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Quality is a key factor restricting the development and economic benefits of the tomato processing industry, and improving the quality of tomatoes has become a hotspot in the development of the tomato processing industry in Xinjiang. Regulated deficit irrigation (RDI) is an abiotic means of crop yield and quality control widely used for crop yield and quality improvement. This study aimed to investigate the impact of RDI on the dynamics of quality changes in processing tomato fruits during ripening via a 2-year (2022–2023) filed experiment with five water irrigation treatments in Xinjiang, China. The results showed that compared with conventional irrigation, regulated deficit irrigation significantly saved 315–1260 m3 ha−1 irrigation water. Mild RDI increased the single fruit weight and fruit hardness by 0.15 % and 3.29 kg cm2, respectively, thus improved the yield and storage and transportation quality of processed tomatoes. Moderate RDI increased the contents of soluble solid, soluble sugar and lycopene in fruit to 0.6 %, 0.56 % and 3.53 μg/g, respectively, therefore significantly improved the nutritional quality and flavor quality of processed tomato. Ultimately, a comprehensive evaluation using a coupled Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model, taking into account the appearance, nutrition, flavor, and storage and transportation indexes of processed fruits, concluded tha W1 treatment is a sustainable water management approach that balances yield and quality. Therefore, the optimal deficit irrigation model for processing tomato fruits in Xinjiang was recommended to be the W1 treatment, whereas the W2 treatment was considered as an alternate model. The study supported the large-scale development of the tomato processing industry in Xinjiang and the implementation of effective water-saving farming.

Pepper is a globally cultivated vegetable known for its distinct pungent flavor, which is derived from the presence of capsaicinoids, a class of unique secondary metabolites that accumulate specifically in pepper fruits. Since the accumulation of capsaicinoids is influenced by various factors, it is imperative to comprehend the metabolic regulatory mechanisms governing capsaicinoids production. This review offers a thorough examination of the factors that govern the metabolism of capsaicinoids in pepper fruit, with a specific focus on three primary facets: (1) the impact of genotype and developmental stage on capsaicinoids metabolism, (2) the influence of environmental factors on capsaicinoids metabolism, and (3) exogenous substances like methyl jasmonate, chlorophenoxyacetic acid, gibberellic acid, and salicylic acid regulate capsaicinoid metabolism. The findings of this study are expected to enhance comprehension of capsaicinoids metabolism and aid in the improvement of breeding and cultivation practices for high-quality pepper in the future.

Tomato blight significantly threatened tomato yield and quality, making precise disease detection essential for modern agricultural practices. Traditional segmentation models often struggle with over-segmentation and missed segmentation, particularly in complex backgrounds and with diverse lesion morphologies. To address these challenges, we proposed Unet with Vision Mamba and ConvNeXt (VMC-Unet), an asymmetric segmentation model for quantitative analysis of tomato blight. Built on the Unet framework, VMC-Unet integrated a parallel feature-aware backbone combining ConvNeXt, Vision Mamba, and Atrous Spatial Pyramid Pooling (ASPP) modules to enhance spatial feature focusing and multi-scale information processing. During decoding, Vision Mamba was hierarchically embedded to accurately recover complex lesion morphologies through refined feature processing and efficient up-sampling. A joint loss function was designed to optimize the model’s performance. Extensive experiments on both tomato epidemic and public datasets demonstrated VMC-Unet superior performance, achieving 97.82% pixel accuracy, 87.94% F1 score, and 86.75% mIoU. These results surpassed those of classical segmentation models, underscoring the effectiveness of VMC-Unet in mitigating over-segmentation and under-segmentation while maintaining high segmentation accuracy in complex backgrounds. The consistent performance of the model across various datasets further validated its robustness and generalization potential, highlighting its applicability in broader agricultural settings.

ABSTRACTLow temperature severely affects the growth of pomegranate in the early spring during the production process under protected cultivation. To understand the molecular responses to cold stress in Tunisian soft-seed pomegranate, this study investigated the transcriptome profiles and physiological changes of pomegranate leaves exposed to cold stress (6 °C) and freezing stress (0°C). Some potential cold response/resistance genes involved in plant hormone signal transduction, photosynthetic systems and carbon fixation in the C4 pathway, and sucrose and galactose metabolism were identified. In addition, an analysis of physiological indicators indicated that both stresses caused cell membrane damage; the accumulation of soluble sugar, soluble protein and proline; and the occurrence of photoinhibition owing to the damage in photosynthetic apparatus and the decrease in light energy conversion efficiency and electron transfer rate as shown by the decrease in net photosynthetic rate [Pn], potential maximum photochemical efficiency of PSII [Fv/Fm], actual photochemical efficiency of PSII [YII] and photochemical quenching coefficient [qP], and the effect was much moresevere in pomegranate under freezing stress. This study results offer useful information to understand the molecular mechanism of pomegranate response to cold stress and also lay a foundation for the selection of major candidate genes to conduct molecular breeding for cold tolerance in pomegranate.

