PMSM Torque Ripple Suppression Method Based on SMA-Optimized ILC.

Periodic torque ripple often occurs in permanent magnet synchronous motors due to cogging torque and flux harmonic distortion, leading to motor speed fluctuations and further causing mechanical vibration and noise, which seriously affects the performance of the motor vector control system. In response to the above problems, a PMSM torque ripple suppression method based on SMA-optimized ILC is proposed, which does not rely on prior knowledge of the system and motor parameters. That is, an SMA is used to determine the optimal values of the key parameters of the ILC in the target motor control system, and then the real-time torque deviation value calculated by iterative learning is compensated to the system control current set end. By reducing the influence of higher harmonics in the control current, the torque ripple is suppressed. Research results show that this method has high efficiency and accuracy in parameter optimization, further improving the ILC performance, effectively reducing the impact of higher harmonics, and suppressing the torque ripple amplitude.

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm.

Carrier phase measurements currently play a crucial role in achieving rapid and highly accurate positioning of global navigation satellite systems (GNSS). Resolving the integer ambiguity correctly is one of the key steps in this process. To address the inefficiency and slow search problem during ambiguity solving, we propose a single-frequency GNSS integer ambiguity solving based on an adaptive genetic particle swarm optimization (AGPSO) algorithm. Initially, we solve for the floating-point solution and its corresponding covariance matrix using the carrier-phase double difference equation. Subsequently, we decorrelate it using the inverse integer Cholesky algorithm. Furthermore, we introduce an improved fitness function to enhance convergence and search performance. Finally, we combine a particle swarm optimization algorithm with adaptive weights to conduct an integer ambiguity search, where each generation selectively undergoes half-random crossover and mutation operations to facilitate escaping local optima. Comparative studies against traditional algorithms and other intelligent algorithms demonstrate that the AGPSO algorithm exhibits faster convergence rates, improved stability in integer ambiguity search results, and in practical experiments the baseline accuracy of the solution is within 0.02 m, which has some application value in the practical situation of short baselines.

The intelligent football players' motion recognition system based on convolutional neural network and big data.

This article focuses on evaluating the efficacy of intelligent image processing techniques using deep learning algorithms in the context of football, to present pragmatic solutions for enhancing the functional strength training of football players. The article commences by delving into the prevailing research landscape concerning image recognition in football. It then embarks on a comprehensive examination of the prevailing landscape in soccer image recognition research. Subsequently, a novel soccer image classification model is meticulously crafted through the fusion of Space-Time Graph Neural Network (STGNN) and Bi-directional Long Short-Term Memory (BiLSTM). The devised model introduces the potency of STGNN to extract spatial features from sequences of images, adeptly harnessing spatial information through judiciously integrated graph convolutional layers. These layers are further bolstered by the infusion of graph attention modules and channel attention modules, working in tandem to amplify salient information within distinct channels. Concurrently, the temporal dimension is adroitly addressed by the incorporation of BiLSTM, effectively capturing the temporal dynamics inherent in image sequences. Rigorous simulation analyses are conducted to gauge the prowess of this model. The empirical outcomes resoundingly affirm the potency of the proposed deep hybrid attention network model in the realm of soccer image processing tasks. In the arena of action recognition and classification, this model emerges as a paragon of performance enhancement. Impressively, the model notched an accuracy of 94.34 %, precision of 92.35 %, recall of 90.44 %, and F1-score of 89.22 %. Further scrutiny of the model's image recognition capabilities unveils its proficiency in extracting comprehensive features and maintaining stable recognition performance when applied to football images. Consequently, the football intelligent image processing model based on deep hybrid attention networks, as formulated within this article, attains high recognition accuracy and demonstrates consistent recognition performance. These findings offer invaluable insights for injury prevention and personalized skill enhancement in the training of football players.

Optimized Dropkey-Based Grad-CAM: Toward Accurate Image Feature Localization.

