Search Results (262)

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

In this study, we analyzed the lithospheric seismic background of the Tianshan and adjacent areas by combining various geophysical methods (effective elastic thickness, time-varying gravity, apparent density, and InSAR), and explored the genesis mechanism of the Wushi Ms7.1 earthquake as an example, which led to the following conclusions: (1) The effective elastic thickness (Te) of the Tianshan lithosphere is low (13–28 km) and weak, while the Tarim and Junggar basins have Te > 30 km with high intensity, and the loads are all mainly from the surface (F < 0.5). Earthquakes occur mostly in areas with low values of Te. (2) Medium and strong earthquakes are prone to occur in regions with alternating positive and negative changes in the gravity field during the stage of large-scale reverse adjustment. It is expected that the risk of a moderate-to-strong earthquake occurring again in the vicinity of the survey area between 2025 and 2026 is relatively high. (3) Before the Wushi earthquake, the positive and negative boundaries of the apparent density of the crust at 12 km shifted to be approximately parallel to the seismic fault, and the earthquake was triggered after undergoing a “solidification” process. (4) The Wushi earthquake is a leptokurtic strike-slip backwash type of earthquake; coseismic deformation shows that subsidence occurs in the high-visual-density zone, and vice versa for uplift. The results of this study reveal the lithosphere-conceiving environment of the Tianshan and adjacent areas and provide a basis for regional earthquake monitoring, early warning, and post-disaster disposal. Full article

Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km²) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. Full article

In this article, we investigate the fractional soliton solutions as well as the chaotic analysis of the fourth-order nonlinear Ablowitz–Kaup–Newell–Segur wave equation. This model is considered an intriguing high-order nonlinear partial differential equation that integrates additional spatial and dispersive effects to extend the concepts to more intricate wave dynamics, relevant in engineering and science for understanding complex phenomena. To examine the solitary wave solutions of the proposed model, we employ sophisticated analytical techniques, including the generalized projective Riccati equation method, the new improved generalized exponential rational function method, and the modified F-expansion method, along with mathematical simulations, to obtain a deeper insight into wave propagation. To explore desirable soliton solutions, the nonlinear partial differential equation is converted into its respective ordinary differential equations by wave transforms utilizing $\beta$-fractional derivatives. Further, the solutions in the forms of bright, dark, singular, combined, and complex solitons are secured. Various physical parameter values and arrangements are employed to investigate the soliton solutions of the system. Variations in parameter values result in specific behaviors of the solutions, which we illustrate via various types of visualizations. Additionally, a key aspect of this research involves analyzing the chaotic behavior of the governing model. A perturbed version of the system is derived and then analyzed using chaos detection techniques such as power spectrum analysis, Poincaré return maps, and basin attractor visualization. The study of nonlinear dynamics reveals the system’s sensitivity to initial conditions and its dependence on time-decay effects. This indicates that the system exhibits chaotic behavior under perturbations, where even minor variations in the starting conditions can lead to drastically different outcomes as time progresses. Such behavior underscores the complexity and unpredictability inherent in the system, highlighting the importance of understanding its chaotic dynamics. This study evaluates the effectiveness of currently employed methodologies and elucidates the specific behaviors of the system’s nonlinear dynamics, thus providing new insights into the field of high-dimensional nonlinear scientific wave phenomena. The results demonstrate the effectiveness and versatility of the approach used to address complex nonlinear partial differential equations. Full article

Cyber-physical systems such as Supervisory Control and Data Acquisition (SCADA) have been applied in industrial automation and infrastructure management for decades. They are hybrid tools for administration, monitoring, and continuous control of real physical systems through their computational representation. SCADA systems have evolved along with computing technology, from their beginnings with low-performance computers, monochrome monitors and communication networks with a range of a few hundred meters, to high-performance systems with advanced 3D graphics and wired and wireless computer networks. This article presents a methodology for the design of a SCADA system with a 3D Visualization for Drinking Water Supply, and its implementation in the Lerma Basin System of Mexico City as a case study. The monitoring of water consumption from the wells is presented, as well as the pressure levels throughout the system. The 3D visualization is generated from the GIS information and the communication is carried out using a hybrid radio frequency transmission system, satellite, and telephone network. The pumps that extract water from each well are teleoperated and monitored in real time. The developed system can be scaled to generate a simulator of water behavior of the Lerma Basin System and perform contingency planning. Full article