Lodging results in severe yield loss and harvest difficulty in wheat production, especially in the drip-irrigation technique dominated wheat system in arid area of North-west China. We assume that our previously developed enlarging lateral space (ELS) drip irrigation pattern benefits plant lodging resistance due to the enlarged border row space (BRS) by narrowing row space (NRS). In a three-year field experiment, we compared spring wheat lodging resistance of the ELS patterns with the conventional drip irrigation pattern of TR4 (one drip tube serves plant rows (TR) of four, with normal row space (RS) of 15 cm, as control). Six ELS patterns included the normal ELS patterns of TR6 and TR8 (expanding TR from four to six and eight, with normal RS of 15 cm), large BRS ELS patterns of TR6L (BRS of 35 cm) and TR8L (BRS of 45 cm) by narrowing RS to 10 cm, and shorter BRS ELS patterns of TR6S and TR8S with BRS of 25 cm and RS of 10 cm. The normal ELS patterns with even RS (TR6 & TR8) (10.82–48.21%) significantly increased lodging rate as compared with TR4 (7.22–42.41%). Reversely, patterns with broadened BRS effectively reduced lodging rate (5.62–39.55% for TR6L & TR8L, 6.18–41.73% for TR6S & TR8S). The much improved lodging resistance of the outermost or the border rows essentially contributed to the much lower lodging rate of the modified ELS patterns. The border plants in the modified ELS patterns performed higher photosynthesis of leaf and lignin content in basal stems, subsequently increased stem pole substantial degree and finally improved stem mechanical properties. In addition, the modified ELS patterns also optimized accumulation and redistribution of dry matter in stems in the outermost rows. Therefore, the modified ELS drip irrigation patterns via NRS which leaved border effects were recommended as low lodging risks approach towards stabilized wheat productivity, of which TR6S was strongly recommended.

This study is aimed at investigating the effects of exogenous selenium (Se) on the ionic equilibrium and micro-domain distribution, state transitions between photosystem I (PSI) and photosystem II (PSII), and the photosynthetic carbon assimilation efficiency of tomato (Solanum lycopersicon L.) seedlings under the influence of salt stress. The application of 0.01 mmol•L-1 exogenous Se had no significant effects on the selective transport capacity of sodium (Na), potassium (K), calcium (Ca) and magnesium (Mg) from the roots to leaves under salt stress. It, however, significantly hindered the absorption of Na by the root system and leaves, increased the ratios of K/Na, Ca/Na and Mg/Na, and relieved the nonuniformity of micro-domain ionic distribution, thus, mitigating the ionic homeostasis imbalance and ion toxicity induced by salt stress. Additionally, the application of exogenous Se overcame stomatal limitation, regulated the state transitions between PSI and PSII, and enhanced the initial and overall activity of Rubisco as well as the activities of Rubisco activase (RCA) and fructose-1,6-bisphosphatase (FBPase). It also increased the levels of expression of nine relevant genes in Calvin cycle, which subsequently improved the concentration of photosynthetic substrates, balanced the distribution of activation energy between PSI and PSII, promoted the efficiency of CO2 carboxylation and carbon assimilation, thereby increasing the photosynthetic efficiency of tomato seedling leaves under salt stress. Hence, the supply of exogenous Se can alleviate the inhibition of salt stress on tomato seedling growth by rebuilding ionic homeostasis and promoting photosynthetic capacity.

The climatic parameters within greenhouse facilities, such as temperature, humidity, and light, exert significant influence on the growth and yield of crops, particularly seedlings. Therefore, it is crucial to establish an accurate predictive model to monitor and adjust the greenhouse microclimate for optimizing the greenhouse environment to the fullest extent. To precisely forecast the greenhouse microclimate and assess the suitability of nursery environments, this study focuses on greenhouse environmental factors. This study leveraged open-source APIs to acquire meteorological data, integrated a model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), and utilized the sparrow search algorithm to optimize model parameters, consequently developing a time series greenhouse microclimate prediction model. Furthermore, Squeeze-and-Excitation (SE) Networks were employed to enhance the model’s attention mechanism, enabling more accurate predictions of environmental factors within the greenhouse. The predictive results indicated that the optimized model achieved high precision in forecasting the greenhouse microclimate, with average errors of 0.540 °C, 0.936%, and 1.586 W/m2 for temperature, humidity, and solar radiation, respectively. The coefficients of determination (R2) reached 0.940, 0.951, and 0.936 for temperature, humidity, and solar radiation, respectively. In comparison to individual CNN or LSTM models, as well as the back-propagation (BP) neural network, the proposed model demonstrates a significant improvement in predictive accuracy. Moreover, this research was applied to the greenhouse nursery environment, demonstrating that the proposed model significantly enhanced the efficiency of greenhouse seedling cultivation and the quality of seedlings. Our study provided an effective approach for optimizing greenhouse environmental control and nursery environment suitability, contributing significantly to achieving sustainable and efficient agricultural production.