Regarding the interpretable techniques in the field of image recognition, Grad-CAM is widely used for feature localization in images to reflect the logical decision-making information behind the neural network due to its high applicability. However, extensive experimentation on a customized dataset revealed that the deep convolutional neural network (CNN) model based on Gradient-weighted Class Activation Mapping (Grad-CAM) technology cannot effectively resist the interference of large-scale noise. In this article, an optimization of the deep CNN model was proposed by incorporating the Dropkey and Dropout (as a comparison) algorithm. Compared with Grad-CAM, the improved Grad-CAM based on Dropkey applies an attention mechanism to the feature map before calculating the gradient, which can introduce randomness and eliminate some areas by applying a mask to the attention score. Experimental results show that the optimized Grad-CAM deep CNN model based on the Dropkey algorithm can effectively resist large-scale noise interference and achieve accurate localization of image features. For instance, under the interference of a noise variance of 0.6, the Dropkey-enhanced ResNet50 model achieves a confidence level of 0.878 in predicting results, while the other two models exhibit confidence levels of 0.766 and 0.481, respectively. Moreover, it exhibits excellent performance in visualizing tasks related to image features such as distortion, low contrast, and small object characteristics. Furthermore, it has promising prospects in practical computer vision applications. For instance, in the field of autonomous driving, it can assist in verifying whether deep learning models accurately understand and process crucial objects, road signs, pedestrians, or other elements in the environment.

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment.

The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left-right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel.

Real-Time Hybrid Test Control Research Based on Improved Electro-Hydraulic Servo Displacement Algorithm.

Real-time hybrid testing (RTH) is a test method for dynamic loading performance evaluation of structures, which is divided into digital simulation and physical testing, but the integration of the two may lead to problems such as time lag, large errors, and slow response time. The electro-hydraulic servo displacement system, as the transmission system of the physical test structure, directly affects the operational performance of RTH. Improving the performance of the electro-hydraulic servo displacement control system has become the key to solving the problem of RTH. In this paper, the FF-PSO-PID algorithm is proposed to control the electro-hydraulic servo system in real-time hybrid testing (RTH), which uses the PSO algorithm to operate the optimized PID parameters and the feed-forward compensation algorithm to compensate the displacement. First, the mathematical model of the electro-hydraulic displacement servo system in RTH is presented and the actual parameters are determined. Then, the objective evaluation function of the PSO algorithm is proposed to optimize the PID parameters in the context of RTH operation, and a displacement feed-forward compensation algorithm is added for theoretical study. To verify the effectiveness of the method, joint simulations were performed in Matlab/Simulink to compare and test FF-PSO-PID, PSO-PID, and conventional PID (PID) under different input signals. The results show that the proposed FF-PSO-PID algorithm effectively improves the accuracy and response speed of the electro-hydraulic servo displacement system and solves the problems of RTH time lag, large error, and slow response.

Temperature-Dependent Photoluminescence of CdS/ZnS Core/Shell Quantum Dots for Temperature Sensors.

Exploring the temperature-dependent photoluminescence (PL) properties of quantum dots (QDs) is not only important for understanding the carrier recombination processes in QD-based devices but also critical for expanding their special applications at different temperatures. However, there is still no clear understanding of the optical properties of CdS/ZnS core/shell QDs as a function of temperature. Herein, the temperature-dependent PL spectra of CdS/ZnS core/shell QDs were studied in the temperature range of 77-297 K. It was found that the band-edge emission (BEE) intensity decreases continuously with increasing temperature, while the surface-state emission (SSE) intensity first increases and then decreases. For BEE intensity, in the low temperature range, a small activation energy (29.5 meV) in the nonradiative recombination process led to the decrease of PL intensity of CdS/ZnS core/shell QDs; and at high temperature the PL intensity attenuation was caused by the thermal escape process. On the other hand, the temperature-dependent variation trend of the SSE intensity was determined by the competition of the trapping process of the surface trap states and the effect of thermally activated non-radiative defects. As the temperature increased, the PL spectra showed a certain degree of redshift in the peak energies of both band-edge and surface states and the PL spectrum full width at half-maximum (FWHM) increases, which was mainly due to the coupling of exciton and acoustic phonon. Furthermore, the CIE chromaticity coordinates turned from (0.190, 0.102) to (0.302, 0.194), which changed dramatically with temperature. The results indicated that the CdS/ZnS core/shell QDs are expected to be applied in temperature sensors.

Improvement of AD-Census Algorithm Based on Stereo Vision.

