Search Results (10,109)

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

Biofouling remains a critical challenge in bioelectrochemical cells (BECs), hindering their efficiency and performance. This research article reviews advances in biofouling mitigation techniques within BEC systems during the period from 2019 to 2025, focusing on membrane modifications and electro-assisted membrane technologies. Through comprehensive analysis, it is revealed that Nafion alternatives, including ceramic membranes and recycled nonwoven fabrics like polypropylene, have emerged as significant contenders due to their combination of low cost and high performance. Additionally, the incorporation of silver, zeolite, and graphene oxide onto membranes has demonstrated efficacy in mitigating biofouling under laboratory conditions. Furthermore, the application of direct current electric fields has shown potential as a chemical-free preventative measure against biofouling in BECs. However, challenges related to long-term stability, scalability, and cost-effectiveness must be addressed for widespread adoption. Full article

Over the past decade, recurring influxes of pelagic Sargassum have posed significant environmental and economic challenges in the Caribbean Sea. Effective monitoring is crucial for understanding bloom dynamics and mitigating their impacts. This study presents a comprehensive machine learning (ML) and deep learning (DL) framework for detecting Sargassum and estimating its fractional cover using imagery from key satellite sensors: the Operational Land Imager (OLI) on Landsat-8 and the Multispectral Instrument (MSI) on Sentinel-2. A spectral library was constructed from five core spectral bands (Blue, Green, Red, Near-Infrared, and Short-Wave Infrared). It was used to train an ensemble of five diverse classifiers: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), a Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (1D-CNN). All models achieved high classification performance on a held-out test set, with weighted F1-scores exceeding 0.976. The probabilistic outputs from these classifiers were then leveraged as a direct proxy for the sub-pixel fractional cover of Sargassum. Critically, an inter-algorithm agreement analysis revealed that detections on real-world imagery are typically either of very high (unanimous) or very low (contentious) confidence, highlighting the diagnostic power of the ensemble approach. The resulting framework provides a robust and quantitative pathway for generating confidence-aware estimates of Sargassum distribution. This work supports efforts to manage these harmful algal blooms by providing vital information on detection certainty, while underscoring the critical need to empirically validate fractional cover proxies against in situ or UAV measurements. Full article

As cold spray additive manufacturing matures, significant efforts are being made to develop spray conditions for more challenging materials, thereby expanding the technology’s range of applications. One main challenge while using commercially available equipment is that, even under optimized conditions, deposition efficiency remains low for some materials. Powder particles that do not adhere are wasted, which can severely affect the process economics, especially in a mass production context and/or when expensive feedstocks are used. Powder recovery and reuse is a logical solution to mitigate this problem, yet few studies evaluate its feasibility and its impact on powder characteristics and ultimately coating performance. In this work, powder recovery was investigated for two cases: a Ni powder and a NdFeB-Al powder mix, used respectively for repair applications and for the fabrication of permanent magnets. A prototype recovery system was built, achieving a recovery efficiency of up to 75%. The powders were recovered after up to four spray runs, and their morphology and size distribution were characterized. The magnetic properties of both powders and coatings were evaluated using hysteresis measurements. Although the process affects the particle size distribution and their magnetic properties, powders remain suitable for re-deposition for both materials. In particular, it was shown that NdFeB-Al mix maintains 97% of its initial magnetic performance under industrial operating conditions. Full article

White Spot Syndrome Virus (WSSV) is one of the most devastating viral pathogens affecting shrimp, causing severe economic losses to the global farmed shrimp trade. The globalization of live shrimp trade and waterborne transmission have facilitated the rapid spread of WSSV across major shrimp-producing countries since its initial emergence. The present review gives an updated account of WSSV biology, pathology, transmission dynamics, and recent developments in control measures. The virus, a double-stranded DNA virus of the Nimaviridae family, utilizes advanced immune evasion strategies, resulting in severe mortality. Shrimp lack adaptive immunity and hence rely predominantly on innate immunity, which is insufficient to mount an effective response against severe infections. Traditional disease control measures such as augmented biosecurity, selective breeding, and immunostimulants have, despite extensive research, achieved only limited success. New biotechnological tools such as RNA interference, CRISPR-Cas gene editing, and nanotechnology offer tremendous potential for disease mitigation. In parallel, the development of DNA and RNA vaccines targeting WSSV structural proteins, such as VP28, holds significant promise for stimulating the shrimp immune system. This review highlights the urgent need for a convergent approach to sustainable disease management in global shrimp aquaculture, with interdisciplinarity playing a pivotal role in shaping the future of WSSV control. Full article