The application of regulated deficit irrigation (RDI) strategies with high water-saving effects for processing tomatoes is an important current research trend. In this study, we aimed to reveal the patterns of growth, yield, and irrigation water productivity (WPI) in response to the water deficit in processing tomatoes under drip irrigation and plastic mulching in Xinjiang. To determine a more precise irrigation regime, various degrees of RDI treatments were applied to processing tomatoes in 2022 and 2023. A total of five water gradients were set up: RI (Regular Irrigation; 4500 m3/ha), W1 (4185 m3/ha), W2 (3870 m3/ha), W3 (3555 m3/ha), and W4 (3240 m3/ha). The results revealed that under RI, the yield and irrigation water productivity were 142 t/ha and 31.49 kg/m3, respectively. Compared with RI, W1 exhibited an increase in yield and irrigation water productivity of 12.13% and 22.39%, respectively; however, other treatments exhibited a decrease. The main reasons for the increase in yield under the W1 treatment were: the W1 treatment, improved photosynthetic performance, increased dry matter accumulation, and improved soil moisture conditions, thus promoting plant growth and development. In addition, in terms of water regulation at various fertility stages, moderate water deficiency at the seedling stage (S), flowering stage (F), and maturity stage (M) and rewatering at the fruit expansion stage (E) were more conducive to optimizing the yield structure. In conclusion, considering plant growth status, dry matter accumulation, yield, and WPI, we suggested that the W1 treatment is the optimal RDI mode most suitable for drip irrigation under mulching for processing tomatoes in Xinjiang. This study provided a theoretical and technical basis for the promotion of “water-saving and efficiency-enhancing” production of processing tomatoes.

Soil salinity is a severe threat to agricultural production. Most saline soils turn alkaline, increasing the soil pH and, in turn, hampering the growth and development of crops. In this study, the effects of a foliar spray of melatonin (MT; 100 μmol·L−1) on the pH of the root environment, growth of tomato seedlings, endogenous MT levels, rapid chlorophyll fluorescence induction kinetics, and key enzymes of the Calvin cycle under alkaline (60 mmol·L−1 NaHCO3) stress were studied in Riegel 87-5 tomatoes. The results revealed that the growth and photosynthesis of tomato seedlings were inhibited by increased pH in the root environment under alkali stress; however, the application of exogenous MT reduced the pH of the root environment, alleviated the inhibition of growth of tomato seedlings under alkali stress, increased the content of photosynthetic pigments, alleviated the damage of the donor and acceptor sides of the photosynthetic electron transport chain, increased the activity and efficiency of photosynthetic electron transport, and optimized the share of the light energy allocated to PSII reaction centers. Increased expression levels of Calvin-cycle enzymes, including fructose-1,6-bisphosphate aldolase (FBA), fructose-1,6-bisphosphate esterase (FBP), and phosphoglycerate kinase (PGK), led to enhanced photosynthetic performance in tomato seedlings. Exogenous MT boosted endogenous MT levels and stimulated the production and secretion of organic acids in the root system. This regulation of organic acid content reduced the environmental pH in the inter-root zone, alleviating the damage caused by alkali stress. This study indicated that the exogenous administration of MT may mediate an increase in endogenous MT levels, regulate the efficiency of photosynthesis and root pH levels, and play a crucial role in mitigating injury caused by alkali stress in tomato seedlings.

