Search Results (10,592)

Currently, research on the hydrodynamic characteristics of artificial reef deployment still faces challenges such as insufficient environmental coupling, but accurate simulation of the deployment process holds significant engineering importance for optimizing deployment efficiency and ensuring reef stability. This study employs the Smoothed Particle Hydrodynamics (SPH) method to establish a 3D numerical model, focusing on the influence of key parameters—inflow velocity and water entry angle—on the hydrodynamic characteristics of cubic artificial reef deployment. The results indicate that under flow velocities of 0.4–0.5 m/s, pressure fluctuations are relatively minor, with peak pressure gradients below 15 kPa/m, exhibiting a gradual trend, while particle concentration remains high, and drag gradually increases. At flow velocities of 0.6–0.8 m/s, the maximum pressure at the bottom reaches up to 35 kPa, with low-pressure areas at the tail dropping to −10 kPa; particle concentration decreases compared to conditions at 0.4–0.5 m/s; settling time extends from 8.4 s to 12 s, representing a 42% increase. Under different water entry angles, drag varies nonlinearly with the angle, reaching its maximum at 20° and its minimum at 25°, with a reduction of approximately 47% compared to the maximum. The anti-sliding safety factor and anti-overturning safety factor are used to assess the stability of the cubic reef placed on the seabed. Across different inflow velocities, the anti-sliding safety factor of the cubic artificial reef significantly exceeds 1.2, whereas the anti-overturning safety factor is below 1.2 at 0.4 m/s but exceeds 1.2 at velocities of 0.5 m/s and above, indicating that the reef maintains stability under the majority of these flow conditions. Our findings provide a scientific basis for the deployment process, site selection, and geometric design of cubic artificial reefs, offering valuable insights for the precise deployment and structural optimization of artificial reefs in marine ranching construction. Full article

Marine species photo-identification and location for tracking are crucial for understanding the characteristics and patterns that distinguish each marine species. However, challenges in camera data acquisition and the unpredictability of animal movements have restricted progress in this field. To address these challenges, we present a novel algorithm for the first stage of marine species photo-identification and location methods. For marine species photo-identification applications, a color index-based thresholding segmentation method is proposed. This method is based on the characteristics of the GMR (Green Minus Red) color index and the proposed empirical BMG (Blue Minus Green) color index. These color indexes are modified to provide better information about the color of regions, such as marine animals, the sky, and land found in the scientific sightings images, allowing an optimal thresholding segmentation method. In the case of marine species location, a SURFs (Speeded-Up Robust Features)-based supervised classifier is used to obtain the location of the marine animal in the sighting image; with this, its tracking could be obtained. The tests were performed with the Kaggle happywhale public database; the results obtained in precision shown range from 0.77 up to 0.98 using the proposed indexes. Finally, the proposed method could be used in real-time marine species tracking with a processing time of 0.33 s for images of 645 × 376 pixels using a standard PC. Full article

Micro-RNAs (miRNAs) are short, non-coding RNA molecules regulating genes’ expression. Studies published over last years demonstrated that they play an important role in allergic diseases by regulating humoral and cellular immunity, cytokine secretion and epithelium function. Some of them seem potential non-invasive biomarkers facilitating diagnosis of the most common allergic diseases, such as allergic rhinitis (miR-21, miR-126, miR-142-3p, miR-181a, miR-221), asthma (miR-16, miR-21, miR-126, miR-146a, miR-148a, miR-221, miR-223) and atopic dermatitis (miR-24, miR-124, miR-155, miR-191, miR-223, miR-483-5p), or objectively assessing severity of inflammation and endotype of the disease. In spite of the large body of literature available, its scientific value is limited due to the small numbers of study participants, heterogeneity of populations enrolled, and diverse methodology. Some studies have revealed significant changes in miRNAs’ profile in the course of allergen immunotherapy. Tolerance induction is associated with processes controlled by miRNAs: enhanced activity of Treg cells and increased production of tolerogenic IL-10 and TGF-β. Thus, miRNAs may be candidates as biomarkers of successful immunotherapy. Finally, they are also possible therapeutic agents or targets of therapies based on antagomirs blocking their activity. However, so far no studies are available that demonstrate efficacy in overcoming delivery barriers, tissue targeting or drugs’ safety. As a consequence, despite promising results of in vitro and animal model studies, translation into human therapeutic agents is uncertain. Full article