The system analysis method is suitable for detecting the optimal pathways for regional sustainable (e.g., green, low carbon) socioeconomic development. This study develops an inexact fractional energy–output–water–carbon nexus system planning model to minimize total carbon emission intensity (CEI, total carbon emissions/total economic output) under a set of nexus constraints. Superior to related research, the model (i) proposes a CEI considering both sectoral intermediate use (indirect) and final use (direct); (ii) quantifies the dependencies among energy, output, water, and carbon; (iii) restricts water utilization for carbon emission mitigation; (iv) adopts diverse mitigation measures to achieve carbon neutrality; (v) handles correlative chance-constraints and crisp credibility-constraints. A case in Fujian province (in China) is conducted to verify its feasibility. Results disclose that the total CEI would fluctuate between 45.05 g/CNY and 47.67 g/CNY under uncertainties. The annual total energy and total output would, on average, increase by 0.58% and 2.82%, respectively. Eight mitigation measures would be adopted to reduce the final carbon emission into the air to 0 by 2060. Compared with 2025, using water for carbon emission mitigation would increase 17-fold by 2060. For inland regions, authorities should incorporate other unconventional water sources. In addition, the coefficients of embodied energy consumption and water utilization are the most critical parameters. Full article

Vegetation fires are among the most common natural disasters, posing significant threats to people and the natural environment worldwide. Density-based clustering methods can be used to identify geospatial clustering patterns of fire points. It further helps reveal the spatial distribution characteristics of wildfires, which are crucial for regional-specific fire mapping, prediction, mitigation, and protection. DBSCAN (density-based spatial clustering of applications with noise) is widely used for clustering spatial objects. It needs two user-determined threshold values: the local radius and the minimum number of neighboring points for core points, which require user expertise and background information. This work proposes a dual-population genetic optimization to determine threshold values of DBSCAN for clustering vegetation fire points in western China. By constructing randomly generated threshold populations, optimized threshold values are obtained through crossover, mutation, and inter-population exchange, measured by multiple clustering metrics. Focusing on vegetation wildfires in western China during 2016–2022, the results reveal that vegetation wildfires can be divided into eight regions, each exhibiting distinct spatiotemporal patterns and geographic contexts. Full article

The sustainable management of water bodies with hydropower plants (HPPs) and protected rheophilic fish species is challenging. The key question is whether impounded rivers can still provide habitat for protected rheophilic fish species, including Natura 2000 species. We investigated hydro-morphological conditions and fish communities, focusing on the bottom-dwelling Danube longbarbel gudgeon (Romanogobio uranoscopus) and the medium-distance migrating cactus roach (Rutilus virgo) in the Brežice HPP system on the Sava River in Slovenia. Fish sampling using an electric bottom trawl in the HPP impoundment, electrofishing in the nearshore, and video surveillance in the fish pass revealed a diverse and distinctive fish community. This community reflected rheophilic conditions in the upper impoundment and fish pass, and lentic conditions in the lower impoundment. These findings provide evidence that impounded rivers, when complemented by well-designed mitigation measures, can sustain rheophilic fish species, including the Danube longbarbel gudgeon and cactus roach. Maintaining rheophilic habitat within the impoundment, combined with a functioning river-like side channel, is crucial. However, at Brežice HPP, changes in the management of the fish pass water inflow are necessary to ensure adequate and consistent hydraulic conditions and water temperatures. Applying a knowledge co-creation approach, which requires productive interaction among scientists, managers and policy makers, could help to find the best solutions for sustainable water ecosystem management. Full article

The ODYSEA (Ocean DYnamics and Surface Exchange with the Atmosphere) mission will provide simultaneous two-dimensional measurements of currents and winds for the first time. According to the ODYSEA radar concept, with a high incidence angle, current noise is primarily driven by backscattered power, which is triggered by wind speed. Therefore, random noise will affect the quality of observations. In low wind conditions, the absence of surface roughness increases the noise level considerably, to the point where the measurement becomes unusable, as the error can exceed 3 m/s at 5 km posting compared to mean current amplitudes of tens of cm/s. Winds higher than 7.5 m/s enable current measurements at 5 km posting with an RMS accuracy below 50 cm/s, but derivatives of currents will amplify noise, hampering the understanding of ocean dynamics and the interaction between the ocean and the atmosphere. In this context, this study shows the advantages and limitations of using noise-reduction algorithms. A convolutional neural network, a UNet inspired by the work of the SWOT (Surface Water and Ocean Topography) mission, is trained and tested on simulated radial velocities that are representative of the global ocean. The results are compared with those of classical smoothing: an Adaptive Gaussian Smoother whose filtering transfer function is optimized based on local wind speed (e.g., more smoothing in regions of low wind). The UNet outperforms the kernel smoother everywhere with our simulated dataset, especially in low wind conditions (SNR << 1) where the smoother essentially removes all velocities whereas the UNet mitigates random noise while preserving most of the signal of interest. Error is reduced by a factor of 30 and structures down to 30 km are reconstructed accurately. The UNet also enables the reconstruction of the main eddies and fronts in the relative vorticity field. It shows good robustness and stability in new scenarios. Full article