Problems such as low light, similar background colors, and noisy image acquisition often occur when collecting images of lunar surface obstacles. Given these problems, this study focuses on the AD-Census algorithm. In the original Census algorithm, in the bit string calculated with the central pixel point, the bit string will be affected by the noise that the central point is subjected to. The effect of noise results in errors and mismatching. We introduce an improved algorithm to calculate the average window pixel for solving the problem of being susceptible to the central pixel value and improve the accuracy of the algorithm. Experiments have proven that the object contour in the grayscale map of disparity obtained by the improved algorithm is more apparent, and the edge part of the image is significantly improved, which is more in line with the real scene. In addition, because the traditional Census algorithm matches the window size in a fixed rectangle, it is difficult to obtain a suitable window in the image range of different textures, affecting the timeliness of the algorithm. An improvement idea of area growth adaptive window matching is proposed. The improved Census algorithm is applied to the AD-Census algorithm. The results show that the improved AD-Census algorithm has been shown to have an average run time of 5.3% and better matching compared to the traditional AD-Census algorithm for all tested image sets. Finally, the improved algorithm is applied to the simulation environment, and the experimental results show that the obstacles in the image can be effectively detected. The improved algorithm has important practical application value and is important to improve the feasibility and reliability of obstacle detection in lunar exploration projects.

Controllable Preparation of Ag-SiO2 Composite Nanoparticles and Their Applications in Fluorescence Enhancement.

Metal nanoparticles have attracted a great deal of interest due to their unique properties of surface plasmon resonance. Metal nanoparticles can enhance the fluorescence emission intensity of quantum dots (QDs) through the local surface plasmon resonance effect, which is mainly determined by the distance between them. Therefore, it is very important to achieve controllable distance between metal and QDs, and study fluorescence enhancement. In this work, the controllable adjustment of the distance between metal nanoparticles and QDs was successfully realized by controlling the thickness of the SiO2 shell of Ag@SiO2 nanoparticles. Firstly, Ag nanoparticles with uniform size distribution and relatively high concentration were prepared, and then the thickness of the SiO2 shell was controlled by controlling the amount of tetra-ethyl orthosilicate (TEOS) in the hydrolysis of TEOS reaction. (3-aminopropyl) triethoxysilane (APS) was used to connect CdS/ZnS QDs with Ag@SiO2 nanoparticles to form Ag@SiO2@CdS/ZnS QD composite nanoparticles. The fluorescence spectra shows that the fluorescence intensity of the Ag@SiO2@CdS/ZnS QD composite nanoparticles is significantly enhanced. Photoexcitation spectra and fluorescence spectra of CdS/ZnS QD and Ag@SiO2@CdS/ZnS QD composite nanoparticles, measured under different energy excitation conditions, indicate that the existence of Ag nanoparticles can enhance the fluorescence intensity of CdS/ZnS QDs. Finally, a further physical mechanism of fluorescence enhancement is revealed.

Serendipita Fungi Modulate the Switchgrass Root Transcriptome to Circumvent Host Defenses and Establish a Symbiotic Relationship.

The fungal family Serendipitaceae encompasses root-associated lineages with endophytic, ericoid, orchid, and ectomycorrhizal lifestyles. Switchgrass is an important bioenergy crop for cellulosic ethanol production owing to high biomass production on marginal soils otherwise unfit for food crop cultivation. The aim of this study was to investigate the host plant responses to Serendipita spp. colonization by characterizing the switchgrass root transcriptome during different stages of symbiosis in vitro. For this, we included a native switchgrass strain, Serendipita bescii, and a related strain, S. vermifera, isolated from Australian orchids. Serendipita colonization progresses from thin hyphae that grow between root cells to, finally, the production of large, bulbous hyphae that fill root cells during the later stages of colonization. We report that switchgrass seems to perceive both fungi prior to physical contact, leading to the activation of chemical and structural defense responses and putative host disease resistance genes. Subsequently, the host defense system appears to be quenched and carbohydrate metabolism adjusted, potentially to accommodate the fungal symbiont. In addition, prior to contact, switchgrass exhibited significant increases in root hair density and root surface area. Furthermore, genes involved in phytohormone metabolism such as gibberellin, jasmonic acid, and salicylic acid were activated during different stages of colonization. Both fungal strains induced plant gene expression in a similar manner, indicating a conserved plant response to members of this fungal order. Understanding plant responsiveness to Serendipita spp. will inform our efforts to integrate them into forages and row crops for optimal plant-microbe functioning, thus facilitating low-input, sustainable agricultural practices.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Applying TS-DBN model into sports behavior recognition with deep learning approach.