Rational irrigation and nitrogen management strategies are crucial for wheat growth. However, the optimal amount of water and nitrogen for the newly developed drip irrigated spring wheat system (TR6S, one drip tube service for six rows of wheat, with a row spacing of 10 cm and an inter-block space of 25 cm, saves drip tubes and obtains higher profits) in dry and semi-arid areas remains unclear. Therefore, a field experiment was conducted with four nitrogen levels (300, 270, 240, and 0 kg ha−1 referred N300, N270, N240, and N0) and four irrigation levels (4500, 4200, 3900, and 3600 m3 ha−1 referred I4500, I4200, I3900, and I3600) during the 2021–2022 and 2022–2023 spring wheat seasons to analyze the effects of irrigation (I) and nitrogen (N) levels on grain yield, water-nitrogen use efficiency, profit, biomass accumulation, and nitrogen nutrient absorption status under TR6S. Compared with the traditional irrigation and nitrogen management strategy (N300–I4500, as control), lesser irrigation and nitrogen supply (II>3979 m3 ha−1 and 300 kg ha−1 >N>273 kg ha−1) improved grain yield, WUE, AEN, and profit. The increase in grain yield was mainly related to the rise in 1000-grain weight and kernels per spike. Although the moderate reduction in irrigation lowered soil moisture status, the dry matter pre-stored in the vegetative organs before anthesis that gets redistributed into grains during grain filling was improved. Moreover, the moderate reduction in nitrogen supply resulted in a more reasonable nitrogen nutrition index (NNI) of wheat plant, which improved flag leaf area and chlorophyll relative content (SPAD) at the anthesis stage. This also played a positive role in biomass accumulation and redistributed, yield structure optimization. Considering comprehensively yield, WUE, AEN and profit, combination of 285 kg ha−1 N and 4170 m3 ha−1 I was optimal irrigation and nitrogen application pattern for TR6S. This strategy can be applied to other arid and semi-arid regions.

This study investigated the protective effects of exogenous ascorbic acid (AsA, 0.5 mmol·L−1) treatment on salt-induced photosystem inhibition in tomato seedlings under salt stress (NaCl, 100 mmol·L−1) conditions with and without the AsA inhibitor lycorine. Salt stress reduced the activities of photosystem II (PSII) and PSI. AsA treatment mitigated inhibition of the maximal photochemical efficiency of PSII (Fv/Fm), maximal P700 changes (Pm), the effective quantum yields of PSII and I [Y(II) and Y(I)], and non-photochemical quenching coefficient (NPQ) values under salt stress conditions both with and without lycorine. Moreover, AsA restored the balance of excitation energy between two photosystems (β/α-1) after disruption by salt stress, with or without lycorine. Treatment of the leaves of salt-stressed plants with AsA with or without lycorine increased the proportion of electron flux for photosynthetic carbon reduction [Je(PCR)] while decreasing the O2-dependent alternative electron flux [Ja(O2-dependent)]. AsA with or without lycorine further resulted in increases in the quantum yield of cyclic electron flow (CEF) around PSI [Y(CEF)] while increasing the expression of antioxidant and AsA–GSH cycle-related genes and elevating the ratio of reduced glutathione/oxidized glutathione (GSH/GSSG). Similarly, AsA treatment significantly decreased the levels of reactive oxygen species [superoxide anion (O2−) and hydrogen peroxide (H2O2)] in these plants. Together, these data indicate that AsA can alleviate salt-stress-induced inhibition of PSII and PSI in tomato seedlings by restoring the excitation energy balance between the photosystems, regulating the dissipation of excess light energy by CEF and NPQ, increasing photosynthetic electron flux, and enhancing the scavenging of reactive oxygen species, thereby enabling plants to better tolerate salt stress.

The Dempster-Shafer(DS) evidence theory was applied to cooperative spectrum sensing and address the paradox of evidence in classical DS evidence theory, a new weighted distance measure based algorithm was proposed.Firstly, the evidences were extracted for each sensing user.Then, the weighted distance measure of extracted basic probability assignment data was adopted as the similarity between evidences of sensing users.Finally, the similarity of evidences was transformed into credibility, which was utilized as the weight to obtain the weighted average of basic probability assignment.In order to reduce the amount of data reported to the fusion center, the projection approximation method was employed to adjust the basic probability assignment.Both theoretical analysis and simulation results show that the proposed method can improve the detection performance of spectrum sensing while the paradox of evidence exists.Compared with traditional methods the cooperation overhead is reduced.

The unnecessary application of nitrogen could negatively impact both the yield and quality of crops, and could also lead to nitrate leaching and soil pollution. The objective of this study was to investigate the effects of the optimized nitrogen application on 15N accumulation, utilization and distribution of processing tomato in China. Through field experiments with 15N and consisted two treatments: optimized nitrogen application (NY) and no nitrogen application (N0). The results showed that the absorption efficiency of applied fertilizer nitrogen was 46%. In processing tomato plants, 128.9 kg ha−1 of nitrogen came from applied fertilizer, while the remainder of the nitrogen (177.6 kg ha−1) came from the soil. The processing tomato fully absorbed the soil nitrogen, which reduced the nitrogen loss rate and resulted in higher production of dry matter. In the harvest stage. The residual percentage in soil of applied fertilizer nitrogen was 34%, which was mainly distributed in the 0-40 cm soil layer. This reduced the leaching of nitrogen and was conducive to the reuse of crops in the next year. This study provides a reference for the development of precise nitrogen management techniques using drip irrigation methods in arid areas..

