Search Results (14,882)

Salinity is a key environmental survival factor for all aquatic organisms, especially migratory species. The masu salmon (Oncorhynchus masou) is a representative migratory fish species. Following the freshwater parr stage, anadromous masu salmon briefly inhabit brackish water and transition before migrating to the ocean. To demonstrate the physiological responses of masu salmon (length: 8 ± 0.5 cm, water temperature: 10 ± 0.5 °C) to variations in salinity, we carried out three gradual transfer experiments (gradual daily increases direct transfer experiment of 3.2 (D10), 1.6 (D20), and 1.1 (D30) ppt until reaching 32 ppt) and one (immediate transfer to 32 ppt on day 0) as domestication regimens for masu salmon. The results indicated the following: (1) In the gradual transfer experiment group, growth performance, along with ion and hormone indicators, suggested that the D30 treatment group of anadromous masu salmon exhibited a high level of adaptability. (2) In the direct transfer experiment, in addition to the activity of antioxidant enzymes, both ion concentrations and hormone indicators returned to a stable state within 7 days. Our findings provide a scientific protocol for salinity regulation during the artificial propagation of masu salmon and establish critical acclimation parameters for land-based recirculating aquaculture systems aimed at marine salmonid farming, thereby highlighting their practical value. Full article

Petroleum hydrocarbons remain major environmental contaminants, and understanding the mechanisms governing their biodegradation is essential for designing effective remediation plans. The strategy in this article is slightly different from other cases in the literature. Such literature models require, for their elaboration, a significant number of experiments; the number of experimental determinations is at least proportional to the square of the number of constants introduced in the mathematical expressions. For this reason, the strategy followed in this article is different—starting from a set of experiments carried out and presented in a coherent and published manner, a simple methodology for building specific and minimal models, which will allow solving specific problems, was effectively developed. This study develops a nonlinear mathematical structure, expressed as a system of coupled differential equations, that simultaneously describes the degradation of petroleum hydrocarbons and the dynamics of hydrocarbon-degrading bacteria and fungi in soil–sludge mixtures. The model was calibrated using experimental data obtained from biopiles prepared with different volumetric ratios of contaminated soil and sewage sludge. Approximate analytical solutions were derived and the distributed constants were evaluated. For a consistent discussion, the analytical solutions were assessed against numerical desk simulations performed with a classical fourth-order Runge–Kutta method, which accurately reproduced the nonlinear behavior of the specific system. This numerical approach was chosen in order to overcome the proper difficulties encountered in this strategy implementation. The results show that the soil–sludge ratio strongly influences biodegradation efficiency, while kinetic parameters determine whether microbial communities evolve toward a stationary regime or accelerated contaminant removal. The combined analytical–numerical framework provides a robust predictive tool for optimizing mixture composition and improving the design of bioremediation treatments for petroleum-contaminated soils. Full article

Tungsten is an extremely durable metal with a wide range of industrial applications and its toxicity is relatively low, although chronic exposure to its compounds can lead to adverse health effects. This paper proposes a method for the determination of trace amounts of tungsten using cathodic stripping voltammetry (CSV). A hybrid structure based on a mixture of multi-walled carbon nanotubes and spherical glassy carbon was used as the working electrode, on the surface of which a film of lead was formed during the measurement to increase the efficiency of the determination. A comprehensive optimization of the analytical parameters, including accumulation potential and time, signal recording conditions and electrolyte solution composition, was carried out to maximize sensitivity and improve the signal-to-noise ratio. The method developed achieved a detection limit for tungsten of 3 × 10⁻¹⁰ mol L⁻¹, demonstrating its high sensitivity. The working electrode showed selectivity, signal reproducibility and resistance to the presence of potential interferences. The reliability and applicability of the proposed solution were confirmed by applying the method to the analysis of real environmental samples and certified reference materials, with satisfactory results. The presented analytical procedure represents a promising tool for the routine determination of tungsten in complex real matrices. Full article