The purposes are to automatically collect information about human sports behavior from massive video data and provide an explicit recognition and analysis of body movements. The analysis of multi-scale input data, the improvement of spatiotemporal Deep Belief Network (DBN), and the different pooling strategies are regarded as the focuses to improve the belief networks in deep learning (DL). Moreover, a human sports behavior recognition model is proposed based on particular spatio-temporal features. Also, video frame data are collected from the Royal Institute of Technology (KTH) and University of Central Florida (UCF) datasets for training. The TensorFlow platform is employed to simulate the built algorithm. Finally, the constructed algorithm model is compared with the DBN proposed by Yang et al. the Convolutional Neural Network (CNN) proposed by Ullah et al. and the DBN-Hidden Markov Model (HMM) algorithm proposed by Xu et al. to analyse its performance. The recognition effects of each algorithm in the two datasets are analyzed. Results demonstrate that CNN developed by Ullah et al. has the worst accuracy on the KTH and UCF datasets, followed by DBN developed by Yang et al. and DBN-HMM developed by Xu et al. The constructed algorithm model can provide the highest accuracy, reaching about 90%, and the recognition accuracy of human sports behaviors of each algorithm on the KTH dataset is lower than that on the UCF dataset. On the KTH dataset, the recognition accuracy for boxing is the highest and running the lowest. Analyzing the model's accuracy in the four scenes (S1, S2, S3, and S4) on the KTH dataset suggests that the recognition accuracy for the indoor scene (S4) is higher than that of the outdoor scenes (S1, S2, and S3). On the UCF dataset, the recognition accuracy for lifting is the highest, reaching 99%, and that for walking is the lowest, reaching 51%. Therefore, the proposed human sports recognition model can provide a higher accuracy than other classic DL algorithms, providing an experimental basis for subsequent human sports recognition research.

Serendipita bescii promotes winter wheat growth and modulates the host root transcriptome under phosphorus and nitrogen starvation.

Serendipita vermifera ssp. bescii, hereafter referred to as S. bescii, is a root-associated fungus that promotes plant growth in both its native switchgrass host and a variety of monocots and dicots. Winter wheat (Triticum aestivum L.), a dual-purpose crop, used for both forage and grain production, significantly contributes to the agricultural economies of the Southern Great Plains, USA. In this study, we investigated the influence of S. bescii on growth and transcriptome regulation of nitrogen (N) and phosphorus (P) metabolism in winter wheat. Serendipita bescii significantly improved lateral root growth and forage biomass under a limited N or P regime. Further, S. bescii activated sets of host genes regulating N and P starvation responses. These genes include, root-specific auxin transport, strigolactone and gibberellin biosynthesis, degradation of phospholipids and biosynthesis of glycerolipid, downregulation of ammonium transport and nitrate assimilation, restriction of protein degradation by autophagy and subsequent N remobilization. All these genes are hypothesized to regulate acquisition, assimilation and remobilization of N and P. Based on transcriptional level gene regulation and physiological responses to N or P limitation, we suggest S. bescii plays a critical role in modulating stress imposed by limitation of these two critical nutrients in winter wheat.

Volatile fatty acids production from waste activated sludge during anaerobic fermentation: The effect of superfine sand.

Superfine sand in waste activated sludge (WAS) increased the uncertainty of anaerobic fermentation. Experiments showed that VFAs production from WAS was positively affected by superfine sand, with an increase from 2513 mg COD/L in the control (without superfine sand) to 3002 mg COD/L with superfine sand. A mechanism study demonstrated that the main factor responsible for the improved VFAs accumulation in response to superfine sand was acetic acid, which increased by nearly 30%. Further investigation exhibited that the process of solubilization and acidification were facilitated by superfine sand and the abundance of anaerobic functional microorganisms was greatly increased. Moreover, the activities of acetate kinase (AK) as well as the quantity of AK encoding gene were greatly promoted by superfine sand. The heat release during WAS anaerobic fermentation with superfine sand was higher than that without superfine sand (25.8 × 10-3 versus 24.7 × 10-3 W·min at about 70 min).

Nitrogen-doped porous carbon derived from digested sludge for electrochemical reduction of carbon dioxide to formate.