Timely and accurate judgment of the nitrogen nutritional status of crops is the key to develop an optimal nitrogen application strategy. However, the evaluation criteria of nitrogen nutrition and nitrogen application strategies at each growth stage of wheat are not clear for the new type of drip-irrigated spring wheat system, TR6S (where one drip tube serves six rows of wheat, with a row spacing (RS) of 10 cm, inter-block space (IBS) of 25 cm and the lateral spacing (LS) of 80 cm, which achieved a lower drip-tube input and higher profit compared with the traditional planting system in Xinjiang). Therefore, we studied the recommendation mechanism of nitrogen fertilizer in different growth stages of wheat based on the critical SPAD values of leaves under TR6S. We set four nitrogen treatments (N1 (300 kg ha−1), N2 (270 kg ha−1), N3 (240 kg ha−1) and N4 (0 kg ha−1)) during two spring wheat growth seasons. The results revealed that the correlation coefficient (r2) between SPAD (soil plant analysis development) value and plant nitrogen content in the middle of first top leaf (L1-M) of wheat was higher than that in other leaf types and leaf positions under TR6S. A quadratic function relationship existed between a SPAD value of L1-M and grain yield. The critical SPAD values at the jointing, booting, anthesis, early milk, and late milk stages were 37.34, 39.40, 42.25, 45.57, and 35.91, respectively. In addition, through the establishment of the nitrogen application recommendation model for various wheat growth stages based on the critical SPAD value, the recommended optimal nitrogen application rates at jointing, booting, anthesis, early milk, and late milk stages were observed to be 69.4, 80.0, 90.8, 44.0, and 6.0 kg ha−1, respectively. This recommended nitrogen application strategy exhibited a better parallel relationship with the nitrogen nutrition index (NNI) of each growth period than the conventional nitrogen application strategy. Therefore, it was more in line with the actual absorption and utilization of nitrogen in wheat of TR6S. In conclusion, the SPAD values of L1-M could be relatively more accurate to evaluate the nitrogen nutrition status of wheat. Compared to traditional nitrogen application strategy, reducing and delaying nitrogen application, recommended based on the leaf SPAD model, was more suitable for nitrogen utilization under TR6S. The results can be applied in other arid and semiarid regions.

In this study, the protective role of exogenous ascorbic acid (AsA) on salt-induced inhibition of photosynthesis in the seedlings of processing tomatoes under salt stress has been investigated. Plants under salt stress (NaCl, 100 mmol/L) were foliar-sprayed with AsA (0.5 mmol/L), lycorine (LYC, 0.25 mmol/L, an inhibitor of key AsA synthesis enzyme l-galactono-γ-lactone dehydrogenase activity), or AsA plus LYC. The effects of AsA on fast OJIP fluorescence rise curve and JIP parameters were then examined. Our results demonstrated that applying exogenous AsA significantly changed the composition of O-J-I-P fluorescence transients in plants subjected to salt stress both with and without LYC. An increase in basal fluorescence (Fo) and a decrease in maximum fluorescence (Fm) were observed. Lower K- and L-bands and higher I-band were detected on the OJIP transient curves compared, respectively, with salt-stressed plants with and without LYC. AsA application also significantly increased the values of normalized total complementary area (Sm), relative variable fluorescence intensity at the I-step (VI), absorbed light energy (ABS/CSm), excitation energy (TRo/CSm), and reduction energy entering the electron transfer chain beyond QA (ETo/CSm) per reaction centre (RC) and electron transport flux per active RC (ETo/RC), while decreasing some others like the approximated initial slope of the fluorescence transient (Mo), relative variable fluorescence intensity at the K-step (VK), average absorption (ABS/RC), trapping (TRo/RC), heat dissipation (DIo/RC) per active RC, and heat dissipation per active RC (DIo/CSm) in the presence or absence of LYC. These results suggested that exogenous AsA counteracted salt-induced photoinhibition mainly by modulating the endogenous AsA level and redox state in the chloroplast to promote chlorophyll synthesis and alleviate the damage of oxidative stress to photosynthetic apparatus. AsA can also raise the efficiency of light utilization as well as excitation energy dissipation within the photosystem II (PSII) antennae, thus increasing the stability of PSII and promoting the movement of electrons among PS1 and PSII in tomato seedling leaves subjected to salt stress.

The device-to-device (D2D) communication is viewed as an attractive technique to increase the spectrum efficiency and the data transmission rate in the wireless network. In this paper, we investigate the joint resource allocation and power control problem for cooperative D2D users (DUs) which multiplex cellular users (CUs) in downlink cooperative D2D heterogeneous networks. The studied resource allocation problem contains the spectrum resource block allocation and the selection of an idle user which works as a relay to assist the D2D links communication, while the power control aims to reduce the interference between users and improve the communication quality of service (QoS). To efficiently maximize the total throughput of all the DU links and the CU links on the premise of guaranteeing the communication QoS for CUs, we propose a quantum coral reefs optimization algorithm (QCROA) to obtain the optimal joint resource allocation and power control scheme. The simulation results demonstrate that the proposed QCROA achieves an excellent performance for different network communication scenarios.