In unmanned ground vehicle (UGV) swarm systems, comprehensive environmental awareness is critical for coordinated operations. Yet they are frequently deployed in occlusion-rich, constrained environments where multi-agent visual fusion is essential. However, existing methods are critically limited by offline-calibrated extrinsic parameters, hindering flexible deployment, and by a strong co-visibility assumption, which fails under severe occlusion. To overcome these constraints, we introduce an end-to-end, calibration-free framework for the joint registration of cameras and subjects. Our approach begins with a single-view module that estimates subjects’ poses and appearance features. Subsequently, a novel graph-based pose propagation module (GPPM) treats UGVs’ cameras as nodes in a graph, connecting them with edges when they share co-visible subjects identified via appearance matching. Breadth-first search (BFS) then finds the shortest registration path from any camera to a designated root camera, enabling pose propagation via local co-visibility links and global alignment of all subjects into a unified bird’s-eye-view (BEV) space. This strategy relaxes the stringent requirement of full co-visibility with the root node. A multi-task loss function is proposed to jointly optimize pose estimation and feature matching. Trained and evaluated on a synthetic dataset with occlusions (CSRD-O) collected by a UGV swarm system, our framework achieves mean camera pose errors of 1.57 m/8.70° and mean subject pose errors of 1.40 m/9.14°. Furthermore, we demonstrate a scene monitoring task using a UGV swarm system. Experiments show that the proposed method generates robust BEV estimates even under severe occlusion and low inter-view overlap. This work presents a purely visual, self-calibrating multi-view fusion perception scheme, demonstrating its potential to support cooperative perception, task-oriented monitoring, and collective situational awareness in UGV swarm systems. Full article

Unmanned Aerial Vehicles (UAVs) are widely used in fields such as autonomous missions, reconnaissance, surveillance, and various industrial applications. These vehicles can perform desired tasks without human intervention in challenging environmental conditions. However, UAV control can be difficult due to environmental factors, wind disturbances, and uncertainties in system parameters. Therefore, developing reliable control strategies for UAVs is a significant challenge for researchers and engineers. This study presents a comprehensive review of rotary-wing UAVs, focusing on quadcopter and helicopter systems. Approximately 77 studies were selected from the Web of Science (WOS) database and analyzed, with an emphasis on Sliding Mode Control (SMC) and Fractional-Order SMC (FOSMC) applications in these systems. The review addresses key topics such as degrees of freedom, proposed control methods, adjustment techniques, comparative methods, fractional-order definitions, simulation tools, and explanations. The literature analysis highlights current research trends by showing the performance advantages and limitations of SMC and FOSMC methods. Furthermore, future research directions and existing knowledge gaps are discussed in detail. This review was prepared to provide the control engineering community with a comprehensive understanding of SMC and FOSMC applications in rotary wing systems and to contribute to the development of innovative and effective control strategies. Full article

This study used machine learning methods and spatial analysis to examine groundwater quality in Winkler County, Texas, focusing on nitrate pollution. By analyzing 85 years of groundwater data from six aquifers, the study uses advanced machine learning models Random Forest, Decision Tree, Linear Regression, and XGBoost to predict contamination levels and explore spatial and temporal trends. These models were chosen because of their ability to handle larger and more complex datasets and their ability to capture nonlinear relationships between water quality parameters and environmental variables. These machine learning algorithms are particularly effective at identifying patterns and interactions that may not be obvious with traditional analytical methods, and get more reliable and accurate results. Our decadal analysis specifically identified systematic fluctuations in nitrate levels, with a notable increase since the early 2000s, driven by the synergistic effects of rising temperatures and intensified agricultural land use. Climate change, pressured by rising temperatures and lessened precipitation, along with natural factors such as the formation of sinkholes, has been identified as a key driver of groundwater quality fluctuations. Elevated nitrate levels were mostly related to agricultural irrigation and excessive use of synthetic fertilizers. The machine learning model also highlights how land cover changes and human activities are contributing to groundwater quality deterioration. This research reinforces the value of integrating machine learning and spatial analysis for groundwater management. This is especially true in areas affected by sinkholes. It provides important information to reduce man-made impacts to water quality in West Texas. Full article

