Search Results (85)

There are discrepancies regarding the effectiveness of enhanced efficiency nitrogen (N) fertilizer (EENF) products on ammonia loss from unincorporated, surface applications of urea-based fertilizers. Soil properties and management practices may account for the differences in the performance of EENF. However, few studies have investigated the performance of urea- and urea ammonium nitrate (UAN)-based EENF on soils with contrasting properties. Controlled-environment incubation experiments were conducted on two soils with different properties to evaluate the efficacy of urea and UAN forms of EENF to minimize ammonia volatilization losses. The experiments were set up as a completely randomized design, with seven treatments replicated four times for 16 days. The N treatments, which were surface-applied at 134 kg N ha⁻¹, included untreated urea, untreated UAN, urea+ANVOL^TM (urease inhibitor product), UAN+ANVOL^TM, environmentally smart nitrogen (ESN^®), SUPERU^® (urease and nitrification inhibitor product), and urea+Excelis^® (urease and nitrification inhibitor product). In this study, urea was more susceptible to ammonia loss (24.12 and 26.49% of applied N) than UAN (5.24 and 16.17% of applied N), with lower ammonia volatility from soil with a pH of 5.8 when compared to 7.0. Urea-based EENF products performed better in soil with a pH of 5.8 compared to the soil with pH 7.0, except for ESN, which was not influenced by pH. In contrast, the UAN-based EENF was more effective in the high-pH soil (7.0). Across both soils, all EENFs reduced cumulative ammonia loss by 32–91% in urea and 27–70% in UAN, respectively, when compared to their untreated forms. The urea-based EENF formulations containing both nitrification and urease inhibitors were the least effective among the EENF types, performing particularly poorly in high-pH soil (pH 7.0). In conclusion, the efficacy of EENF is dependent on soil pH, N source, and the form of EENF. These findings underscore the importance of tailoring EENF applications to specific soil conditions and N sources to optimize N use efficiency (NUE), enhance economic returns for producers, and minimize environmental impacts. Full article

Metaconcrete (MC) is a class of engineered cementitious composites that integrates locally resonant inclusions to filter stress waves. While the dynamic benefits are well established, the effect of resonator content and geometry on static compressive resistance remains unclear. This study develops the first two-dimensional mesoscale finite-element model that explicitly represents steel cores, rubber coatings, and interfacial transition zones to predict the quasi-static behavior of MC. The model is validated against benchmark experiments, reproducing the 56% loss of compressive strength recorded for a 10.6% resonator volume fraction with an error of less than 1%. A parametric analysis covering resonator ratios from 1.5% to 31.8%, diameters from 16.8 mm to 37.4 mm, and coating thicknesses from 1.0 mm to 4.2 mm shows that (i) strength decays exponentially with volumetric content, approaching an asymptote at ≈20% of plain concrete strength; (ii) larger cores with thinner coatings minimize stiffness loss (<10%) while limiting strength reduction to 15–20%; and (iii) material properties of the resonator have a secondary influence (<6%). Two closed-form expressions for estimating MC strength and Young’s modulus (R² = 0.83 and 0.94, respectively) are proposed to assist with the preliminary design. The model and correlations lay the groundwork for optimizing MC that balances vibration mitigation with structural capacity. Full article

Water-quality monitoring in agricultural irrigation systems is challenging due to the dynamic and heterogeneous nature of mixed water sources, which complicates traditional and remote sensing-based assessment methods. Traditional water quality monitoring relies on water sampling and laboratory analysis, which can be time-consuming, labor-intensive, and spatially limited. In situ hyperspectral reflectance sensing (HRS) presents a promising alternative, offering high-resolution, non-invasive monitoring capabilities. However, applying HRS in mixed-water environments—where served-water effluent, precipitation, and natural river water converge—presents significant challenges due to variability in water composition and environmental conditions. While HRS has been widely explored in controlled or homogeneous water bodies, its application in highly dynamic agricultural mixed-water systems remains understudied. This study addresses this gap by evaluating the relationships between in situ hyperspectral data (450–900 nm) and key water-quality parameters—pH, turbidity, nitrates, and chlorophyll-a—across three campaigns in a Colombian tropical agricultural irrigation system. A Pearson’s correlation analysis revealed the strongest spectral associations for nitrates, with positive correlations at 500 nm (r ≈ 0.76) and 700 nm (r ≈ 0.85) and negative correlations in the near-infrared (850 nm, r ≈ −0.88). Conversely, the pH exhibited weak and diffuse correlations, with a maximum of r ≈ 0.51. Despite their optical activity, turbidity and chlorophyll-a showed unexpectedly weak correlations, likely due to the optical complexity of the mixed water matrix. Random Forest regression identified key spectral regions for each parameter, yet model performance was limited, with R² values ranging from 0.51 (pH) to −1.30 (chlorophyll-a), and RMSE values between 0.41 and 1.51, reflecting the challenges of predictive modeling in spatially and temporally heterogeneous wastewater systems. Despite these challenges, this study establishes a baseline for future hyperspectral applications in complex agricultural water monitoring and highlights critical spectral regions for further investigation. To improve the feasibility of HRS in mixed-water assessments, future research should focus on enhancing data-preprocessing techniques, integrating complementary sensing modalities, and refining predictive models to better account for environmental variability. Full article