With the increasing requirements for “Green Mine” construction, Cemented Tailings Backfill (CTB) has emerged as the preferred strategy for solid waste management and ground pressure control in underground metal mines. However, full tailings, characterized by wide particle size distribution and high fine-grained content, exhibit complex physicochemical properties that lead to significant non-linear behavior in slurry rheology and strength evolution, posing challenges for accurate prediction using traditional empirical formulas. Addressing the issues of significant strength fluctuations and difficulties in mix proportion optimization in a specific lead–zinc mine, this study systematically conducted physicochemical characterizations, slurry sedimentation and transport performance evaluations, and mechanical strength tests. Through multi-factor coupling experiments, the synergistic effects of cement type, cement-to-tailings (c/t) ratio, slurry concentration, and curing age on backfill performance were elucidated. Quantitative results indicate that solids mass concentration is the critical factor determining transportability. Concentrations exceeding 68% effectively mitigate segregation and stratification during the filling process while maintaining optimal fluidity. Regarding mechanical properties, the c/t ratio and concentration show a significant positive correlation with Uniaxial Compressive Strength (UCS). For instance, with a 74% concentration and 1:4 c/t ratio, the 3-day strength increased by 1.4 times compared to the 68% concentration, with this increment expanding to 2.0 times by 28 days. Furthermore, a comparative analysis of four cement types revealed that 42.5# cement offers superior techno-economic indicators in terms of reducing binder consumption and enhancing early-age strength. This research not only establishes an optimized mix proportion scheme tailored to the operational requirements of the lead–zinc mine but also provides a quantitative scientific basis and theoretical framework for the material design and safe production of CTB systems incorporating high fine-grained full tailings. Full article

Monitoring stem sap flow is essential for understanding plant water-use strategies and eco-physiological processes in the ecologically fragile karst region. In the study, we continuously monitored four co-occurring species—Cryptomeria japonica var. sinensis (LS), Liquidambar formosana (FX), Camptotheca acuminata (XS), and Melia azedarach (KL)—using the thermal dissipation probe method in a karst farmland-to-forest restoration area. We analyzed diurnal and nocturnal sap flow variations across different growth periods and their responses to environmental factors at an hourly scale. The results showed (1) A “high daytime, low nighttime” sap flow pattern during the growing season for all species. (2) The proportion of nocturnal sap flow was significantly lower in the growing than in the non-growing season. (3) Daytime sap flow was primarily driven by photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) during the growing season. In the non-growing season, daytime drivers were species-specific: relative humidity (RH, 39.39%) for LS; air temperature (Ta, 23.14%) for FX; PAR (33.03%) for XS; and soil moisture at a 10 cm depth (SM1, 25.2%) for KL. Nocturnal flow was governed by VPD and RH during the growing season versus soil moisture (SM1 and SM2) and RH in the non-growing season. These findings reveal interspecific differences in water-use strategies and provide a scientific basis for species selection and afforestation management in the karst ecological restoration of this research area. Full article

Background: The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. Although not as rapidly as in the past, MW has steadily evolved, updating its mwTab and JSON deposition text file formats and its web-based infrastructure. However, the growth of MW has been exponential since its inception in 2013 and continues to be exponential, with the number of datasets hosted on the repository increasing by 50% since April 2024. As part of regular maintenance to keep up with changes to the mwTab file format and an earnest effort to use MW datasets in meta-analyses, the mwtab Python package has been updated. Methods: Updates include better error handling for batch processing, better parsing to read more files without error, and extensive improvements to the validation capabilities of the package. These updates also required our mwFileStatusWebsite to be updated and improved. Results: We used the enhanced validation features of the mwtab package to evaluate all available datasets in MW to facilitate improved curation, FAIRness of the repository, and reuse for meta-analyses. Conclusions: Version 2.0.0 of the mwtab Python package is now officially released and freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license, with documentation available on GitHub. The updated mwFileStatusWebsite is also officially in its 2.0.0 version. Full article

