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In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on the research status of non-destructive detection techniques used for plant disease identification and detection, mainly introducing the following two types of methods: spectral technology and imaging technology. It also elaborates, in detail, on the principles and application examples of each technology and summarizes the advantages and disadvantages of these technologies. This review clearly indicates that non-destructive detection techniques can achieve plant disease and pest detection quickly, accurately, and without damage. In the future, integrating multiple non-destructive detection technologies, developing portable detection devices, and combining more efficient data processing methods will become the core development directions of this field. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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21 pages, 262 KiB

Open AccessArticle

Sustainability in Boreal Forests: Does Elevated CO₂ Increase Wood Volume?

by Nyonho Oh, Eric C. Davis and Brent Sohngen

Sustainability 2025, 17(15), 7017; https://doi.org/10.3390/su17157017 (registering DOI) - 1 Aug 2025

Abstract

While boreal forests constitute 30% of the Earth’s forested area and are responsible for 20% of the global carbon sink, there is considerable concern about their sustainability. This paper focuses on the role of elevated CO₂, examining whether wood volume in these forests has responded to increased CO₂ over the last 60 years. To accomplish this, we use a rich set of wood volume measurement data from the Province of Alberta, Canada, and deploy quasi-experimental techniques to determine the effect of elevated CO₂. While the few experimental studies that have examined boreal forests have found almost no effect of elevated CO₂, our results indicate that a 1.0% increase in lifetime exposure to CO₂ leads to a 1.1% increase in aboveground wood volume in these boreal forests. This study showcases the value of research designs that use natural settings to better account for the effects of prolonged exposure to elevated CO₂. Our results should enable improved delineation of the drivers of historical changes in wood volume and carbon storage in boreal forests. In addition, when combined with other studies, these results will likely aid policymakers in designing management or policy approaches that will enhance the sustainability of forests in boreal regions. Full article

(This article belongs to the Section Air, Climate Change and Sustainability)

25 pages, 1138 KiB

Open AccessArticle

Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information

by Fei Chen and Wenchi Zhou

Electronics 2025, 14(15), 3092; https://doi.org/10.3390/electronics14153092 (registering DOI) - 1 Aug 2025

Abstract

In order to increase the effectiveness of model training, data reduction is essential to data-centric Artificial Intelligence (AI). It achieves this by locating the most instructive examples in massive datasets. To increase data quality and training efficiency, the main difficulty is choosing the best examples rather than the complete datasets. In this paper, we propose an effective data reduction strategy based on Pointwise 𝒱-Information (PVI). To enable a static method, we first use PVI to quantify instance difficulty and remove instances with low difficulty. Experiments show that classifier performance is maintained with only a 0.0001% to 0.76% decline in accuracy when 10–30% of the data is removed. Second, we train the classifiers using a progressive learning strategy on examples sorted by increasing PVI, accelerating convergence and achieving a 0.8% accuracy gain over conventional training. Our findings imply that training a classifier on the chosen optimal subset may improve model performance and increase training efficiency when combined with an efficient data reduction strategy. Furthermore, we have adapted the PVI framework, which was previously limited to English datasets, to a variety of Chinese Natural Language Processing (NLP) tasks and base models, yielding insightful results for faster training and cross-lingual data reduction. Full article

(This article belongs to the Special Issue Data Retrieval and Data Mining)

31 pages, 1370 KiB

Open AccessArticle

AIM-Net: A Resource-Efficient Self-Supervised Learning Model for Automated Red Spider Mite Severity Classification in Tea Cultivation

by Malathi Kanagarajan, Mohanasundaram Natarajan, Santhosh Rajendran, Parthasarathy Velusamy, Saravana Kumar Ganesan, Manikandan Bose, Ranjithkumar Sakthivel and Baskaran Stephen Inbaraj

AgriEngineering 2025, 7(8), 247; https://doi.org/10.3390/agriengineering7080247 (registering DOI) - 1 Aug 2025

Abstract

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

16 pages, 3713 KiB

Open AccessArticle

Synergistic Alleviation of Saline–Alkali Stress and Enhancement of Selenium Nutrition in Rice by ACC (1-Aminocyclopropane-1-Carboxylate) Deaminase-Producing Serratia liquefaciens and Biogenically Synthesized Nano-Selenium

by Nina Zhu, Xinpei Wei, Xingye Pan, Benkang Xie, Shuquan Xin and Kai Song

Plants 2025, 14(15), 2376; https://doi.org/10.3390/plants14152376 (registering DOI) - 1 Aug 2025