Increasing wheat (Triticum aestivum L.) grain protein concentration (GPC) without excessive nitrogen (N) inputs requires understanding the interactions between N source characteristics and application technology parameters. This study evaluated the effects of foliar N applications at anthesis on wheat grain yield and GPC across three locations over three growing seasons in Oklahoma. Treatments consisted of two N sources (urea-ammonium nitrate [UAN] and aqueous urea [Aq. urea]), three nozzle types (flat fan [FF], 3D, and twin [TW]), and two droplet types (fine and coarse). Late foliar applications increased GPC by 12% without affecting grain yield (0.5–5.8 Mg ha⁻¹). During the 2020–21 growing season, a late season freeze during anthesis resulted in no significant differences in GPC across locations. UAN produced significantly higher GPC (13.7%) than Aq. urea (13.1%). Among nozzle types, the 3D nozzle consistently produced the highest GPC (13.8%), compared to FF (13.1%) and TW nozzles (13.2%). Two-way interactions revealed UAN with fine droplets achieved consistently high GPC (14.6%), as did Aq. urea with coarse droplets (14.5%) at Lake Carl Blackwell in 2021–22 as compared to Aq. Urea_Fine (13.8%). At Chickasha 2021–22 and Perkins 2020–21, a significant three-way interaction was observed, with the UAN_3D_Fine (13.2%) and UAN_3D_Coarse (12.2%) treatments producing the highest GPC, with 8% and 15% greater than the Aq. Urea_TW_Fine, respectively, which is lowest. These findings provide a foundation for precision agriculture approaches that optimize foliar N application parameters to enhance wheat quality while maintaining sustainable production practices. Full article

In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization. Full article

Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric deviations. These defects significantly impact the fatigue life of the material by acting as stress concentrators that accelerate failure under cyclic loading. On the one hand, this type of model is very complicated in its approach, since, even with encouraging results, the complexity of the calculation with these variables makes it difficult to obtain a simple result that allows for a generalized interpretation. On the other hand, using more familiar methods, it is possible to qualitatively guess the behavior that helps obtain results with better applicability, even at limited levels of precision. This paper presents a simplified finite element method combined with a statistical approach to model the presence of porosity in metal components produced by additive manufacturing. The proposed model considers a two-dimensional square plate subjected to tensile stress, with randomly introduced defects characterized by size, shape, and orientation. The percentage of porosity that affects each aspect determines the adjustment of the mechanical properties of finite elements. A series of simulations were performed to generate multiple models with random defect distributions to estimate maximum stress values. This approach demonstrates that complex models are not always necessary for a preliminary practical estimate of the effects of new manufacturing techniques. Furthermore, it demonstrates the potential for the extension of frugal computational techniques, which aim to minimize computational and experimental costs in the engineering field. The article discusses future research directions, particularly those related to potential business applications, including commercial uses. This follows a discussion of the existing limitations of this study. Full article

Background. Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton. Methods. Five PwST were recruited in this pilot cross-sectional study. We observed three experimental conditions: walking without and with the UAN.GO exoskeleton and walking with the UAN.GO combined with the OPTIGO walker. Each condition included five trials related to ADLs such as sitting and walking. Results. No statistically significant difference was found between heart rate and R–R of ECG data while comparing all the observed conditions during each respective trial. The NASA Task Load Index did not show significant differences across all trials, except for a significant difference between Condition 2 and Condition 3 in Trial 4 (p = 0.043). However, walking and sit-to-stand tasks seem to be more challenging according to the NASA-TLX. Only one participant scored over 70 points on the System Usability Scale. The TSQ-WT scores for conditions 2 and 3 were 62 (56.5–72.5) and 70 (66.5–75) points, respectively. Conclusions. This study suggests that UAN.GO exoskeleton could be used for RAGT in PwST with disability without compromising cardiovascular function. Full article