Background: low-quantity or degraded samples are often studied in forensic genetics. Therefore, it is important to efficiently obtain all the available DNA from the biological sample analyzed to provide the most reliable results. This is particularly challenging in bone marrow processing due to its hydrophobic molecular structure, as for other lipid-rich tissues, especially if rancid. In fact, during adipose tissue decomposition, the putrefaction of fatty acids can in some instances give a compact cerous consistency to the lipidic tissue, hardly susceptible to the nucleic acid extraction mechanisms. According to environmental circumstances, this condition is notably observable in submerged bodies or in putrefied bone marrow. Thus, this study is focused on developing an optimized nucleic acids extraction protocol for putrefied bone marrow. Methods: genetic analyses were performed on putrefied yellow bone marrow collected from 20 human femora recovered from bodies in different decomposition stages. The optimized method was developed by integrating additional steps, reagents and time intervals on a silica-based column commercial kit. This strategy was compared in DNA yield to a standard extraction protocol, represented by the same commercial kit, but following the manufacturer’s directions. Both these strategies were tested in nucleic acid isolation efficiency by performing DNA typing, including real-time PCR quantification, Short Tandem Repeats (STR) amplification and fragments analysis steps. The analytical parameters evaluated were allele count, DNA concentration (ng/µL) and Degradation Index (DI). Results: for allele count and DNA concentration parameters, the optimized protocol showed clear and significant qualitative and quantitative improvements compared with the standard protocol, supporting its potential applicability in forensic casework and laying the foundation for future studies. Conclusions: prior to appropriate laboratory internal validation, the optimized protocol can be used for tough lipid-rich tissues processing without the need to purchase a dedicated system and using a same commercial kit routinely adopted for other forensic genetics matrices. Full article

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously absent in the literature. We identify optimal powertrain efficiency values that are directly derived from publicly available vehicle specifications, ensuring transparency and accessibility. Our findings demonstrate that this simple, physics-based model accurately estimates fuel consumption and CO₂ emissions for standard EPA cycles and can be effectively generalized to user-defined scenarios. This establishes a computationally efficient, interpretable, and robust method for environmental impact assessment, policy evaluation, and real-time emissions estimation. Full article

Silicon-based treatments applied with UAV technology were evaluated over two consecutive rice-growing seasons (2024–2025) under Mediterranean field conditions. Silicon and silicon–manganese applications significantly reduced the Pyricularia infestation index (PII) by up to 77% at 35 DAS compared to the control (p < 0.01). Grain yield increased from 1717 kg ha⁻¹ in control plots to 4328 kg ha⁻¹ under silicon treatment and 3958 kg ha⁻¹ under silicon–manganese treatment. In contrast, Sentinel-2 spectral bands (B4 and B8) and vegetation indices (NDVI, RVI, NDRE, IRECI) were mainly influenced by interannual variability rather than treatment effects. While canopy reflectance showed high residual variability at later growth stages, agronomic and sanitary parameters consistently responded to silicon-based applications. These results indicate that foliar silicon, particularly when combined with manganese, improves Pyricularia suppression and yield stability under variable environmental conditions, although satellite-derived vegetation indices were more sensitive to year effects than to treatment differences. Full article