Abstract

Soil salinization and selenium (Se) deficiency threaten global food security. This study developed a composite bioinoculant combining ACC deaminase-producing Serratia liquefaciens and biogenically synthesized nano-selenium (SeNPs) to alleviate saline–alkali stress and enhance Se nutrition in rice (Oryza sativa L.). A strain of S. liquefaciens with high ACC deaminase activity was isolated and used to biosynthesize SeNPs with stable physicochemical properties. Pot experiments showed that application of the composite inoculant (S3: S. liquefaciens + 40 mmol/L SeNPs) significantly improved seedling biomass (fresh weight +53.8%, dry weight +60.6%), plant height (+31.6%), and root activity under saline–alkali conditions. S3 treatment also enhanced panicle weight, seed-setting rate, and grain Se content (234.13 μg/kg), meeting national Se-enriched rice standards. Moreover, it increased rhizosphere soil N, P, and K availability and improved microbial α-diversity. This is the first comprehensive demonstration that a synergistic bioformulation of ACC deaminase PGPR and biogenic SeNPs effectively mitigates saline–alkali stress, enhances soil fertility, and enables safe Se biofortification in rice. Full article

(This article belongs to the Special Issue Nanomaterials in Plant Growth and Stress Adaptation—2nd Edition)

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19 pages, 3765 KiB

Open AccessArticle

Mathematical Study of Pulsatile Blood Flow in the Uterine and Umbilical Arteries During Pregnancy

by Anastasios Felias, Charikleia Skentou, Minas Paschopoulos, Petros Tzimas, Anastasia Vatopoulou, Fani Gkrozou and Michail Xenos

Fluids 2025, 10(8), 203; https://doi.org/10.3390/fluids10080203 (registering DOI) - 1 Aug 2025

Abstract

This study applies Computational Fluid Dynamics (CFD) and mathematical modeling to examine uterine and umbilical arterial blood flow during pregnancy, providing a more detailed understanding of hemodynamic changes across gestation. Statistical analysis of Doppler ultrasound data from a large cohort of more than [...] Read more.

P S V

) between the 22nd and 30th weeks, while uterine artery velocities remain relatively stable, suggesting adaptations in vascular resistance during pregnancy. By combining the Navier–Stokes equations with Doppler ultrasound-derived inlet velocity profiles, we quantify several key fluid dynamics parameters, including time-averaged wall shear stress (

T A W S S

), oscillatory shear index (

O S I

), relative residence time (

R R T

), Reynolds number (

R e

), and Dean number (

D e

), evaluating laminar flow stability in the uterine artery and secondary flow patterns in the umbilical artery. Since blood exhibits shear-dependent viscosity and complex rheological behavior, modeling it as a non-Newtonian fluid is essential to accurately capture pulsatile flow dynamics and wall shear stresses in these vessels. Unlike conventional imaging techniques, CFD offers enhanced visualization of blood flow characteristics such as streamlines, velocity distributions, and instantaneous particle motion, providing insights that are not easily captured by Doppler ultrasound alone. Specifically, CFD reveals secondary flow patterns in the umbilical artery, which interact with the primary flow, a phenomenon that is challenging to observe with ultrasound. These findings refine existing hemodynamic models, provide population-specific reference values for clinical assessments, and improve our understanding of the relationship between umbilical arterial flow dynamics and fetal growth restriction, with important implications for maternal and fetal health monitoring. Full article

(This article belongs to the Special Issue Advances in Computational Mechanics of Non-Newtonian Fluids, 2nd Edition)

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26 pages, 1886 KiB

Open AccessArticle

Landscape Ecological Risk Assessment of Peri-Urban Villages in the Yangtze River Delta Based on Ecosystem Service Values

by Yao Xiong, Yueling Li and Yunfeng Yang

Sustainability 2025, 17(15), 7014; https://doi.org/10.3390/su17157014 (registering DOI) - 1 Aug 2025