Enhanced efficiency nitrogen fertilizer (EENF) is a viable N management tool that can minimize N loss and maximize crop productivity. Research on the efficiency of EENF under relatively newer N-sidedress placement methods for liquid fertilizers, such as Y-Drop dribble, is limited, hence the necessity to address this knowledge gap. A two-year field trial was conducted to evaluate ammonia volatilization, grain quality, and grain yield from surface-applied EENF forms of urea and EENF forms of urea ammonium nitrate (UAN) applied under four placement methods in a rainfed no-till corn production system. The EENF forms of urea evaluated in this trial included Environmentally Smart Nitrogen, SuperU, and ANVOL-treated urea, while ANVOL-treated UAN was the EENF form of UAN. All the urea fertilizers were surface broadcasted while the UAN fertilizers were broadcasted, dribbled in between rows, Y-Drop dribbled, and injected behind colters. Among the non-EENF, urea volatilized the most ammonia (23.1% of total N applied) compared to UAN. Broadcast UAN was much more susceptible to ammonia volatilization than dribbled UAN in between rows. In this trial, the EENF forms of urea and UAN reduced ammonia loss by 65 to 94 and 50 to 51%, respectively, compared to their corresponding non-EENF forms. In 2021, the grain yield of the non-EENF forms of UAN was similar to the EENF forms of the corresponding placement methods. In contrast, there was an additive yield effect from all the urea forms of EENF. A significant negative relationship was observed between ammonia loss and grain protein, grain N, and grain yield; hence, placement methods with higher ammonia loss potential are prone to yield loss. Full article

A secure and effective authentication and communication scheme between users and underwater sensors plays an important role in improving the detection and utilization of marine resources in underwater acoustic networks (UANs). However, due to the energy limitations and susceptibility to capture of underwater sensors and gateways, it is necessary to design a lightweight authentication protocol that can resist capture of sensors and gateways during attacks. In this paper, a lightweight authentication protocol for UANs based on the Physical Unclonable Function (PUF) and chaotic map is proposed. We used the advantages of PUF to resist sensors and gateways being captured in attacks and the chaotic map to achieve lightweight authentication because the computational cost of the chaotic map is almost one-third that of Elliptic Curve Cryptography (ECC). Additionally, we used the formal security proof in the random oracle model to prove the security of the proposed scheme. Our scheme was more secure and efficient compared with some other related schemes in terms of security and performance requirements, and the proposed scheme is suitable for UANs. Full article

The following research hypotheses were established in this study: the applied urea and ammonium nitrate solution with potassium thiosulfate (UAN-KTS) has a positive effect on the chemical composition of spring wheat, spring rape and maize; fertilization with nitrogen, potassium and sulfur increases their contents in the usable parts of plants; the forms of applied fertilizers reduce the antagonistic effect of nitrogen and potassium on the content of other elements in plants. Two doses of nitrogen (N 1—optimal dose of nitrogen for a plant species; N 2—25% lower dose of nitrogen) and different ratios of N:K:S elements (N:K:S—without K and S; N:K₁:S₁—a narrowed ratio; N:K₂:S₂—the optimal ratio; N:K₃:S₃—an expanded ratio) were applied. The experiment was carried out on two soils of different quality. The improved soil quality resulted in a significant increase in the calcium (as opposed to magnesium) content of the three crops, in the nitrogen and sulfate–sulfur (VI) content of spring wheat grain and spring rapeseed, and in the phosphorus content of spring rapeseed and maize aerial parts. Reducing the dose (N 2) had a negative effect on the nitrogen content of spring wheat and on the sulfate–sulfur (VI) content of spring rape, maize and especially spring wheat. Increasing the N:K:S ratio had a small but usually antagonistic effect on the nitrogen content of all plant species, but a synergistic one on the potassium content of spring wheat, maize and partly of spring rape, and especially on the content of sulfate–sulfur (VI) in the crops. The effect of type of soil and fertilizers with different N:K:S ratios on the content of other macronutrients was related to plant species. The new fertilizer with the N:K₂:S₂ ratio had the greatest effect on the content of the tested macronutrients in spring wheat, spring rape and maize. In order to confirm the obtained relationships, it seems justified to carry out field experiments and studies with other plant species. Full article