With the increasing demand for sustainable building materials, it is essential to investigate the durability of recycled fine aggregate concrete (RFAC) under corrosive environmental conditions. This study systematically assessed the performance of RFAC with three compressive strengths after dry–wet cycles in chloride and sulfate environments, respectively. The experimental program encompassed measurements of compressive strength, mass variation, porosity, ion penetration depth, and free ion content, complemented by comprehensive microstructural characterization. Results show that under sulfate exposure, 20 MPa and 40 MPa RFAC suffered significant strength losses of 60.1% and 18.0% after 70 cycles, while 60 MPa RFAC gained 2.5% strength. In chloride environments, 20 MPa and 40 MPa RFAC experienced strength reductions of 30.7% and 6.9%, whereas 60 MPa RFAC increased in strength by 6.6%. Compared to sulfate exposure, all groups exhibited slight mass increases or porosity reduction under chloride exposure, with high-strength RFAC showing the most noticeable densification. The chloride penetration depth in RFAC of 60 MPa was measured at 14.65 mm, representing a 41.0% reduction compared to RFAC of 20 MPa; sulfate penetration depth was 17.84 mm, which is 44.6% lower than that of the 20 MPa counterpart. Microstructural analysis revealed that sulfate-induced ettringite and gypsum formation triggered crack propagation, while chloride mainly affected pore structure through crystallization and filling, and the formation of C-S-H in high-strength RFAC inhibits pore expansion and mitigates deterioration. Full article

Urban green spaces provide measurable cooling that can mitigate urban heat islands, yet few studies have quantified these effects over multiple decades. This study analyzed Landsat imagery from four epochs (1990, 2000, 2013, 2018) to derive land surface temperature (LST) and vegetation indices—NDVI for greenness and NDMI for moisture content—for four large urban parks in Krakow. Late spring/summer LST in parks was compared with that of urban areas within 0–150 m and 150–300 m of park boundaries. Statistical significance was evaluated using bootstrapped confidence intervals, long-term trends were assessed via the Mann–Kendall test, and correlation analysis was used to examine relationships between LST and each vegetation index. Results show a persistent park cooling effect, with park interiors ~2–3 °C cooler than adjacent urban areas in all years. Despite an overall city-wide LST rise of ~5–6 °C from 1990 to 2018, the park cool island intensity (temperature difference between park and city) remained stable (no significant long-term trend, p > 0.7). Bootstrapped 95% confidence intervals confirmed that each park’s cooling effect was statistically significant in each year analyzed. NDMI (vegetation moisture content) correlated more strongly with LST (r ~ −0.90) than NDVI (r ~ −0.7 to −0.9), highlighting the importance of vegetation moisture in park cooling. These findings demonstrate that well-watered urban parks can sustain substantial cooling benefits over decades of urban development. The persistent ~2–3 °C daytime cooling observed underscores the value of water-sensitive green space planning as a long-term urban heat mitigation strategy. Full article

Fruit sunburn is a major abiotic stress limiting apple production worldwide, with losses potentially reaching 50% due to climate change-driven heat events. This study aimed to evaluate sustainable strategies to mitigate or reduce sunburn on ‘Gala Galaxy Selecta’ apple trees. Field trials conducted in summer 2021 compared eight treatments: silicon-based application (Eckosil^®), foliar fertilization with algae extracts, macro- and micronutrients, and amino acids, increased irrigation (+35% ET_c), mineral particle films (Surround^®, Vegepron Sun^®, Agrowhite^®, Sunstop^®), and an untreated control. Randomized block designs with replicates were used. Agronomic parameters, including particle film coverage, trunk cross-sectional area, yield, and fruit quality (color, sunburn incidence, firmness, soluble solids content, dry matter, starch), were measured at harvest. Physiological responses, such as net photosynthesis, maximum quantum yield of Photosystem II, specific leaf area, fruit surface temperature, photoprotective pigments, antioxidants, and heat shock protein gene expression, were also assessed. Foliar fertilization, Agrowhite^®, and water reinforcement produced the highest yield per trunk cross-sectional area, with increased soluble solids content and enhanced red pigmentation. Surround^® minimized sunburn incidence but reduced photosynthetic activity, as did Vegepron Sun^®. Agrowhite^® balanced sunburn protection with maintenance of fruit quality and physiological function. These findings provide practical guidance for growers to select effective treatments, balancing sunburn mitigation, fruit quality, and tree physiological performance, while offering researchers insights into integrating agronomic and physiological strategies for climate-resilient apple production. Full article