Abstract

The rapid urbanization process has accelerated the degradation of ecosystem services (ESs) in peri-urban rural areas of the Yangtze River Delta (YRD), leading to increasing landscape ecological risks (LERs). Establishing a scientifically grounded landscape ecological risk assessment (LERA) system and corresponding control strategies is therefore imperative. Using rural areas of Jiangning District, Nanjing as a case study, this research proposes an optimized dual-dimensional coupling assessment framework that integrates ecosystem service value (ESV) and ecological risk probability. The spatiotemporal evolution of LER in 2000, 2010, and 2020 and its key driving factors were further studied by using spatial autocorrelation analysis and geodetector methods. The results show the following: (1) From 2000 to 2020, cultivated land remained dominant, but its proportion decreased by 10.87%, while construction land increased by 26.52%, with minimal changes in other land use types. (2) The total ESV increased by CNY 1.67 × 10⁹, with regulating services accounting for over 82%, among which water bodies contributed the most. (3) LER showed an overall increasing trend, with medium- to highest-risk areas expanding by 55.37%, lowest-risk areas increasing by 10.10%, and lower-risk areas decreasing by 65.48%. (4) Key driving factors include landscape vulnerability, vegetation coverage, and ecological land connectivity, with the influence of distance to road becoming increasingly significant. This study reveals the spatiotemporal evolution characteristics of LER in typical peri-urban villages. Based on the LERA results, combined with terrain features and ecological pressure intensity, the study area was divided into three ecological management zones: ecological conservation, ecological restoration, and ecological enhancement. Corresponding zoning strategies were proposed to guide rural ecological governance and support regional sustainable development. Full article

(This article belongs to the Special Issue Impacts of Human Activities on Ecosystem Health and Landscape Sustainability)

11 pages, 3000 KiB

Open AccessArticle

Comparative Study of the Bulk and Foil Zinc Anodic Behavior Kinetics in Oxalic Acid Aqueous Solutions

by Vanya Lilova, Emil Lilov, Stephan Kozhukharov, Georgi Avdeev and Christian Girginov

Materials 2025, 18(15), 3635; https://doi.org/10.3390/ma18153635 (registering DOI) - 1 Aug 2025

Abstract

The anodic behavior of zinc electrodes is important for energy storage, corrosion protection, electrochemical processing, and other practical applications. This study investigates the anodic galvanostatic polarization of zinc foil and bulk electrodes in aqueous oxalic acid solutions, revealing significant differences in their electrochemical behavior, particularly in induction period durations. The induction period’s duration depended on electrolyte concentration, current density, and temperature. Notably, the temperature dependence of the kinetics exhibited contrasting trends: the induction period for foil electrodes increased with temperature, while that of bulk electrodes decreased. Chemical analysis and polishing treatment comparisons showed no significant differences between the foil and bulk electrodes. However, Scanning Electron Microscopy (SEM) observations of samples anodized at different temperatures, combined with Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) analysis of dissolved electrode material, provided insights into the distinct anodic behaviors. X-ray Diffraction (XRD) studies further confirmed these findings, revealing a crystallographic orientation dependence of the anodic behavior. These results provide detailed information about the electrochemical properties of zinc electrodes, with implications for optimizing their performance in various applications. Full article

(This article belongs to the Special Issue New Generation Materials for Advanced Electronic and Biomedical Applications)

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14 pages, 3725 KiB

Open AccessArticle

Gut Hormones and Postprandial Metabolic Effects of Isomaltulose vs. Saccharose Consumption in People with Metabolic Syndrome

by Jiudan Zhang, Dominik Sonnenburg, Stefan Kabisch, Stephan Theis, Margrit Kemper, Olga Pivovarova-Ramich, Domenico Tricò, Sascha Rohn and Andreas F. H. Pfeiffer

Nutrients 2025, 17(15), 2539; https://doi.org/10.3390/nu17152539 (registering DOI) - 1 Aug 2025

