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Sustainable agriculture is an important conception to meet the growing food demand of the global population. The increased need for adequate and safe food, as well as the ongoing ecological destruction associated with conventional agriculture practices are key global challenges. Nanomaterials are being developed in the agriculture sector to improve the growth and protection of crops. Among the various engineered nanomaterials, carbon nanotubes (CNTs) are one of the most promising carbon-based nanomaterials owing to their attractive physiochemical properties such as small size, high surface area, and superior mechanical and thermal strength, offering better opportunities for agriculture sector applications. This review provides basic information about CNTs, including their history; classification; and electrical, thermal, and mechanical properties, with a focus on their applications in the agriculture field. Furthermore, the mechanisms of the uptake and translocation of CNTs in plants and their defense mechanisms against environmental stresses are discussed. Finally, the major shortcomings, threats, and challenges of CNTs are assessed to provide a broad and clear view of the potential and future directions for CNT-based agriculture applications to achieve the goal of sustainability.

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner. The ability to predict the survival and response to treatment of cancer patients may improve patient care. Here, the authors generate a 32 gene signature that can predict the survival and response to treatment in gastric cancer patients.

In this paper, numerical studies were carried out on scour around a subsea pipeline. A coupled solver between computational fluid dynamics (CFD) and discrete element method (DEM) was selected to simulate fluid flow and particle interactions. To select and validate the numerical model parameters in the solver, angles of repose and incipient motion were simulated. From the validation studies, the selected coefficient of rolling friction with spherical particles could predict the behavior of non-spherical particles. The fluid flow around the subsea pipeline was simulated, and the motion of individual soil particles was tracked. Particle motions were generated by the drag force, due to a high velocity. Three scour development process, such as onset of scour, tunnel erosion, and lee-wake erosion, were studied and discussed. The scour depth evolution showed good agreement with the experimental data. It was confirmed that the selected solver, with numerical model parameters, predicted the scour process around a subsea pipeline well.

To ensure the long-term reliability of tidal stream turbines, waves and currents must be considered during the design phase. In this paper, a three-bladed horizontal axis turbine with a diameter of 0.9 m was used as the baseline model. OpenFOAM, an open-source computational fluid dynamics (CFD) library platform, was used to predict the performance of a horizontal axis tidal stream turbine (HATST) under waves and currents. A mesh dependency test was carried out to select the optimal mesh to capture the flow’s features. As a validation study, the power of the turbine under only the current was predicted and was found to be consistent with the experimental results. The generated wave profile under a current was compared with the results obtained using the third-order Stokes wave theory. The performance of the HATST was predicted for various wave frequencies and heights and compared with experimental data. The effect of the wave height on the power performance was greater than the wave frequency. Vortical flow structures behind the turbine were investigated for various wave conditions. The generated tip vortices propagated upward and downward at wave crest and trough conditions, respectively.

The control and promotion of plant and crop growth are important challenges globally. In this study, we have developed a nanomaterial-assisted bionic strategy for accelerating plant growth. Although nanomaterials have been shown to be toxic to plants, we demonstrate herein that graphene oxide can be used as a regulator tool for enhancing plant growth and stability. Graphene oxide was added to the growth medium of Arabidopsis thaliana L. as well as injected into the stem of the watermelon plant. We showed that with an appropriate amount provided, graphene oxide had a positive effect on plant growth in terms of increasing the length of roots, the area of leaves, the number of leaves, and the formation of flower buds. In addition, graphene oxide affected the watermelon ripeness, increasing the perimeter and sugar content of the fruit. We believe that graphene oxide may be used as a strategy for enabling the acceleration of both plant growth and the fruit ripening process.

Cavitation is a common phenomenon in naval and ocean engineering, typically occurring in the wakes of high-speed rotating propellers and on the surfaces of fast-moving underwater vehicles. To investigate cavitation phenomena, computational fluid dynamics (CFD) simulations are indispensable. Nevertheless, the inherently complex nature of cavitation, which involves phase transitions, heat transfer, and significant pressure fluctuations, often results in high computational costs for these simulations. To address the computational challenges associated with cavitation simulations, a DeepCFD model, which leverages convolutional neural networks (CNNs), was employed to accurately predict cavitation around hydrofoils. Through specific modifications, the DeepCFD model was trained on 400 hydrofoil configurations, learned from CFD simulations. The numerical methods were validated against a modified NACA66 hydrofoil. It was found that the model could accurately predict cavitation shapes under various flow conditions, although it showed some discrepancies in velocity predictions, especially for detached cavitating flows. The significance of this study lies in its potential to simply predict cavitating flows and expedite marine vehicle design through the application of CNNs in cavitation prediction, offering a novel and impactful approach to computational fluid dynamics in the field.

