Iron is one of the necessary trace elements for plant growth and the human body. The ‘hidden hunger’ phenomenon in the human body caused by an imbalance of iron in soil is increasingly prominent. Addressing this issue and optimizing soil through regulatory measures to improve the absorption and utilization of iron by crops has become an urgent priority in agricultural development. This study carries out pot experiments to observe the growth process of Triticum aestivum L. under various soil iron environments. Combined with previous research results, the transport mechanism of iron in the soil—Triticum aestivum L. system was systematically explored. The results indicate that during the jointing and maturity stages of Triticum aestivum L., iron was preferentially enriched in the underground parts; at the maturity stage, the iron content in various organs of Triticum aestivum L. shows a trend of increase followed by a decrease with the soil iron content varying in the following sequence: deficient, moderately deficient, medium, moderately adequate, and adequate. The iron-deficient stress environment causes an increase in the effectiveness of rhizosphere iron, resulting in a higher level of iron in the plant stems, leaves, and seeds. Conversely, when the soil iron content is medium or adequate, the effectiveness of rhizosphere iron decreases, leading to a reduction in the iron content in each part of the plant. A concentration gradient of 7.2 mg/kg in the experimental setup is found to be the most favorable to the enrichment of iron in the shoots of Triticum aestivum L. plants. The findings of this experiment provide guidance for the fertilization strategy to mitigate iron deficiency symptoms in plants under similar acidic-alkaline conditions of soil, as well as a systematic mechanism reference and basis for studying the soil-plant-human health relationship.

This article presents a novel distributed model predictive control (DMPC) method for multilevel converters with multiple switching configurations. The central aspect of the control approach is to organize the dynamic game among multiple subcontrollers and achieve the Nash equilibrium to realize the deadbeat control of the multilevel converter. Compared with the conventional lumped structure finite control set model predictive control (FCS-MPC) method, DMPC is more flexible in structure and easy to expand. Moreover, the output of DMPC is duty cycle for modulation resulting in fixed switching frequency and improved ripple. The DMPC method has been experimentally validated with a 100 kHz GaN-based five-level flying capacitor (FC) converter. Experimental results show that compared with FCS-MPC, the current ripple, the voltage ripple, and the total harmonic distortion (THD) under DMPC control are reduced by 80%, 70%, and 90%, respectively. At the same time, the computational burden of DMPC is only 16% of that of FCS-MPC. These advantages would promote the implementation of fixed frequency and deadbeat nonlinear control of the FCMC at high levels.

This study innovatively leveraged proximal remote sensing to address the challenge of mineral exploration in vegetation-covered regions. Remote and proximal sensing has proven to be highly effective in pinpointing surface-exposed alteration minerals and detecting potential mining sites in previously unproductive areas. However, in regions where vegetation is abundant, the presence of foliage poses a significant challenge to mineral exploration efforts, creating a natural barrier that hinders the search for valuable minerals. In this study, we explored the linear relationship between the spectral changes induced by metals (specifically Fe and Mo) in wheat plants and the concentrations of these metal elements in different parts of the plant canopy at various growth stages. This investigation was conducted through meticulously designed controlled experiments to understand the interaction between metal elements in the soil and wheat plants. We have established linear models linking wheat biochemistry, vegetation spectroscopy, and soil concentration gradients of Fe and Mo. The analysis of Fe and Mo concentrations in leaves and wheat spikes across varying soil concentration gradients revealed significant positive correlations between the canopy accumulation sites and soil element concentrations (p < 0.05), with a correlation coefficient (R) exceeding 0.85, affirming the representativeness of these two canopy sites for subsequent spectral analysis and modeling. Regarding the wheat spectral analysis, the absorption features at specified wavelengths were identified as significant for creating valid linear models to analyze the effect of Fe and Mo in wheat leaf and spike spectra. Comparing the univariate (URL) and multivariate (MLR) models demonstrated that MLR modeling, incorporating multiple absorption feature parameters, provided more accurate results compared to scenarios with only one absorption feature in the modeling process (MLR: Fe-leaf: R2 = 0.941, RMSE = 1.171; Mo-spike: R2 = 0.934, RMSE = 0.042). To conclude, this study introduces a novel method that exploits the wheat spectral properties observed across different canopy sections during various growth stages of vegetation and under varying concentrations of Fe and Mo gradients. The methodology elucidated in this research provides technical support and lays the theoretical foundation for evaluating mineral resources in vegetated areas.