The recent appreciation of deep reinforcement learning (DRL) arises from its successes in many domains, but the applications of DRL in practical engineering are still unsatisfactory, including optimizing control strategies in cognitive electronic warfare (CEW). CEW is a massive and challenging project, and due to the sensitivity of the data sources, there are few open studies that have investigated CEW. Moreover, the spatial sparsity, continuous action, and partially observable environment that exist in CEW have greatly limited the abilities of DRL algorithms, which strongly depend on state-value and action-value functions. In this paper, we use Python to build a 3-D space game named Explorer to simulate various CEW environments in which the electronic attacker is an unmanned combat air vehicle (UCAV) and the defender is an observation station, both of which are equipped with radar as the observation sensor. In our game, the UCAV needs to accomplish the task of detecting the target as early as possible to perform follow-up tracking and guidance tasks. To allow an "infant" UCAV to understand what "target searching" is, we train the UCAV's maneuvering strategies by means of a well-designed reward shaping, a simplified constant accelerated motion control, and a deep deterministic policy gradient (DDPG) algorithm based on a generative model and variational Bayesian estimation. The experimental results show that when the operating cycle is 0.2 s, the search success rate of the trained UCAV in 10000 episodes is improved by 33.36% compared with the benchmark, and the target destruction rate is similarly improved by 57.84%.

The wireless energy harvesting (EH) technique is regarded as a new way to provide an energy supply for energy-constrained cognitive relay networks (CRNs). A novel wireless EH cognitive multi-user relay network (CMRN) for the underlay protocol is investigated in this paper. In this system, there are multiple primary users (PUs) and multiple secondary users (SUs). The SUs can share the licensed spectrum and harvest energy from ambient signals. The problems of multiple relay selection by the SUs and of finding the optimal EH ratio are considered. We analytically derive the exact expression of the throughput of a secondary network. In it, there are four constraints: for the permitted peak interference with each primary transmitter (PT); for the sum interference for each PT; that the transmit power of secondary source nodes (SSNs) and secondary relays (SRs) should be less than the energy harvested; and that each secondary source node/secondary destination node (SSN-SDN) pair can only choose one SR. To obtain the optimal performance of the secondary network's throughput, we should optimize the multiple relay selection scheme and the EH ratio. Actually, it is a classic integer optimization problem to design an optimal multiple relay selection scheme. However, the selection of the optimal EH ratio is a continuous optimization problem. The joint multiple relay selection and time slot allocation is a classical hybrid optimization problem. So, we propose a novel quantum sine cosine algorithm (QSCA) for resolving the difficulty with optimization of multiple relay selection and the EH ratio. Our simulation results verify our proposed solution by showing the influence of different parameters for the proposed model and by demonstrating good performance under the QSCA.

Abstract A novel direction-finding method is proposed for bistatic multiple-input-multiple-output (MIMO) radar in the impulse noise in this paper. The method has the capacity to suppress the impulse noise by means of infinite norm normalization and can obtain better performance for direction finding via the weighted signal subspace fitting algorithm. To solve the objective function of this method, we devise a quantum-inspired grey wolf optimization algorithm to acquire the global optimal solution. The proposed method based on QGWO can resolve the direction-finding difficulties of bistatic MIMO radar. Monte-Carlo experiments have confirmed the robustness and superiority of the proposed method for locating independent and coherent sources with a small number of snapshots in the impulse noise compared with some existing direction-finding methods in a series of scenarios. In addition, we present the Cramér-Rao bound (CRB) of angle estimation for bistatic MIMO radar in the impulse noise, which generalizes the Gaussian CRB for performance analysis.