The sliding window method is a commonly used data processing in time series forecasting tasks, and determining the appropriate window size is a crucial step in constructing predictive models. However, the current setting of window size parameters is often based on empirical knowledge, making the scientific determination of the optimal sliding window size highly significant. This paper proposes an algorithm for optimizing window size based on sample entropy, which is applicable not only to the original undecomposed sequences but also effectively to the decomposed sequences. The proposed algorithm has been validated using the open-source Elia grid data across multiple model architectures, including recurrent (GRU/LSTM) and attention-based (Transformer) networks. Experimental results demonstrate that the algorithm effectively determines an optimal window size of 106. The optimized window consistently leads to superior prediction performance, with the CEEMD-GRU model achieving a MAPE of 0.256, RMSE of 22.529, and MAE of 18.186—representing reductions of over 5% compared to the undecomposed benchmark. Furthermore, the enhancement is more significant for decomposed sequences, and the algorithm’s efficacy is validated across different neural network architectures (e.g., LSTM, GRU, Transformer), confirming its practical utility and generalizability. Full article

As a complex and destructive natural disaster, the characteristics of typhoons are closely related to human activities, and their accurate categorization is of vital significance for improving disaster warning and management capabilities. This study highlights the key role of typhoon clustering in analyzing typhoon behaviors, aiming to provide reliable support for disaster prevention and control. Based on the NOAA meteorological dataset from 2003 to 2024, this study firstly adopts the K-means clustering algorithm to classify typhoons into seven categories and then utilizes eight machine learning models to train and validate the classification results, and introduces the Shapley’s additive interpretation (SHAP) algorithm to enhance the interpretability of the models. The study data covers a variety of features such as air temperature, wind speed, atmospheric pressure, and weather station observations, etc. After a systematic preprocessing process, a feature matrix containing key variables such as typhoon intensity and moving speed is constructed. The results show that the XGBoost model outperforms others across multiple evaluation metrics (Accuracy: 0.992, Precision: 0.989, Recall: 0.992, F1.5 Score: 0.990), highlighting its exceptional capability in managing complex weather classification tasks. The seven categories of typhoon types classified by K-means exhibit different feature patterns, while the SHAP analysis further reveals the effects of each feature on the classification and its potential interactions. This study not only verifies the effectiveness of K-means combined with machine learning in typhoon classification but also lays a solid scientific foundation for accurate prediction, risk assessment and optimization of management strategies for typhoon disasters through the in-depth analysis of feature impacts. Full article

Background: The objective of this work was to support the implementation of the European Health Technology Assessment Regulation (EU HTAR) and optimize performance of the evolving EU HTA system. Therefore, an inclusive multi-stakeholder framework of key performance indicators (KPI) for success measurement was developed. Methods: A modified Delphi-procedure was applied as follows: (1) development of a generic KPI pool at the Fall Convention 2024 of the European Access Academy (EAA); (2) review of initial pool and identification of additional KPIs; (3) development of prioritized KPIs covering patient, clinician, Health Technology Developer (HTD), and System/Member State (MS) perspectives, and (4) consolidation of the stakeholder-centric KPIs after EAA’s Spring Convention 2025. Results: Steps 1 and 2 of the Delphi procedure revealed 14 generic KPI domains. Steps 3 and 4 resulted in four prioritized KPIs for patients (patient input; utilization of patient-centric outcome measures; time to access; equity); six for clinicians (population/intervention/comparator/outcomes (PICO); addressing uncertainty; clinician involvement; transparency; equity and time to access); four for HTDs (PICO; joint scientific consultation (JSC) process; joint clinical assessment (JCA) process; time to national decision making); five from a system/MS perspective (PICO; learning and training the health system; reducing duplication; equity and time to access). The scope of, e.g., the PICO-related KPI, differed between stakeholder groups. Also, several KPIs intentionally reached beyond the remit of EU HTA as they are also dependent on MS-specific factors including national health systems and budgets. Discussion and Conclusions: The KPI framework developed here presents a step towards the generation of systematic multi-stakeholder evidence to support a successful implementation of the EU HTAR. The relevance of the identified stakeholder-centric KPIs is confirmed by their alignment with the Health System Goals suggested in the context of “Performance measurement for health improvement” by the World Health Organisation. Implementation of the framework, i.e., measurement of KPIs, is envisioned to provide evidence to inform the 2028 revision of the EU HTAR. Full article