Air pollution, outstanding particulate matter (PM_2.5), poses severe risks to human health and the environment in densely populated urban areas. Accurate short-term forecasting of PM_2.5 concentrations is therefore crucial for timely public health advisories and effective mitigation strategies. This work proposes a hybrid approach that combines machine learning models with STL decomposition to provide precise short-term PM_2.5 predictions. Daily PM_2.5 series from four major Pakistani cities—Islamabad, Lahore, Karachi, and Peshawar—are first pre-processed to handle missing values, outliers, and variance instability. The data are then decomposed via seasonal-trend decomposition using Loess (STL), which explicitly exploits the symmetric and recurrent structure of seasonal patterns. Each decomposed component (trend, seasonality, and remainder) is modeled independently using an ensemble of statistical and machine learning approaches. Forecasts are combined through a weighted aggregation scheme that balances bias–variance trade-offs and preserves the distributional consistency. The final recombined forecasts provide one-day-ahead PM_2.5 predictions with associated uncertainty measures. The model evaluation employs multiple statistical accuracy metrics, distributional diagnostics, and out-of-sample validation to assess its performance. The results demonstrate that the proposed framework consistently outperforms conventional benchmark models, yielding robust, interpretable, and probabilistically coherent forecasts. This study demonstrates how periodic and recurrent seasonal structure decomposition and probabilistic ensemble methods enhance the statistical modeling of environmental time series, offering actionable insights for urban air quality management. Full article

Shallow-buried, closely spaced tunnels under bias loading often encounter stability challenges due to excavation-induced interaction effects. These effects are particularly significant in the middle rock pillar zone. To evaluate the influence of face lag distance on tunnel stability, the Georgia No. 1 Tunnel was selected as a case study. Numerical simulations and field monitoring were combined to analyze the deformation and stress evolution under different face lag distances. The analysis focused on ground surface settlement, vault displacement, and tunnel clearance convergence. The results indicate that ground surface settlement decreases notably as the face lag distance increases. When the face lag distance increased from 0.5 D to 2.0 D, the maximum settlement decreased by about 11.9%, with the absolute maximum measured value of approximately 3.48 mm. Stress concentration occurred mainly within 15 m behind the excavation face, suggesting that a face lag distance exceeding this range can effectively mitigate tunnel interaction effects. The biased tunnel side experienced greater vault settlement and convergence, requiring closer monitoring. An insufficient face lag distance amplifies deformation superposition, whereas an excessive one causes additional horizontal fluctuations. For the geological and structural conditions of the Georgia No. 1 Tunnel, a face lag distance of approximately 2.0 D provides an optimal balance between stability, safety, and construction efficiency. These findings offer practical guidance for the design and safe construction of shallow-buried twin tunnels under bias loading. Full article

The Ti6Al4V alloy is considered the most beneficial of the titanium alloys for use in biomedical applications. However, it corrodes when exposed to various biocompatible fluids. This investigation aims to evaluate the corrosion inhibition performance of the Ti6Al4V in a saline solution (SS) using thiocolchicoside (TCC) drug as an environmentally acceptable corrosion inhibitor. The corrosion assessments were conducted using potentiodynamic polarization curves (PPCs), open-circuit potential (OCP), and electrochemical impedance spectroscopy (EIS) methodologies, supplemented by scanning electron microscopy (SEM), energy-dispersive X-ray (EDS) analysis, atomic force microscopy (AFM), and contact angle (CA) measurements. The outcomes indicated that the inhibitory efficacy improved with higher TCC concentrations (achieving 92.40% at 200 mg/L of TCC) and diminished with an increase in solution temperature. TCC’s physical adsorption onto the surface of the Ti6A14V, which adheres to the Langmuir adsorption isotherm, explains its mitigating power. The TCC acts as a mixed-type inhibitor. The adsorption and inhibitory impact of TCC were examined at various temperatures using PPC and EIS. When TCC is present, the corrosion’s apparent activation energy is higher (35.79 kJ mol⁻¹) than when it is absent (14.46 kJ mol⁻¹). In addition, the correlation between the structural properties of thiocolchicoside (TCC) and its corrosion inhibition performance was systematically analyzed. Density Functional Theory (DFT) calculations were utilized to characterize the adsorption mechanism, supported by Natural Bond Orbital (NBO) analysis and Molecular Dynamics (MD) simulations. The combined computational and electrochemical findings confirm that TCC provides effective and enhanced corrosion protection for the Ti6Al4V alloy in a saline environment. These characteristics provide compelling evidence for the suitability of these pharmaceutical compounds as promising corrosion inhibitors. Full article