The occurrence of cucumber downy mildew in solar greenhouses directly affects the yield and quality of cucumber. Chemical control methods may cause excessive pesticide residues, endanger food quality and safety, pollute the ecological environment, etc. Therefore, it is very important to predict the disease before its occurrence. To provide farmers with better and effective guidance for the prevention and control work, minimize the loss of disease damage, this article took cucumber ‘Lyujingling No. 2′ as the experimental material and acquired greenhouse environmental factors data by wireless sensors, including Temp (Temperature), RH (Relative Humidity), ST (Soil Temperature) and SR (Solar Radiation). LSTM (Long Short-Term Memory) neural network structure was constructed based on Keras deep learning framework to develop a prediction model with time-series environmental factors. Combined with the occurrence of downy mildew from manual investigation and statistics, through debugging the parameters, this article developed an occurrence prediction model for cucumber downy mildew and compared it with KNN (K-Nearest Neighbors Classification) and ANN (Artificial Neural Network). In the prediction model, the forecasted results of the four environmental factors were consistent with the true value distributions, and R2 (R-Squared) were all above 0.95. Among them, the ST variable predicted the best results, e.g., R2 = 0.9982, RMSE (Root Mean Square Error) = 0.08 °C, and MAE (Mean Absolute Error) = 0.05 °C. In the disease occurrence prediction model, the training accuracy was 95.99%, the Loss value was 0.0159, the disease occurrence prediction Accuracy was 90%, Precision was 94%, Recall was 89%, F1-score was 91%, the AUC (Area Under Curve) value was 90.15%, and Kappa coefficient was 0.80. It also had obvious advantages over other different models. In summary, the model had a high classification accuracy and performance, and it can provide a reference for the occurrence prediction of cucumber downy mildew in actual production.

The effects of exogenous GSH (reduced glutathione) on photosynthetic characteristics, photosystem II efficiency, absorbed light energy allocation and the H2O2-scavenging system in chloroplasts of salt-stressed tomato (Solanum lycopersicum L.) seedlings were studied using hydroponic experiments in a greenhouse. Application of exogenous GSH ameliorated saline-induced growth inhibition, the disturbed balance of Na+ and Cl− ions and Na+/K+ ratios, and the reduction of the net photosynthetic rate (Pn). GSH also increased the maximal photochemical efficiency of PSII (Fv/Fm), the electron transport rate (ETR), the photochemical quenching coefficient (qP), and the non-photochemical quenching coefficient (NPQ). In addition, GSH application increased the photochemical quantum yield (Y(II)) and relative deviation from full balance between the photosystems (β/α–1) and decreased the PSII excitation pressure (1–qP) and quantum yield of non-regulated energy dissipation (Y(NO)) in leaves of salt-stressed tomatoes without BSO (L-buthionine-sulfoximine, an inhibitor of key GSH synthesis enzyme γ-glutamylcysteine synthetase) or with BSO. Further, the addition of GSH depressed the accumulation of H2O2 and malondialdehyde (MDA), induced the redistribution of absorbed light energy in PSII reaction centers, and improved the endogenous GSH content, GSH/GSSH ratio and activities of H2O2-scavenging enzymes (including superoxidase dismutase (SOD), catalase (CAT), peroxidase (POD) and key enzymes in the AsA-GSH cycle and Grx system) in the chloroplasts of salt-stressed plants with or without BSO. Therefore, GSH application alleviates inhibition of salt-induced growth and photosynthesis mainly by overcoming stomatal limitations, improving the PSII efficiency, and balancing the uneven distribution of light energy to reduce the risk of ROS generation and to mediate chloroplast redox homeostasis and the antioxidant defense system to protect the chloroplasts from oxidative damage. Thus, GSH may be used as a potential tool for alleviating salt stress in tomato plants.

Cognitive radio technology is an important branch in the field of wireless communication, and automatic identification is a major part of cognitive radio technology. Convolutional neural network (CNN) is an advanced neural network, which is the forefront of application in the digital image recognition area. In this paper, we explore CNN in an automatic system to recognize the cognitive radio waveforms. Excitedly, it is a more effective model with high ratio of successful recognition (RSR) under high power background noise. The system can identify eight kinds of signals, including binary phase shift keying (Barker codes modulation) linear frequency modulation, Costas codes, Frank code, and polytime codes (T1, T2, T3, and T4). The recognition part includes a CNN classifier. First, we determine the appropriate architecture to make CNN effective for proposed system. Specifically, we focus on how many convolutional layers are needed, what appropriate number of hidden units is, and what the best pooling strategy is. Second, we research how to obtain the image features into CNN that based on Choi-Williams time-frequency distribution. Finally, by means of the simulations, the results of classification are demonstrated. Simulation results show the overall RSR is 93.7% when the signal-to-noise ratio is -2dB.

In emerging nanotechnologies, due to the manufacturing process, a significant percentage of components may be faulty. In order to make systems based on unreliable nano-scale components reliable, it is necessary to design fault-tolerant architectures. This paper presents a novel fault-tolerant technique for nanocomputers, namely the XOR multiplexing technique. This hardware redundancy technique is based on a numerous duplication of faulty components. We analyze the error distributions of the XOR multiplexing unit and the error distributions of multiple stages of the XOR multiplexing system, then compare them to the NAND multiplexing unit and the NAND multiplexing multiple stages system, respectively. The simulation results show that XOR multiplexing is more reliable than NAND multiplexing. Bifurcation theory is used to analyze the fault-tolerant ability of the system and the results show that XOR multiplexing technique has a high fault-tolerant ability. Similarly to the NAND multiplexing technique, this fault-tolerant technique is a potentially effective fault tolerant technique for future nanoelectronics.