Carbon-based materials have been applied as cost-effective electrocatalysts to reduce carbon dioxide (CO2) into valuable chemicals. Here, an environment-friendly method is proposed to obtain nitrogen-doped porous carbons (NPCs) from digested sludge, which is an abundant waste product from sewage treatment plants. The materials were used as a metal-free electrocatalyst for electrochemical reduction of CO2 to formate. The synthesized material (NPC-600) had a mesoporous and microporous structure with a specific surface area of 246.21 m2 g-1 and pore volume of 0.494 cm3 g-1. Active sites based on nitrogen atoms accounted for 2.98 atom% of the content and included pyrrolic-, pyridinic-, and graphitic-N, which is useful for CO2 adsorption and electron transfer in electrochemical reduction. The Faradaic efficiency for formate production from CO2 in the presence of NPC-600 was 68% at the potential of -1.5 V vs. SCE. Tafel analysis indicated that the pathway of CO2 conversion involved the reduction of CO2 to CO2*- intermediate, which was then converted to HCOO*- and finally formate.

Bisphenol A alters volatile fatty acids accumulation during sludge anaerobic fermentation by affecting amino acid metabolism, material transport and carbohydrate-active enzymes.

Bisphenol A (BPA), a typical persistent organic pollutant in waste activated sludge, was chosen to explore its influence on the accumulation of volatile fatty acids (VFAs), which is an important raw material, during anaerobic fermentation. BPA in the range of 0-200 mg/kg dry sludge was beneficial to VFAs production, from 1564 mg chemical oxygen demand (COD)/L in the control to 2095 mg COD/L with 50 mg/kg BPA; the acetic acid yield was 563 and 1010 mg COD/L with 0 and 50 mg/kg BPA, respectively. The abundance of microorganisms that can consume VFAs was reduced and those responsible for producing VFAs was increased by BPA. Homologous genes of related enzymes in the pathways for amino acid metabolism, fatty acid biosynthesis, ABC transporters and quorum sensing were enhanced in the presence of BPA. The abundance of carbohydrate-active enzymes increased with BPA when compared with the control, benefitting VFAs production.

[Removal Efficiency of Trichloroacetamide by UV/Sodium Sulfite].

Haloacetamides (HAcAms) are an emerging class of nitrogenous disinfection by-products (N-DBPs) with high cytotoxicity and genotoxicity, which are widely detected in drinking water. The toxicity of trichloroacetamide (TCAcAm) is significantly higher than traditional DBPs. In this study, ultraviolet (UV) treatment was combined with sodium sulphite (Na2SO3) to remove TCAcAm from water. The effects of different light intensities, different agent dosages (Na2SO3), and pH conditions on the removal of TCAcAm by UV/Na2SO3 advanced reduction process were investigated. The results showed that TCAcAm could be rapidly degraded by the UV/Na2SO3 system. The degradation effect was directly proportional to light intensity, dosage of Na2SO3, and pH. Moreover, the pH had a significant effect on the reaction rate and degradation rate. As the pH increased from 6 to 9, the degradation rate of TCAcAm increased from 12.8% to 99.6%, in 120 min. The removal rate of TCAcAm reached 99.4% when the UV light intensity, pH, Na2SO3 dosage, and reaction time were 450 µW·cm-2, 9, 1.00 mmol·L-1, and 30 min, respectively. The experimental results indicated that the UV/Na2SO3 system is an efficient advanced reduction process for the removal of TCAcAm, and it has the potential to reduce other halogenated DBPs. Therefore, it could be used for the degradation of halogenated DBPs in the treatment of drinking water.

Scavenging organic nitrogen and remodelling lipid metabolism are key survival strategies adopted by the endophytic fungi, Serendipita vermifera and Serendipita bescii to alleviate nitrogen and phosphorous starvation in vitro.

Serendipitaceae represents a diverse fungal group in the Basidiomycota that includes endophytes and lineages that repeatedly evolved ericoid, orchid and ectomycorrhizal lifestyle. Plants rely upon both nitrogen and phosphorous, for essential growth processes, and are often provided by mycorrhizal fungi. In this study, we investigated the cellular proteome of Serendipita vermifera MAFF305830 and closely related Serendipita vermifera subsp. bescii NFPB0129 grown in vitro under (N) ammonium and (P) phosphate starvation conditions. Mycelial growth pattern was documented under these conditions to correlate growth-specific responses to nutrient starvation. We found that N-starvation accelerated hyphal radial growth, whereas P-starvation accelerated hyphal branching. Additionally, P-starvation triggers an integrated starvation response leading to remodelling of lipid metabolism. Higher abundance of an ammonium transporter known to serve as both an ammonium sensor and stimulator of hyphal growth was detected under N-starvation. Additionally, N-starvation led to strong up-regulation of nitrate, amino acid, peptide, and urea transporters, along with several proteins predicted to have peptidase activity. Taken together, our finding suggests S. bescii and S. vermifera have the metabolic capacity for nitrogen assimilation from organic forms of N compounds. We hypothesize that the nitrogen metabolite repression is a key regulator of such organic N assimilation.