Reverse osmosis concentrate (ROC) contains relatively high levels of refractory organic pollutants, posing significant challenges due to its difficult treatment and high environmental risks. Therefore, efficient and convenient removal strategies are essential. In this study, a self-developed iron-modified activated carbon fiber (Fe-ACF) was employed as an adsorbent to remove organic pollutants from ROC. Additionally, response surface methodology (RSM) was applied to model the adsorption process, identify and evaluate key influencing parameters, and optimize operational conditions. The adsorption mechanisms and regeneration stability of Fe-ACF were also investigated. Kinetic analysis revealed that the adsorption process is predominantly governed by chemisorption, with intraparticle diffusion identified as the primary rate-limiting step. Isothermal adsorption studies demonstrated that the Langmuir–Freundlich model best describes the adsorption behavior, yielding a theoretical maximum adsorption capacity of 12.21 ± 0.80 mg/g. Thermodynamic analysis confirmed that the adsorption process is spontaneous, endothermic, and driven by an increase in entropy. The RSM optimization identified pH as the dominant factor. The optimal adsorption conditions were a pH of 4.18, a temperature of 34.63 °C, a stirring speed of 547.91 rpm, and an adsorbent dosage of 1.55 g/L. The adsorption mechanism involves hydrogen bonding, π–π interactions, surface complexation, and electrostatic forces. Fe-ACF exhibits competitive regeneration stability and structural integrity. In summary, Fe-ACF demonstrates significant potential as a treatment material for ROC. Full article

The increasing demand for sustainable materials has accelerated the development of environmentally friendly filaments for fused deposition modeling (FDM). In this study, the surface roughness and thermal degradation behavior of sustainable PLA-based filaments, including PLA, recycled PLA (Re–PLA), and wood-filled PLA (Wood–PLA), were systematically investigated under different FDM printing conditions. A full factorial experimental design was employed to identify the dominant processing parameters and optimize surface quality. Surface roughness was evaluated using values Ra, Rz, and Rq parameters measured on three different surface orientations (top surface at 0°, top surface at 45°, and side surface). Scanning electron microscopy (SEM) was used to examine the relationship between roughness measurements and surface morphology, while thermogravimetric analysis (TGA) was performed to evaluate the thermal degradation behavior of the filaments in relation to printing temperature. The results have shown that filament material is the most important parameter affecting surface roughness. While Wood–PLA exhibited the highest roughness due to fiber-induced surface heterogeneity, recycled Re–PLA showed moderate surface irregularities resulting from degradation compared to pure PLA. Despite a rougher filament surface prior to production, recycled PLA exhibited a surface morphology similar to that of pure PLA after printing, influenced by the processing parameters. Furthermore, SEM findings indicated that the Ra parameter predominantly reflects macro-scale surface topography, while local microstructural heterogeneity can be better characterized by complementary roughness parameters such as Rz. These findings support optimizing printing conditions to improve surface quality and more widespread use of sustainable FDM filaments in applications where surface roughness is critical. Full article

The growing consumption of natural aggregates in concrete production has raised significant environmental and sustainability concerns, motivating the search for alternative and waste-based materials. Walnut shells (WSs), an abundant agricultural by-product, have attracted increasing attention as a potential partial replacement for fine and coarse aggregates in concrete. This study presents a comprehensive review and comparative analysis of published experimental data examining the influence of WS incorporation on the fresh and hardened properties of concrete. Data from the literature covering WS replacement ratios ranging from 1% to 50% were systematically compiled and evaluated with respect to compressive strength, splitting tensile strength, flexural strength, slump, and density. The results indicate that low WS replacement levels (generally ≤10%) may preserve acceptable mechanical performance while contributing to sustainability objectives, whereas higher replacement ratios lead to pronounced reductions in strength, particularly in splitting tensile and flexural capacities. Workability consistently decreases with increasing WS content due to the porous structure and high water absorption of the shells, while density reductions suggest the potential for producing lightweight concrete. Overall, the findings demonstrate that WSs can be effectively utilized in concrete at limited replacement levels, provided that mix design parameters and performance requirements are carefully balanced. The study also highlights the need for further research focusing on durability, long-term behavior, and optimization strategies to enhance the practical applicability of WS-based sustainable concrete. Full article