Due to the open underwater channels and untransparent network deployment environments, underwater acoustic networks (UANs) are more vulnerable to hostile environments. Security research is also being conducted in cryptography, including authentication based on asymmetric algorithms and key distribution based on symmetric algorithms. In recent years, the advancement of quantum computing has made anti-quantum attacks an important issue in the field of security. Algorithms such as lattice and SPHINCS+ have become a research topic of interest in the field of security. However, within the past five years, few papers have discussed security algorithms for UANs to resist quantum attacks, especially through classical algorithms. Some existing classical asymmetric and symmetric algorithms are considered to have no prospects. From the perspective of easy deployment in engineering and anti-quantum attacks, our research focuses on a comprehensive lightweight security framework for data protection, authentication, and malicious node detection through the Elliptic Curve and Hash algorithms. Our mechanism is suitable for ad hoc scenarios with limited underwater resources. Meanwhile, we have designed a multi-party bit commitment to build a security framework for the system. A management scheme is designed by combining self-certifying with the threshold sharing algorithm. All schemes are designed based on certificate-less and ad hoc features. The proposed scheme ensures that the confidentiality, integrity, and authentication of the system are well considered. Moreover, the scheme is proven to be of unconditional security and immune to channel eavesdropping. The resource and delay issues are also taken into consideration. The simulations considered multiple variables like number of nodes, attackers, and message length to calculate proper values that can increase the efficiency of this scheme. The results in terms of delay, delivery ratio, and consumption demonstrate the suitability of the proposal in terms of security, especially for malicious node detection. Meanwhile, the computational cost has also been controlled at the millisecond level. Full article

In the cultivation of crops in recent times, in addition to taking care of the balanced supply of nutrients to plants and the protection of soil resources, it is also important to take into account the non-productive factor by implementing production systems based on balanced fertilisation. The aim of this study was to demonstrate the effect of soil kind and the application of a new fertiliser based on a urea and ammonium nitrate solution with potassium thiosulphate (UAN-KTS) on the yielding and biometric characteristics of spring wheat, spring rape, and maize to determine the optimal N:K:S ratio. An increase in the soil kind increased the weight of 1000 spring rape seeds, the yield of maize fresh matter, a reduction in the height of spring wheat and maize plants, a reduction in the yield of spring wheat grains and straw, and the yield of the fresh and dry matter of spring rape straw. A higher nitrogen dose promoted the growth of spring rape at the later growing stage and maize and had a positive effect on the yield of spring wheat grains and straw, spring rape seeds and straw, and the above-ground parts of maize. The application of potassium fertilisers caused a significant increase in the spring rape plant height, an increase in the yield of spring wheat grains and straw and spring rape seeds and straw, the above-ground parts of maize, a reduction in the plant height at the beginning of the spring wheat growing period, and a reduction in the weight of 1000 spring rape seeds (only on the soil with rich quality). The fertiliser with the N:K₂:S₂ ratio had a particularly favourable effect on the yielding of spring wheat. In the cultivation of maize, the same effect was most often obtained under the influence of fertiliser in the ratio of N:K₁:S₁ on the weaker soil fertilised at the same time as a higher dose of nitrogen (N 1) and N:K₂:S₂ (in other cases). In the case of spring rape, generally, fertiliser with N:K₁:S₁ was the strongest, although, in some objects, a higher yield was achieved under the influence of N:K₂:S₂. The existence of statistically confirmed correlations (expressed as the percentage of the variability observed) between the soil kind and the fertilisers applied and the yielding and biometric characteristics of the plants were observed. Full article