The present study focused on the Upper Cretaceous to Middle Miocene sequence in the Central Gulf of Suez, Egypt. The Upper Cretaceous to Middle Miocene sequence in the October field is thick and deeply buried, consisting mainly of brown limestone, chalk limestone, and reefal limestone intercalated with clastic shale. This study integrated various datasets, including total organic carbon (TOC), Rock-Eval pyrolysis, visual kerogen examination, vitrinite reflectance (%Ro), and bottom-hole temperature measurements. The main objective of this study is to delineate the source rock characteristics of these strata regarding organic richness, thermal maturity, kerogen type, timing of hydrocarbon transformation and generation. The Upper Cretaceous Brown Limestone Formation is represented by 135 samples from four wells and is considered to be a fair to excellent source rock, primarily containing type I and II kerogen. It is immature to early mature, generating oil with a low to intermediate level of hydrocarbon conversion. The Eocene Thebes Formation is represented by 105 samples from six wells and is considered to be a good to fair oil source rock with some potential for gas, primarily containing type II and II/III kerogen. Most samples are immature with a low level of hydrocarbon conversion while few are mature having an intermediate degree of hydrocarbon conversion. The Middle Miocene Lower Rudeis Formation is represented by 8 samples from two wells and considered to be a fair but immature source rock, primarily containing type III kerogen with a low level of conversion representing a potential source for gas. The Middle Miocene Belayim Formation is represented by 29 samples from three wells and is considered to be a poor to good source rock, primarily containing kerogen type II and III. Most samples are immature with a low level of hydrocarbon conversion while few are mature having an intermediate degree of hydrocarbon conversion. 1D basin model A-5 well shows that the Upper Cretaceous Brown Limestone source rock entered the early oil window at 39 Ma, progressed to the main oil window by 13 Ma, and remains in this stage today. The Eocene Thebes source rock began generating hydrocarbons at 21.3 Ma, advanced to the main oil window at 11 Ma, and has been in the late oil window since 1.6 Ma. The Middle Miocene Lower Rudeis source rock entered the early oil window at 12.6 Ma, transitioned to the main oil window at 5.7 Ma, where it remains active. In contrast, the Middle Miocene Belayim source rock has not yet reached the early oil window and remains immature, with values ranging from 0.00 to 0.55 % Ro. The transformation ratio plot shows that the Brown Limestone Formation began transforming into the Upper Cretaceous (73 Ma), reaching 29.84% by the Miocene (14.3 Ma). The Thebes Formation initiated transformation in the Late Eocene (52.3 Ma) and reached 6.42% by 16.4 Ma. The Lower Rudeis Formation began in the Middle Miocene (18.7 Ma), reaching 3.59% by 9.2 Ma. The Belayim Formation started its transformation at 11.2 Ma, reaching 0.63% by 6.8 Ma. Full article

Climate change and intensified human activities have increasingly threatened the sustainability of groundwater resources, especially in ecologically fragile karst regions. To address these challenges, this study proposes a karst spring discharge prediction model that integrates BiLSTM (Bidirectional Long Short-Term Memory) and the Transformer Encoder. The BiLSTM component captures both forward and backward information in spring discharge data, extracting trend-related features. The Transformer’s attention mechanism is employed to identify key precipitation factors influencing spring discharge. A patching preprocessing strategy divides monthly scale sequences into annual segments, reducing input length while enabling local modeling and global interaction. Experiments on Shentou Spring discharge show that the BiLSTM–Transformer Encoder outperforms other deep learning models across multiple evaluation metrics, with notable advantages in short-term forecasting. The patching strategy effectively reduces model parameters and improves efficiency. Attention visualization further confirms the model’s ability to capture critical hydrological drivers. This study not only provides a novel approach to sustainable water management in karst spring basins but also demonstrates an effective use of deep learning for long-term hydrological sustainability. Full article

Soil erosion poses a significant threat to natural resources and agricultural productivity in arid regions. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to simulate rainfall erosivity and soil erosion risk in the Wadi Allith basin, Saudi Arabia, using rainfall data from 2016 to 2018. The results demonstrated that the basin experienced a predominant slight level of erosion risk, with around 5 tons/ha annually. This study revealed that a very slight erosion risk was predominant in 2016 (97% of the basin area), 2017 (96%), and 2018 (95%), while less than 1% of the study area was exposed to severe erosion risks across all three years. An increasing trend in erosion severity was observed between 2016 and 2018, correlating with rising average annual rainfall amounts of 120 mm, 145 mm, and 155 mm. This underscores the importance of understanding how climatic factors influence soil stability, particularly in arid regions where water scarcity is typically a limiting factor. The successful application of Geographic Information Systems (GISs) and remote sensing tools integrating the various components of the RUSLE model showcases the effectiveness of these technologies in environmental monitoring and risk assessment. These tools facilitate a comprehensive analysis of the factors contributing to soil erosion, enabling researchers and policymakers to visualize erosion risk across the basin and prioritize areas for intervention. This study highlights the importance of ongoing soil erosion monitoring in arid environments such as the Wadi Allith basin, Saudi Arabia. Full article