Midline shift (MLS) is one of the conditions that strongly affects mortality and prognosis in critical neurological emergencies such as traumatic brain injury (TBI). Especially, MLS over 5 mm requires urgent diagnosis and treatment. Despite widespread tomography imaging capabilities, the lack of radiologists capable of interpreting the images causes delays in the diagnosis process. Therefore, there is a need for AI-supported diagnostic systems specifically tailored to the field for MLS detection. However, the lack of open, disorder-specific datasets in the literature has limited research in the field and hindered the ability to make comparisons against a reliable reference point. Therefore, the current state of deep learning (DL) methods in the field is not sufficiently addressed. Within the scope of this study, a DL architecture is proposed for MLS detection as a classification task, with millimeter-scale MLS measurements used for evaluation and stratified analysis. This process also comprehensively addresses the status of MLS detection in contemporary DL architecture. Furthermore, to address the lack of open datasets in the literature, two publicly available datasets originally collected with a primary focus on TBI have been annotated for MLS detection. The proposed model was tested on two different open datasets and achieved mean sensitivity values of 0.9467–0.9600 for the Radiological Society of North America (RSNA) dataset and 0.8623–0.8984 for the CQ500 dataset in detecting MLS presence above 5 mm across two different scenarios. It achieved a mean Area Under the Curve-Receiver Operating Characteristic (AUC-ROC) value of 0.9219–0.9816 for the RSNA dataset and 0.9443–0.9690 for the CQ500 dataset. The aim of the study is to detect not only emergency cases but also small MLSs independent of quantity for patient follow-up, so the overall performance of the proposed model (MLS present/absent) was calculated without an MLS quantity threshold. Mean F1 Score values of 0.7403 for the RSNA dataset and 0.7271 for the CQ500 dataset were obtained, along with mean AUC-ROC values of 0.8941 for the RSNA dataset and 0.9301 for the CQ500 dataset. The study presents a clinically applicable, optimized, fast, reliable, up-to-date, and successful DL solution for the rapid diagnosis of MLS, intervention in emergencies, and monitoring of small MLS. It also contributes to the literature by enabling a high level of reproducibility in the scientific community with labeled open data. Full article

Functional food plays an increasingly important role in shaping healthy dietary habits by integrating health-promoting components into consumers’ everyday diets and helping reduce food waste. Products developed using by-products and raw material streams from food processing can support the principles of the circular economy (CE) by improving resource efficiency and lowering the environmental footprint of the food system. In Poland, as in many countries worldwide, functional food is a rapidly growing category that can contribute to public health and serve as a driver of economic development. However, the excessive and scientifically unsupported use of the term “functional food” for marketing purposes may mislead consumers and weaken trust in this product group. From a CE perspective, transparent communication and informed consumer choices are essential, as they promote environmentally responsible behaviors and support sustainable production models. The aim of this study was to assess the level of knowledge about functional food among residents of Eastern Poland, which is a less industrialized and predominantly agricultural region. The analysis focused on awareness of health benefits, consumption frequency, consumer attitudes, and interest in educational initiatives promoting functional food within CE principles. The study also examined whether consumers associate functional food with sustainability-oriented practices. The findings offer insights for educational and marketing strategies grounded in scientific evidence and highlight the potential of functional food in building a sustainable, resource-efficient food system. Full article

Solanum betaceum (tamarillo) is Andean fruit rich in secondary metabolites with increasing relevance in food, nutraceutical, and biotechnological research. Despite growing scientific interest, the available evidence remains fragmented and methodologically heterogeneous. This systematic review consolidates and critically analyzes recent studies on the bioactive composition of S. betaceum, the effects of conventional and emerging processing technologies, and the functional activities reported for fresh fruits, by-products, and processed matrices. A comprehensive search of Lens.org, Scopus, and PubMed was conducted following PRISMA 2020 guidelines. From 1049 records identified, 65 studies published between 2020 and 2025 met the inclusion criteria and were included in the qualitative synthesis. The literature reveals substantial variability in polyphenols, anthocyanins, carotenoids, vitamin C, and other metabolites, driven by cultivar, maturity stage, edaphoclimatic conditions, and analytical approaches. Emerging technologies such as ultrasound-assisted extraction, high-pressure homogenization, and spray drying generally improved the recovery and stability of bioactive compounds, whereas intensive thermal treatments were associated with degradation of thermolabile constituents. Functional evidence supports antioxidant, antimicrobial, metabolic modulatory, and cytotoxic activities; however, interpretation is limited by inconsistent reporting practices, limited bioaccessibility assessment, and the predominance of in vitro models. Overall, S. betaceum shows considerable functional and technological potential, but further standardized methodologies, mechanistic studies, and human-relevant models are required to support translational and industrial validation. Full article