Early and accurate disease diagnosis is pivotal for effective phytosanitary management strategies in agriculture. Hyperspectral sensing has emerged as a promising tool for early disease detection, yet challenges remain in effectively harnessing its potential. This study compares parametric spectral Vegetation Indices (VIs) and a nonparametric Gaussian Process Classification based on an Automated Spectral Band Analysis Tool (GPC-BAT) for diagnosing plant bacterial diseases using hyperspectral data. The study conducted experiments on tomato plants in controlled conditions and kiwi plants in field settings to assess the performance of VIs and GPC-BAT. In the tomato experiment, the modeling processes were applied to classify the spectral data measured on the healthy class of plants (sprayed with water only) and discriminate them from the data captured on plants inoculated with the two bacterial suspensions (10⁸ CFU mL⁻¹). In the kiwi experiment, the standard modeling results of the spectral data collected on nonsymptomatic plants were compared to the ones obtained using symptomatic plants’ spectral data. VIs, known for their simplicity in extracting biophysical information, successfully distinguished healthy and diseased tissues in both plant species. The overall accuracy achieved was 63% and 71% for tomato and kiwi, respectively. Limitations were observed, particularly in differentiating specific disease infections accurately. On the other hand, GPC-BAT, after feature reduction, showcased enhanced accuracy in identifying healthy and diseased tissues. The overall accuracy ranged from 70% to 75% in the tomato and kiwi case studies. Despite its effectiveness, the model faced challenges in accurately predicting certain disease infections, especially in the early stages. Comparative analysis revealed commonalities and differences in the spectral bands identified by both approaches, with overlaps in critical regions across plant species. Notably, these spectral regions corresponded to the absorption regions of various photosynthetic pigments and structural components affected by bacterial infections in plant leaves. The study underscores the potential of hyperspectral sensing in disease diagnosis and highlights the strengths and limitations of VIs and GPC-BAT. The identified spectral features hold biological significance, suggesting correlations between bacterial infections and alterations in plant pigments and structural components. Future research avenues could focus on refining these approaches for improved accuracy in diagnosing diverse plant–pathogen interactions, thereby aiding disease diagnosis. Specifically, efforts could be directed towards adapting these methodologies for early detection, even before symptom manifestation, to better manage agricultural diseases. Full article

Due to the space–time coupling access, we find that anti-eavesdropping opportunities exist in underwater acoustic networks (UANs), where packets can be successfully received only by the intended receiver, but collide at the unintended receivers. These opportunities are highly spatially dependent, and this paper studies the case that linearly deployed sensor nodes directly report data toward a single collector. We found an eavesdropping ring centered around these linearly deployed sensor nodes, where the eavesdropper could steal all the reported data. Since the typical receiving-alignment-based scheduling MAC (TRAS-MAC) will expose the relative spatial information among the sensor nodes with the collector, the eavesdropper can locate the eavesdropping ring. Although moving the collector into the one-dimensional sensor node chain can degrade the eavesdropping ring to a point that constrains the eavesdropping risk, the collector’s location will be subsequently exposed to the eavesdropper. To efficiently protect the reported data and prevent the exposure of the collector’s location, we designed a slotted- and receiving-alignment-based scheduling MAC (SRAS-MAC). The NS-3 simulation results showed the effectiveness of the SRAS-MAC and the TRAS-MAC in protecting data from eavesdropping, which protect 90% of the data from eavesdropping in the one-eavesdropper case and up to 80% of data from eavesdropping in ten-eavesdropper cases. Moreover, unlike TRAS-MAC, which will expose the collector’s location, SRAS-MAC provides multiple positions for the collector to hide, and the eavesdropper cannot distinguish where it is. Full article

Carbon and nitrogen compounds in agroecosystems have attracted much attention in recent years due to their key roles in crop production and their impacts on environment quality and/or climate change. Since fertilization profoundly disrupted the C and N cycles, several mitigation and/or adaptation strategies, including the application of farmyard manure (FYM) and/or urease and nitrification inhibitors (UI and NI), have been developed. The aim of this study was to evaluate the contents of soil organic carbon and its fractions, the total and mineral forms of nitrogen, as well as CO₂ and N₂O emissions under mineral and organic fertilization with and without urease and nitrification inhibitors in a maize agroecosystem. A two-year field study was carried out on Cambisols (silt) in Poland. The experiment scheme included nine treatments: C (the control without fertilization), UAN (Urea Ammonium Nitrate), UAN+UI, UAN+NI, UAN+UI+NI, FYM with N mineral fertilizer base, FYM with N mineral fertilizer base+UI, FYM with N mineral fertilizer base+NI, and FYM with N mineral fertilizer base+UI+NI. It was found that treatments fertilized with cattle FYM were higher sinks and sources of C and N compounds in comparison to the UAN plots. The organic carbon, humic and humin acid, and total nitrogen concentrations, in contrast to ammonium and nitrate nitrogen, were not affected by the inhibitors added. Nitrification and urease inhibitors were effective in decreasing N₂O emissions only in treatments that were exclusively applied with UAN and had no significant influence on CO₂ emissions. Full article