The wireless channel is volatile in nature, due to various signal attenuation factors including path-loss, shadowing, and multipath fading. Existing media access control (MAC) protocols, such as the widely adopted 802.11 wireless fidelity (Wi-Fi) family, advocate masking harsh channel conditions with persistent retransmission and backoff, in order to provide a packet-level best-effort service. However, the asymmetry of the network environment of client nodes in space is not fully considered in the method, which leads to the decline of the transmission efficiency of the good ones. In this paper, we propose CoFi, a coding-assisted file distribution protocol for 802.11 based wireless local area networks (LANs). CoFi groups data into batches and transmits a random linear combination of packets within each batch, thereby reducing redundant packet and acknowledgement (ACK) retransmissions when the channel is lossy. In addition, CoFi adopts a MAC layer caching scheme that allows clients to store the overheard coded packets and use such cached packets to assist nearby peers. With this measure, it further improves the effective throughput and shortens the buffering delay when running applications such as bulk data transmission and video streaming. Our trace based simulation demonstrates that CoFi can maintain a similar level of packet delay to 802.11, but increases the throughput performance by a significant margin in a lossy wireless LAN. Furthermore, we perform a reverse-engineering on CoFi and 802.11 using a simple analytical framework, proving that they asymptotically approach different fairness measures, thus resulting in a disparate performance.

The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.

This paper proposes a low-complexity method to estimate the direction of arrival and polarization based on the polarization sensitive array (PSA) which is composed of cross-dipoles. We built a half-quaternions model through the Cayley–Dickson form to remove the redundant information. Then, the directions of arrival (DOAs) were estimated via the root-MUSIC algorithm. Finally, the polarizations were estimated by generalized eigenvalue method. Unlike some existing searching algorithms, such as multiple signal classification (MUSIC), this method can avoid the peak searching and maintains high estimation accuracy. Moreover, we use the oblique projection operators to filter out the interference signals which are decoys of the target signal. Simulation results demonstrate the effectiveness and favorable performance of the proposed method.

The main objective of this study was to develop a nondestructive method for monitoring cotton growth and N status using a digital camera. Digital images were taken of the cotton canopies between emergence and full bloom. The green and red values were extracted from the digital images and then used to calculate canopy cover. The values of canopy cover were closely correlated with the normalized difference vegetation index and the ratio vegetation index and were measured using a GreenSeeker handheld sensor. Models were calibrated to describe the relationship between canopy cover and three growth properties of the cotton crop (i.e., aboveground total N content, LAI, and aboveground biomass). There were close, exponential relationships between canopy cover and three growth properties. And the relationships for estimating cotton aboveground total N content were most precise, the coefficients of determination (R2) value was 0.978, and the root mean square error (RMSE) value was 1.479 g m−2. Moreover, the models were validated in three fields of high-yield cotton. The result indicated that the best relationship between canopy cover and aboveground total N content had an R2 value of 0.926 and an RMSE value of 1.631 g m−2. In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring cotton growth and N status.

In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.

This study is aimed at investigating the effects of exogenous selenium (Se) on the ionic equilibrium and micro-domain distribution, state transitions between photosystem I (PSI) and photosystem II (PSII), and the photosynthetic carbon assimilation efficiency of tomato (Solanum lycopersicon L.) seedlings under the influence of salt stress. The application of 0.01 mmol•L-1 exogenous Se had no significant effects on the selective transport capacity of sodium (Na), potassium (K), calcium (Ca) and magnesium (Mg) from the roots to leaves under salt stress. It, however, significantly hindered the absorption of Na by the root system and leaves, increased the ratios of K/Na, Ca/Na and Mg/Na, and relieved the nonuniformity of micro-domain ionic distribution, thus, mitigating the ionic homeostasis imbalance and ion toxicity induced by salt stress. Additionally, the application of exogenous Se overcame stomatal limitation, regulated the state transitions between PSI and PSII, and enhanced the initial and overall activity of Rubisco as well as the activities of Rubisco activase (RCA) and fructose-1,6-bisphosphatase (FBPase). It also increased the levels of expression of nine relevant genes in Calvin cycle, which subsequently improved the concentration of photosynthetic substrates, balanced the distribution of activation energy between PSI and PSII, promoted the efficiency of CO2 carboxylation and carbon assimilation, thereby increasing the photosynthetic efficiency of tomato seedling leaves under salt stress. Hence, the supply of exogenous Se can alleviate the inhibition of salt stress on tomato seedling growth by rebuilding ionic homeostasis and promoting photosynthetic capacity. [ABSTRACT FROM AUTHOR]