Background: Wound healing contributes to restoring skin integrity. However, scars affect soft tissue in all its layers, including the superficial and deep fascia; moreover, it has been demonstrated that the fibroblasts leading the scarring process develop from progenitors located in the superficial fascia. In the past, research into scar etiology has focused primarily on the dermal and epidermal layers, leaving the role of the fasciae largely overlooked. Many patients presenting with surgical or traumatic scars complain of the increased stiffness and thickness of the scar, reduced extensibility of the area surrounding it, and chronic pain persisting even after the healing process has been completed. The purpose of this systematic review is to investigate the non-invasive tools and methods employed for the objective evaluation of scars that involve fascial layers. Methods: A systematic literature search was conducted on PubMed and WOS. Registration DOI: 10.17605/OSF.IO/SDR3Q. Results: A total of 11 articles were selected; the etiologies of scars were surgical, traumatic, and other (keloids). The investigations were conducted using ultrasound, magnetic resonance imaging, strain elastography, and shear wave elastography on the visceral fasciae, superficial fascia, hypodermis, and musculoskeletal fasciae. Sliding of fasciae was assessed by ultrasound; thickness of fasciae was assessed by ultrasound and magnetic resonance imaging; stiffness was assessed by shear wave elastography and strain elastography; and the qualitative assessment was performed via ultrasound. Conclusions: Our literature review showed that ultrasound, magnetic resonance imaging, strain elastography, and shear wave elastography are currently adopted for investigating the sliding, thickness, stiffness, and qualitative features of scars involving fascial layers. Moreover, our research showed the existence of a gap in the scientific literature on this topic. Full article

Background: Nutritional status assessment is the cornerstone of the Nutrition Care Process, guiding diagnosis, intervention, and monitoring. The classical ABCD model (Anthropometry, Biochemical, Clinical, Dietary) has been widely applied; however, it presents limitations in addressing current nutritional and epidemiological challenges. Objective: This narrative review aims to synthesize and update the scientific evidence on the expanded nutritional assessment model, known as ABCDEFG, which incorporates the Ecological–microbiota (E), Functional (F), and Genomic–nutrigenomic (G) approaches. Methods: A narrative review of the literature was conducted through PubMed, Scopus, and Web of Science, covering publications from 2013 to 2025. Articles were selected based on relevance to at least one of the seven assessment domains. Findings were synthesized descriptively and critically, highlighting applications, strengths, and limitations. Results: The ABCDEFG framework offers a multidimensional perspective of nutritional assessment. While anthropometric, biochemical, clinical, and dietary methods remain essential, the inclusion of ecological dimensions (gut microbiota, environmental influences), functional measures (e.g., muscle strength, physical performance), and genomics enables a more sensitive and personalized evaluation. This integrative approach supports better clinical decision-making and research innovation in nutrition and health sciences. Conclusions: The seven-method model broadens the scope of nutritional assessment, bridging traditional and emerging tools. Its application enhances the capacity to identify nutritional risks, design targeted interventions, and advance precision nutrition. Full article

Climate change has exacerbated flood risks for urban infrastructure, rendering sewage treatment facilities (STFs) particularly vulnerable due to their typical low-lying topographic placement. However, conventional flood risk assessment methodologies often rely solely on physical hazard parameters such as inundation depth, neglecting the functional interdependencies and operational criticality of individual treatment units. To address this limitation, this study proposes the Integrated Hydro-Operational Risk Assessment (IHORA) framework. The IHORA framework synthesizes 2D hydrodynamic modeling with a modified Hazard and Operability Study(HAZOP) study to systematically identify unit-specific physical failure thresholds and employs the Analytic Hierarchy Process (AHP) to quantify the relative operational importance of each process based on expert elicitation. The framework was applied to an underground STF under both fluvial flooding and internal structural breach scenarios. The results revealed a significant risk misalignment in traditional assessments; vital assets like electrical facilities were identified as high-risk hotspots despite moderate physical exposure, due to their high operational weight. Furthermore, Cause–Consequence Analysis (CCA) was utilized to trace cascading failure modes, bridging the gap between static risk metrics and dynamic emergency response protocols. This study demonstrates that the IHORA framework provides a robust scientific basis for prioritizing mitigation resources and enhancing the operational resilience of environmental facilities. Full article