Abstract

Background: Low-glycemic index (GI) carbohydrates like isomaltulose (ISO) are known to enhance incretin release and to improve postprandial glucose control at the following meal (an effect known as second meal effect, or SME), which is particularly beneficial for individuals with metabolic syndrome (MetS). This study aimed to assess the most effective preprandial interval of ISO- or saccharose (SUC) snacks (1 h vs. 3 h preload) to enhance prandial incretin responses to a subsequent meal. Methods: In a randomized crossover design, 15 participants with MetS completed four experimental conditions on four non-consecutive days, combining two preload types (ISO or SUC) and two preload timings (Intervention A: 3 h preload; Intervention B: 1 h preload). Specifically, the four conditions were (1) ISO + Intervention A, (2) SUC + Intervention A, (3) ISO + Intervention B, and (4) SUC + Intervention B. The order of conditions was randomized and separated by a 3–7-day washout period to minimize carryover effects. On each study day, participants consumed two mixed meal tests (MMT-1 and MMT-2) with a standardized preload (50 g ISO or SUC) administered either 3 h or 1 h prior to MMT-2. Blood samples were collected over 9 h at 15 predefined time points for analysis of glucose, insulin, C-peptide, and incretin hormones (GLP-1, GIP, and PYY). Results: The unique digestion profile of ISO resulted in a blunted glucose ascent rate (ΔG/Δt: 0.28 vs. 0.53 mmol/L/min for SUC, p < 0.01), paralleled by synonyms PYY elevation over 540 min monitoring, compared with SUC. ISO also led to higher and more sustained GLP-1 and PYY levels, while SUC induced a stronger GIP response. Notably, the timing of ISO consumption significantly influenced PYY secretion, with the 3 h preload showing enhanced PYY responses and a more favorable SME compared to the 1 h preload. Conclusions: ISO, particularly when consumed 3 h before a meal (vs. 1 h), offers significant advantages over SUC by elevating PYY levels, blunting the glucose ascent rate, and sustaining GLP-1 release. This synergy enhances the second meal effect, suggesting ISO’s potential for managing postprandial glycemic excursions in MetS. Full article

(This article belongs to the Section Nutrition and Metabolism)

27 pages, 39231 KiB

Open AccessArticle

Study on the Distribution Characteristics of Thermal Melt Geological Hazards in Qinghai Based on Remote Sensing Interpretation Method

by Xing Zhang, Zongren Li, Sailajia Wei, Delin Li, Xiaomin Li, Rongfang Xin, Wanrui Hu, Heng Liu and Peng Guan

Water 2025, 17(15), 2295; https://doi.org/10.3390/w17152295 (registering DOI) - 1 Aug 2025

Abstract

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

(This article belongs to the Special Issue Slope Stability Analyses and Landslide Risk Assessment Under Hydrodynamic Action)

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27 pages, 872 KiB

Open AccessArticle

Effect of Monomer Mixture Composition on TiCl₄-Al(i-C₄H₉)₃ Catalytic System Activity in Butadiene–Isoprene Copolymerization: A Theoretical Study

by Konstantin A. Tereshchenko, Rustem T. Ismagilov, Nikolai V. Ulitin, Yana L. Lyulinskaya and Alexander S. Novikov

Computation 2025, 13(8), 184; https://doi.org/10.3390/computation13080184 (registering DOI) - 1 Aug 2025

Abstract

Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This work aims to theoretically describe how the monomer mixture composition in the butadiene–isoprene copolymerization affects the activity of the TiCl₄–Al(i-C₄H₉)₃ catalytic system (expressed by active sites concentration) via kinetic modeling. This enables development of a reliable kinetic model for divinylisoprene rubber synthesis, predicting reaction rate, molecular weight, and composition, applicable to reactor design and process intensification. Active sites concentrations were calculated from experimental copolymerization rates and known chain propagation constants for various monomer compositions. Kinetic equations for active sites formation were based on mass-action law and Langmuir monomolecular adsorption theory. An analytical equation relating active sites concentration to monomer composition was derived, analyzed, and optimized with experimental data. The results show that monomer composition’s influence on active sites concentration is well described by a two-step kinetic model (physical adsorption followed by Ti–C bond formation), accounting for competitive adsorption: isoprene adsorbs more readily, while butadiene forms more stable active sites. Full article

(This article belongs to the Special Issue Feature Papers in Computational Chemistry)

18 pages, 1025 KiB

Open AccessArticle

The Analysis of Three-Dimensional Time-Fractional Helmholtz Model Using a New İterative Method

by Yasin Şahin, Mehmet Merdan and Pınar Açıkgöz

Symmetry 2025, 17(8), 1219; https://doi.org/10.3390/sym17081219 (registering DOI) - 1 Aug 2025

Abstract

This paper proposes a novel analytical method to address the Helmholtz fractional differential equation by combining the Aboodh transform with the Adomian Decomposition Method, resulting in the Aboodh–Adomian Decomposition Method (A-ADM). Fractional differential equations offer a comprehensive framework for describing intricate physical processes, including memory effects and anomalous diffusion. This work employs the Caputo–Fabrizio fractional derivative, defined by a non-singular exponential kernel, to more precisely capture these non-local effects. The classical Helmholtz equation, pivotal in acoustics, electromagnetics, and quantum physics, is extended to the fractional domain. Following the exposition of fundamental concepts and characteristics of fractional calculus and the Aboodh transform, the suggested A-ADM is employed to derive the analytical solution of the fractional Helmholtz equation. The method’s validity and efficiency are evidenced by comparisons of analytical and approximation solutions. The findings validate that A-ADM is a proficient and methodical approach for addressing fractional differential equations that incorporate Caputo–Fabrizio derivatives. Full article