In subsurface geological mapping, it is more than advisable to compare different solutions obtained with neural and other algorithms. Here, for such comparison, we used the previously published and well-prepared dataset of subsurface data collected from the Bjelovar Subdepression, a 2900 km² large regional macrounit in the Croatian part of the Pannonian Basin System. Data on depth were obtained for the youngest (the shallowest) Lonja Formation (Pliocene, Quaternary) and mapped using neural network (NN), inverse distance weighting (IDW), and ordinary kriging (OK) algorithms. The obtained maps were compared based on square error (using k-fold cross-validation) and the visual interpretation of isopaches. Two other algorithms were also tested, namely, random forest (RF) and extreme gradient boosting (XGB) algorithms, but they were rejected as inappropriate for this purpose solely based on the visuals of the obtained maps, which did not follow any interpretable geological structures. The results showed that NN is a highly adjustable method for interpolation, with adjustment for numerous hyperparameters. IDW showed its strength as one of the classical interpolators, and its results are always located close to the top if several methods are compared. OK is the relative winner, showing the flexibility of variogram analysis regarding the number of data points and possible clustering. The presented variogram model, even with a relatively high sill and occasional nugget effect, can be well fitted into OK, giving better results than other methods when applied to the presented area and datasets. This was not surprising because kriging is a well-established method used exclusively for interpolation. In contrast, NN and machine learning algorithms are used in many fields, and these algorithms, particularly the fitting of hyperparameters in NN, simply cannot be the best solution for all. Full article

Benthic diatoms are key primary producers in aquatic ecosystems and sensitive bioindicators for water quality monitoring; for example, the Yellow River Basin exhibits high diatom species diversity. However, traditional microscopic identification of such species remains inefficient and inaccurate. To enable automated identification, we established a benthic diatom dataset containing 3157 SEM images of 32 genera/species from the Yellow River Basin and developed a novel identification method. Specifically, the knowledge extraction module distinguishes foreground features from background noise by guiding spatial attention to focus on mutually exclusive regions within the image. This mechanism allows the network to focus more on foreground features that are useful for the classification task while significantly reducing the interference of background noise. Furthermore, a dual knowledge guidance module is designed to enhance the discriminative representation of fine-grained diatom images. This module strengthens multi-region foreground features through grouped channel attention, supplemented with contextual information through convolution-refined background features assigned low weights. Finally, the proposed method integrates multi-granularity learning, knowledge distillation, and multi-scale training strategies, further improving the classification accuracy. The experimental results demonstrate that the proposed network outperforms comparative methods on both the self-built diatom dataset and a public diatom dataset. Ablation studies and visualization further validate the efficacy of each module. Full article

We highlight the lack of researchers studying freshwater zooplankton in Latin America and contextualize it within the global extinction of taxonomists, global loss of biodiversity, and regional reality to visualize the intensity of this threat and possible strategies for addressing it. The scarcity of researchers working on freshwater zooplankton currently threatens the future of these studies in the world. This global trend of the decreasing interest of scientists and local governments in learning about this important component of freshwater biodiversity is more accentuated in Latin America by regional drivers, such as brain drain, a lack of support by stakeholders, and the absence of planning for the rational use and conservation of this essential natural resource. All these drivers interact and have more dramatic consequences for regional research due to the recent reduction in government funds for science in some of these countries. In the context of Global Change; a loss of biodiversity due to this fact; and the misuse of drainage basins, overexploitation, and regional pressures, the lack of researchers studying freshwater zooplankton and, in general, all aquatic life has emerged as a critical threat to the delicate equilibrium of these ecosystems. Within this situation, scientific integration through intra-regional and extra-regional collaboration networks has emerged as an unavoidable strategy for the survival and future strengthening of studies on biodiversity and the conservation of freshwater zooplankton in Latin America. The development of new technologies such as DNA barcoding, metabarcoding, and metagenomics has emerged as a solution to this problem. Nevertheless, they should be considered as new tools towards integrative taxonomy and not as replacements for taxonomical studies. Full article

