comprehensively characterize these coupled cyclic degradation mechanisms under repeated loading. This study develops a novel hysteresis model explicitly incorporating three key mechanisms: (1) directionally asymmetric strength degradation weighted by hysteretic energy, (2) cycle-dependent pinching governed by damage accumulation paths, and (3) amplitude-driven stiffness degradation decoupled from cycle count, calibrated and validated using 14 column tests from the Pacific Earthquake Engineering Research Center (PEER) structural performance database. Key findings reveal that significant strength degradation primarily manifests during initial loading cycles but subsequently stabilizes. Unloading stiffness degradation demonstrates negligible dependency on cycle number. Pinching effects progressively intensify with cyclic advancement. The model provides a physically rigorous framework for simulating seismic deterioration, significantly improving flexure-shear failure prediction accuracy, while parametric analysis confirms its potential adaptability beyond tested scenarios. However, applicability remains confined to specific parameter ranges with reliability decreasing near boundaries due to sparse data. Deliberate database expansion for edge cases is essential for broader generalization. Full article

Purpose: Previous studies have reported that blood-based inflammatory markers are associated with prognosis in patients with various solid tumors, including colorectal cancer (CRC). The pan-immune inflammation value (PIV) is a novel prognostic biomarker based on blood count. Here, we aimed to study the association between PIV and survival following transarterial radioembolization (TARE) in patients with liver-dominant metastatic colorectal cancer (CLM). Methods: A total of 49 patients with CLM who underwent TARE at the Ankara University Department of Medical Oncology were retrospectively analyzed. The relationship between clinical and laboratory parameters with post-TARE overall survival (OS) was analyzed by multivariate analyses. Results: The median age was 60 years and 71.4% of patients had received at least two lines of chemotherapy. The objective response rate (ORR) was 59.1% following TARE. Patients with hepatic response after TARE treatment demonstrated significantly longer survival compared to non-responders (p: 0.033). The optimal PIV threshold value was calculated as 629 in ROC analyses. This PIV value had 81% sensitivity and 80% specificity for OS prediction (AUC 0.86; 95% CI: 0.75–0.98, p = 0.008). Patients with elevated PIV > 629 had significantly shorter OS (p = 0.002). In the multivariate analysis, adjusted for ECOG PS, TARE response, presence of extrahepatic disease, number of chemotherapy lines, CEA levels and post-TARE NLR and PIV, only low PIV level was associated with longer OS (>629 vs. ≤629; HR: 4.87; 95% CI: 1.32–17.92; p = 0.017). Conclusions: PIV, a blood-based inflammatory score, may reflect the host’s immune response following TARE and serve as a novel predictor of survival. Full article