(This article belongs to the Special Issue Symmetries and Fractional Differential Equations: Theory and Applications)

23 pages, 10868 KiB

Open AccessArticle

Quantitative Analysis and Nonlinear Response of Vegetation Dynamic to Driving Factors in Arid and Semi-Arid Regions of China

by Shihao Liu, Dazhi Yang, Xuyang Zhang and Fangtian Liu

Land 2025, 14(8), 1575; https://doi.org/10.3390/land14081575 (registering DOI) - 1 Aug 2025

Abstract

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

(This article belongs to the Section Land Use, Impact Assessment and Sustainability)

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17 pages, 1340 KiB

Open AccessArticle

Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation

by Yeonkyeong Kim, Kyu Bom Kim, Ah Young Leem, Kyuseok Kim and Su Hwan Lee

J. Clin. Med. 2025, 14(15), 5437; https://doi.org/10.3390/jcm14155437 (registering DOI) - 1 Aug 2025

Abstract

Background/Objectives: Identifying and classifying abnormal lung sounds is essential for diagnosing patients with respiratory disorders. In particular, the simultaneous recording of auscultation signals from multiple clinically relevant positions offers greater diagnostic potential compared to traditional single-channel measurements. This study aims to improve the accuracy of respiratory sound classification by leveraging multichannel signals and capturing positional characteristics from multiple sites in the same patient. Methods: We evaluated the performance of respiratory sound classification using multichannel lung sound data with a deep learning model that combines a convolutional neural network (CNN) and long short-term memory (LSTM), based on mel-frequency cepstral coefficients (MFCCs). We analyzed the impact of the number and placement of channels on classification performance. Results: The results demonstrated that using four-channel recordings improved accuracy, sensitivity, specificity, precision, and F1-score by approximately 1.11, 1.15, 1.05, 1.08, and 1.13 times, respectively, compared to using three, two, or single-channel recordings. Conclusion: This study confirms that multichannel data capture a richer set of features corresponding to various respiratory sound characteristics, leading to significantly improved classification performance. The proposed method holds promise for enhancing sound classification accuracy not only in clinical applications but also in broader domains such as speech and audio processing. Full article

(This article belongs to the Section Respiratory Medicine)

25 pages, 5840 KiB

Open AccessArticle

Creating Micro-Habitat in a Pool-Weir Fish Pass with Flexible Hydraulic Elements: Insights from Field Experiments

by Mehmet Salih Turker and Serhat Kucukali

Water 2025, 17(15), 2294; https://doi.org/10.3390/w17152294 (registering DOI) - 1 Aug 2025

Abstract

The placement of hydraulic elements in existing pool-type fishways to make them more suitable for Cyprinid fish is an issue of increasing interest in fishway research. Hydrodynamic characteristics and fish behavior at the representative pool of the fishway with bottom orifices and notches were assessed at the Dagdelen hydropower plant in the Ceyhan River Basin, Türkiye. Three-dimensional velocity measurements were taken in the pool of the fishway using an Acoustic Doppler velocimeter. The measurements were taken with and without a brush block at two different vertical distances from the bottom, which were below and above the level of bristles tips. A computational fluid dynamics (CFD) analysis was conducted for the studied fishway. The numerical model utilized Large Eddy Simulation (LES) combined with the Darcy–Forchheimer law, wherein brush blocks were represented as homogenous porous media. Our results revealed that the relative submergence of bristles in the brush block plays a very important role in velocity and Reynolds shear stress (RSS) distributions. After the placement of the submerged brush block, flow velocity and the lateral RSS component were reduced, and a resting area was created behind the brush block below the bristles’ tips. Fish movements in the pool were recorded by underwater cameras under real-time operation conditions. The heatmap analysis, which is a 2-dimensional fish spatial presence visualization technique for a specific time period, showed that Capoeta damascina avoided the areas with high turbulent fluctuations during the tests, and 61.5% of the fish presence intensity was found to be in the low Reynolds shear regions in the pool. This provides a clear case for the real-world ecological benefits of retrofitting existing pool-weir fishways with such flexible hydraulic elements. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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