Remote sensing identification of rare metal deposit dikes in the north margin of North China block has important practical needs and theoretical significance.In view of the technical difficulties of vein recognition and the special geological background of the study area,it proposes a"Space-Sky-Ground"synchronous thermal infrared remote sensing algorithm for Nb-Ta polymetallic deposit vein recognition to assist the identification of metallogenic veins.The algorithm uses landsat-8 satellite data and UAV thermal infrared data collected synchronously as the main data source to calculate and correct the surface specific emissivity.Combined with the emission characteristics of Tianhe Petrochemical albite granite and threshold segmentation method,13 dike development areas were delimited and verified in the field.The results show that the accuracy and reliability of the proposed algorithm are high,and the"Space-Sky-Ground"synchronous thermal infrared remote sensing can be used to identify rare metal dikes in Hutoushan area.This study has provided guidance for the exploration of niobium-tantalum deposits in the study area,and will also provide a useful reference for the remote sensing detection and identification of rare metal deposits.

Human health is directly impacted by trace components found in soil, for which hyperspectral remote sensing technology offers a novel way to rapidly assess dynamic soil environmental quality. In most studies, the premise of quantitative inversion on soil elements is that the known target elements will cause growth stress. However, such stress is unusual in non-polluted areas. Consequently, broad-area soil trace element monitoring in non-contaminated areas remains challenging. Spectral inversion of plant material has a longer time window and is more sensitive to elemental changes than soil spectral inversion (bare soil period). In this work, we conducted a wheat pot experiment with concentration gradients of four trace elements (Fe, B, Mo, and Zn), analyzed leaf (jointing stage) and spike (maturity period) samples using spectral measurements and chemical tests, and filtered the characteristic positions and inverse modeling using the spectra of the element-accumulating preference organs in plant canopies. The canopy aggregation sites differed for each element, and both simple linear regression (SLR) and multiple linear regression (MLR) models based on the spectra of canopy aggregation sites achieved high accuracy. The results of this study enable the construction of an inverse model of plant spectra-plant element content-elements in soil, which can serve as a reference for soil monitoring and assessment in typical crop cover areas.

How to extract the indicative signatures from the spectral data is an important issue for further retrieval based on remote sensing technique. This study provides new insight into extracting indicative signatures by identifying oblique extremum points, rather than local extremum points traditionally known as absorption points. A case study on retrieving soil organic matter (SOM) contents from the black soil region in Northeast China using spectral data revealed that the oblique extremum method can effectively identify weak absorption signatures hidden in the spectral data. Moreover, the comparison of retrieval outcomes using various indicative signature extraction methods reveals that the oblique extremum method outperforms the correlation analysis and traditional extremum methods. The experimental findings demonstrate that the radial basis function (RBF) neural network retrieval model exposes the nonlinear relationship between reflectance (or reflectance transformation results) and the SOM contents. Additionally, an improved oblique extremum method based on the second-order derivative is provided. Overall, this research presents a novel perspective on indicative signature extraction, which could potentially offer better retrieval performance than traditional methods.

Xiong'an New Area is defined as the future city of China, and the regulation of water resources is an important part of the scientific development of the city. Baiyang Lake, the main supplying water for the city, is selected as the study area, and the water quality extraction of four typical river sections is taken as the research objective. The GaiaSky-mini2-VN hyperspectral imaging system was executed on the UAV to obtain the river hyperspectral data for four winter periods. Synchronously, water samples of COD, PI, AN, TP, and TN were collected on the ground, and the in situ data under the same coordinate were obtained. A total of 2 algorithms of band difference and band ratio are established, and the relatively optimal model is obtained based on 18 spectral transformations. The conclusion of the strength of water quality parameters' content along the four regions is obtained. This study revealed four types of river self-purification, namely, uniform type, enhanced type, jitter type, and weakened type, which provided the scientific basis for water source traceability evaluation, water pollution source area analysis, and water environment comprehensive treatment.