Deep coal seams in the Junggar Basin, China, have demonstrated high gas yields due to enhanced pore structures resulting from hydraulic fracturing. However, raw coal samples inadequately represent these stimulated reservoirs, and acquiring fractured core samples post-stimulation is impractical. To address this, a novel and operable laboratory method has been developed to fabricate porosity-enhanced synthetic coal plugs that better simulate deep coalbed methane reservoirs. The fabrication process involves crushing lignite and separating it into three particle size fractions (<0.25 mm, 0.25–1 mm, and 1–2 mm), followed by mixing with a resin-based binder system (F51 phenolic epoxy resin, 650 polyamide, and tetrahydrofuran). These mixtures are molded into cylindrical plugs (⌀50 mm × 100 mm) and cured. This approach enables tailored control over pore development during briquette formation. Porosity and pore structure were comprehensively assessed using helium porosimetry, mercury intrusion porosimetry (MIP), and micro-computed tomography (micro-CT). MIP and micro-CT confirmed that the synthetic plugs exhibit significantly enhanced porosity compared to raw lignite, with pore sizes and volumes falling within the macropore range. Specifically, porosity reached up to 27.84%, averaging 20.73% and surpassing the typical range for conventional coal briquettes (1.89–18.96%). Additionally, the resin content was found to strongly influence porosity, with optimal levels between 6% and 10% by weight. Visualization improvements in micro-CT imaging were achieved through iodine addition, allowing for more accurate porosity estimations. This method offers a cost-effective and repeatable strategy for creating coal analogs with tunable porosity, providing valuable physical models for investigating flow behaviors in stimulated coal reservoirs. Full article

Persistent localization is a critical requirement for autonomous underwater vehicles (AUVs) engaged in long-term missions. Conventional dead-reckoning (DR) methods for estimating the position and orientation of AUVs often suffer from drift, necessitating additional information to correct accumulating errors. In this paper, we propose a visual artificial landmarks-based simultaneous localization and mapping (SLAM) system for AUVs. This system utilizes two types of underwater artificial landmarks that are observed using forward and downward-looking cameras. The information obtained from these detected landmarks, along with incremental DR estimates, is integrated within a framework based on the extended Kalman filter (EKF) SLAM approach, allowing for the recursive estimation of both the robot and the landmark states. We implemented the proposed visual SLAM method using our yShark II AUV and conducted experiments in an engineering basin to validate its effectiveness. A ceiling vision-based reference pose acquisition system was installed, facilitating a comparison between the pose estimation results obtained from DR and those derived from the SLAM method. Full article

This study investigated the occurrence of fibrous microplastics and natural and artificial cellulose microfibers in the gastrointestinal tracts of Mullus barbatus and Merluccius merluccius specimens from the Adriatic Sea (Central Mediterranean), an important hotspot for marine litter accumulation. Red mullet and European hake were chosen due to their roles as bioindicators of marine pollution in the Mediterranean, and their economic relevance as fishery resources. Microfibers were found in 72% of M. barbatus and 68% of M. merluccius, at levels ranging from 1 to 67 particles/individual. Most of the microfibers extracted were textile fibers that were blue (33.6%), clear (26.1%), and black (20.3%) in color, while the length distribution showed the prevalence of microfibers in the size range of 350–950 µm. This visual identification, corroborated by the micro-FTIR analysis of a sub-sample of microfibers, revealed that natural and artificial cellulose microfibers were more common (80%) than fibrous microplastics. The results confirmed that both of these fish species are susceptible to microfiber ingestion and indicated the high availability of natural and artificial cellulosic fibers in the Adriatic Basin. Despite the increased evidence of microfiber pollution in the marine ecosystem, only a limited number of studies examine natural/artificial microfiber contamination and ingestion by marine biota. Therefore, greater attention should be given to this new type of contaminant, considering its implications in terms of environmental health, food security, and food safety. Full article

This study introduces a novel, drone-based approach for the detection and classification of Greater Caribbean Manatees (Trichechus manatus manatus) in the Panama Canal Basin by integrating advanced deep learning techniques. Leveraging the high-performance YOLOv8 model augmented with Sliced Aided Hyper Inferencing (SAHI) for improved small-object detection, our system accurately identifies individual manatees, mother–calf pairs, and group formations across a challenging aquatic environment. Additionally, the use of AltCLIP for zero-shot classification enables robust demographic analysis without extensive labeled data, enhancing model adaptability in data-scarce scenarios. For this study, more than 57,000 UAV images were acquired from multiple drone flights covering diverse regions of Gatun Lake and its surroundings. In cross-validation experiments, the detection model achieved precision levels as high as 93% and mean average precision (mAP) values exceeding 90% under ideal conditions. However, testing on unseen data revealed a lower recall, highlighting challenges in detecting manatees under variable altitudes and adverse lighting conditions. Furthermore, the integrated zero-shot classification approach demonstrated a robust top-2 accuracy close to 90%, effectively categorizing manatee demographic groupings despite overlapping visual features. This work presents a deep learning framework integrated with UAV technology, offering a scalable, non-invasive solution for real-time wildlife monitoring. By enabling precise detection and classification, it lays the foundation for enhanced habitat assessments and more effective conservation planning in similar tropical wetland ecosystems. Full article