The transport of dangerous goods (TDG) produces serious risks, particularly in urban areas, due to the high presence of people and sensitive infrastructures from a social, environmental and economic point of view. Transport Risk Assessment combines occurrence, vulnerability and exposure to support urban transport planning aimed at achieving the Sustainable Development Goals. The objective of this paper is to propose a simplified risk calculation method, referring to a single link of the urban transport network, with reference to the occurrence component of the risk. The proposed formulation considers the sequence of factors that determine the overall dangerous event. The specification of the occurrence factors and a quantitative definition of the different parameters for a widespread type of transport of dangerous goods in urban areas is proposed. The results obtained are interesting because (1) the method, with quantitative parameters, can be applied to any urban area, and (2) some of the factors can also be used by replacing and introducing, where known, specific factors and relative parameters calibrated for the area for which it is planned to be implemented. The results indicate the feasibility of the proposed method without significant chemical–physical or electromechanical insights. This work is of potential interest for urban transport planners and public and private decision makers. Full article

Water pollution from the sugar industry is a significant environmental problem as it generates effluents containing organic compounds, solids, nutrients, and chemicals such as H₃PO₄, SO₂, and Ca (OH)₂. Mesoporous silica nanoparticles (MSNs) are a promising option for its treatment, due to their high surface area, and ease of functionalization using organically modified silanes (ORMOSIL) improving its adsorption of contaminants. The objective of this study is to remove anions (Cl⁻, SO₄²⁻, NO₂⁻, NO₃⁻) from the wastewater of a sugar mill in Campeche, Mexico and improve its physicochemical parameters (conductivity, turbidity, dissolved oxygen) using MSNs functionalized with 3-aminopropyltriethoxysilane (MSNs-APTES) or 3-(2-aminoethylamino)propyltrimethoxysilane (MSNs-3-2-A). The synthesized materials were characterized by FTIR and XPS analyses, which confirmed the incorporation of amino functional group and that MSNs-APTES exhibited a stronger N1s signal, indicating greater surface accessibility of amino groups. However, a partial surface masking under complex aqueous conditions was revealed. In contrast, MSNs-3-2-A showed lower apparent surface exposure of amino groups maintaining a more stable functional presence after exposure, likely due to its diamine structure promoting more confined interactions within the mesoporous framework. The results of removing anions and physicochemical parameters of wastewater exposed to MSNs indicate that treatments with MSNs-APTES and MSNs-3-2-A were able to significantly reduce the concentrations of SO₄²⁻, NO₂⁻ and NO₃⁻ anions, but not able to reduce the chloride ion. A decrease in turbidity and an increase in dissolved oxygen were also observed. Then, both materials proved to be functional and stable in contact with wastewater, demonstrating their potential for environmental remediation, particularly for the removal of anionic contaminants from sugar industry effluents. Full article

Escherichia coli O157:H7 (E. coli O157:H7) is a highly virulent foodborne pathogen with an extremely low infectious dose, making its rapid and accurate detection in food and environmental samples critically important. In recent years, significant progress has been made in immunological techniques for the rapid identification of E. coli O157:H7. This review systematically summarizes advances in immunological methods for the detection of E. coli O157:H7 over the past decade, focusing on lateral flow immunoassays (LFIA), enzyme-linked immunosorbent assays (ELISA), immunosensors (optical and electrochemical), and nanobody-based technologies. Key aspects such as detection principles, specificity, antibody types (monoclonal, polyclonal, nanobodies), signal readout mechanisms, and applicability to different sample matrices are compared. Performance parameters, including limit of detection (LOD), specificity, detection time, and matrix compatibility, are summarized to evaluate the advantages and limitations of each method. Furthermore, international food safety standards and regulations (ISO 16654, FDA BAM, USDA) are reviewed to highlight the practical and regulatory requirements of these techniques. On this basis, the role of immunological detection technologies in on-site rapid testing is discussed, with a focus on improvements in sensitivity, specificity, and practicality. Finally, future directions are outlined, including multiplexed assays, integration with molecular biology techniques, and engineering applications of nanobody and recombinant technology. Full article