Organic dye removal from aqueous solutions by hierarchical calcined Ni-Fe layered double hydroxide: Isotherm, kinetic and mechanism studies.

Hierarchically porous nickel-iron-layered double hydroxide (NiFe-LDH) with a Ni2+/Fe3+ molar ratio of 3 was successfully synthesised through a simple hydrothermal route. After calcination at 400°C, NiFe-LDH transformed into nickel-iron-layered double oxides (NiFe-LDO). The as-prepared samples were characterised through X-ray powder diffraction, field emission scanning electron microscopy, energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and nitrogen adsorption. The calcined and uncalcined NiFe-LDH was used as adsorbents to remove Congo red (CR) dye in an aqueous solution. The equilibrium adsorption data of NiFe-LDH and NiFe-LDO samples were well fitted to Langmuir model and were characterised by excellent adsorption capacities of 205 and 330mg/g, respectively. Pseudo-second-order kinetic and intra-particle diffusion models indicated that CR was well adsorbed on the adsorbent. The underlying adsorption mechanism was investigated and observed as anion exchange and reconstruction.

Non-targeted Colonization by the Endomycorrhizal Fungus, Serendipita vermifera, in Three Weeds Typically Co-occurring with Switchgrass.

Serendipita vermifera (=Sebacina vermifera; isolate MAFF305830) is a mycorrhizal fungus originally isolated from the roots of an Australian orchid that we have previously shown to be beneficial in enhancing biomass yield and drought tolerance in switchgrass, an important bioenergy crop for cellulosic ethanol production in the United States. However, almost nothing is known about how this root-associated fungus proliferates and grows through the soil matrix. Such information is critical to evaluate the possibility of non-target effects, such as unintended spread to weedy plants growing near a colonized switchgrass plant in a field environment. A microcosm experiment was conducted to study movement of vegetative mycelia of S. vermifera between intentionally inoculated switchgrass (Panicum virgatum L.) and nearby weeds. We constructed size-exclusion microcosms to test three different common weeds, large crabgrass (Digitaria sanguinalis L.), Texas panicum (Panicum texanum L.), and Broadleaf signalgrass (Brachiaria platyphylla L.), all species that typically co-occur in Southern Oklahoma and potentially compete with switchgrass. We report that such colonization of non-target plants by S. vermifera can indeed occur, seemingly via co-mingled root systems. As a consequence of colonization, significant enhancement of growth was noted in signalgrass, while a mild increase (albeit not significant) was evident in crabgrass. Migration of the fungus seems unlikely in root-free bulk soil, as we failed to see transmission when the roots were kept separate. This research is the first documentation of non-targeted colonization of this unique root symbiotic fungus and highlights the need for such assessments prior to deployment of biological organisms in the field.

Evaluation of the Treatment Process of Landfill Leachate Using the Toxicity Assessment Method.

Landfill leachate is composed of a complex composition with strong biological toxicity. The combined treatment process of coagulation and sedimentation, anaerobics, electrolysis, and aerobics was set up to treat landfill leachate. This paper explores the effect of different operational parameters of coagulation and sedimentation tanks and electrolytic cells, while investigating the combined process for the removal efficiency of physicochemical indices after processing the landfill leachate. Meanwhile, a battery of toxicity tests with Vibrio fischeri, zebrafish larvae, and embryos were conducted to evaluate acute toxicity and calculated the toxicity reduction efficiency after each treatment process. The combined treatment process resulted in a 100% removal efficiency of Cu, Cd and Zn, and a 93.50% and an 87.44% removal efficiency of Ni and Cr, respectively. The overall removal efficiency of chemical oxygen demand (COD), ammonium nitrogen (NH4⁺-N), and total nitrogen (TN) were 93.57%, 97.46% and 73.60%, respectively. In addition, toxicity test results showed that the acute toxicity of landfill leachate had also been reduced significantly: toxicity units (TU) decreased from 84.75 to 12.00 for zebrafish larvae, from 82.64 to 10.55 for zebrafish embryos, and from 3.41 to 0.63 for Vibrio fischeri. The combined treatment process was proved to be an efficient treatment method to remove heavy metals, COD, NH4⁺-N, and acute bio-toxicity of landfill leachate.