When tidal turbines are deployed in water areas with significant waves, assessing the surface wave effects becomes imperative. Understanding the dynamic impact of wave–current conditions on the fluid dynamic performance of tidal turbines is crucial. This paper aims to establish a fundamental understanding of the influence of surface waves on tidal turbines. OpenFOAM, an open-source computational fluid dynamics (CFD) library platform, is utilized to predict the performance of current turbine under waves and currents. This research investigates the effects of two critical wave parameters, wave height and wavelength, on the fluid dynamics and wake structures of current turbine. Additionally, this study explores the influence of various submersion depths on turbine performance. The findings indicate that, under various wave conditions, the turbine’s average power coefficient remains constant, but significant fluctuations are shown. Increasing submersion depth can mitigate the impact of waves. However, in regions characterized by longer wavelengths, altering the submersion depth has limited effects on turbine performance.

Biological and abiotic stresses in plant growth are associated with reduced crop yields. Therefore, improving plant stress resistance can be a crucial strategy to improve crop production. To overcome these problems, plant growth-promoting bacteria are emphasized as one of the alternative tools for sustainable agriculture. This study found a novel strain (L3 T ) of a plant growth-promoting bacterium in fermented Liriope platyphylla fruit. Strain L3 T showed the ability to promote plant growth. The L3 T strain promoted plant growth of D. carota subsp. sativus , increasing the length (increase rate compared to the control group, 36.98%), diameter (47.06%), and weight of carrots (81.5%), ultimately increasing the edible area. In addition, we confirmed that plant growth was improved even in situations that inhibited plant growth, such as salinity and drought stress. Strain L3 T performed indole production, siderophore production, phosphate solubilization, and nitrogen fixation, all characteristics of a strain that promotes plant growth. Genome analysis revealed genes involved in the growth promotion effects of strain L3 T . Additionally, the properties of exopolysaccharides were identified and characterized using FTIR, TGA, and UHPLC. Our results demonstrated that L3 isolated from fermented L . platyphylla fruit can be used to simultaneously alleviate drought and NaCl stress.

With the implementation of the energy efficiency design index (EEDI) by the International Maritime Organization (IMO), the goal of which is to reduce greenhouse gas (GHG) emissions, interest in energy saving devices (ESDs) is increasing. Among such ESDs are air lubrication methods, which reduce the frictional drag of ships by supplying air to the hull surface. This is one of the efficient approaches to reducing a ship’s operating costs and making it environmentally friendly. In this study, the air lubrication method on a flat plate was studied using computational fluid mechanics (CFD). OpenFOAM, the open-source CFD platform, was used. The coupled level-set and volume of fluid (CLSVOF) solver, which combines the advantages of the level-set method and the volume of fluid method, was used to accurately predict the air and water interface. Rayleigh–Taylor instability was simulated to verify the CLSVOF solver. The frictional drag reduction achieved by the air lubrication of the flat plate at various injected airflow rates was studied, and compared with experimental results. The characteristics of the air and water interface and the main factors affecting the cavity formation were also investigated.

As global warming accelerates, the Paris Agreement has emphasized the urgent need for technologies that reduce and manage carbon dioxide emissions. Consequently, carbon capture and storage (CCS) has emerged as a critical area of research. For the safe and efficient transportation of captured carbon dioxide in cryogenic tanks, the design must accurately account for the phase change behavior of liquefied carbon dioxide (LCO2). This study proposes a numerical approach to evaluate the thermal insulation performance of cryogenic tanks by simulating the phase change process of LCO2. The phase transition of LCO2 was simulated in a horizontally oriented Type-C cryogenic tank using the open-source computational fluid dynamics (CFD) framework OpenFOAM (v2312). To validate the numerical methodology, the phase change in liquefied nitrogen (LN2) inside a tank was first simulated and compared with available experimental data. A mesh-independence study was then conducted to determine the optimal grid resolution, and the effects of different equations of state (EOS) for both liquid and gaseous phases, as well as various turbulence models, were examined. The boil-off rate (BOR) and boil-off gas (BOG) generation within the tank were predicted, and variations in internal pressure and flow fields were analyzed. The simulation results over 5000 s showed that the internal tank pressure increased from 7.8 bar to 8.1 bar, and the average temperature rose by approximately 1.3 K. The total mass of LCO2 decreased from 1439.3 kg to 1431.0 kg.