Mangrove is the key vegetation in the transitional zone between land and sea, and its health assessment can indicate the deep-level ecological information. The LAI and six key nutrients of mangrove were selected as quantitative evaluation indicators, and the decisive evaluation method of mangrove growth was expected. The mangrove reserve of Dongzhai Port National Nature Reserve in Hainan Province, China, was selected as the study area, with an area of 17.71 km(2). The study area was divided into adjacent urban areas, aquaculture areas, and agricultural production areas, and key indicators are extracted from satellite hyperspectral data. The extraction process includes spectral data preprocessing, spectral transformation, spectral combination, spectral modeling, and precision inspection. The spatial distribution of LAI and six key nutrient components of mangrove in the study area were obtained. LAI and Chla need to calculate the index after high-order differentiation of the spectrum; MSTR and Chlb need to calculate the envelope after the second-order differential of the spectrum; TN and TP are directly changed by original or exponential spectrum; the spectral transformation method adopted by TK was homogenization after first-order differential. The results of the strength of nutrient content along the three regions show that there was no significant difference in the retrieval index of mangroves in the three regions, and the overall health level of mangroves was consistent. Chla was the key identification component of mangrove growth and health. The contents of nutrient elements with correlation coefficient exceeding 0.80 include MSTR and TK (0.98), Chla and TP (0.96), Chla and TK (0.87), MSTR and Chla (0.86), MSTR and TK (0.83), and MSTR and TP (0.81). The study quantifies the relationship between different LAI and nutrient content of mangrove leaves from the perspectives of water, leaf biology, and chemical elements, which improved our understanding of the relationship between key components during mangrove growth for the first time.

This paper presents a vector-based modulated model predictive (MMPC) control for Eight-State Multilevel Converters, which can minimize the control errors and achieve better dynamic performance by properly selecting the fundamental vectors in different operation conditions. A Four-Level Flying Capacitor Boost Converter (4L-FCBC) is simulated to validate the proposed control strategy. The results show that the inductor current, flying capacitor voltage and output voltage ripple of the MMPC are about 5%, 40% and 20% of the MPC, respectively. Meanwhile, the dynamic response speed is also greatly improved.

In the article, one new sensorless model predictive control method using artificial neural network (ANN-SMPC) is presented for multilevel flying capacitor boost converter (FCBC) to address the issue of over-reliance on flying capacitor (FC) voltage sensors. Firstly, the sampling data obtained from simulation environment is used to train the ANN offline, then the trained ANN is applied to the MPC controller instead of the FC voltage sensors for multilevel FCBC. The ANN structure, data selection and training method of ANN-SMPC are introduced in detail. Its feasibility is proved by simulation and test results. The FPGA-based ANN-SMPC controller can provide control performance comparable to traditional MPC while significantly reduce the FC voltage sensors.

This article proposes a new neural network based adaptive model predictive control (named NN-AMPC) for power converters under load parameter uncertainties. Firstly, a supervisor MPC controller is designed for power converter using matched model parameters. Next, a NN is built and trained offline utilizing the operating information from the supervisor controller. A practical adaptive MPC controller using FPGA is then set up utilizing the trained NN to control the power converter online. The proposed NN-AMPC can adaptively track the variation of load parameters without extra identification process of load parameters. The dynamic response of the NN-AMPC under step changes in load parameters are analyzed and compared with conventional MPC. The concept of NN-AMPC is verified by experimental results on a 3-phase voltage source inverter (VSI) as the case study. It is shown that, the FPGA-based NN-AMPC controller offers better dynamic performance in the presence of uncertain parameters while utilizes reduced FPGA resource requirement compared with the observer based MPC controller.

The article proposes a new vector analysis based model predictive control (VAMPC) method for multilevel converters with four different switch configurations. It is based on track of an error vector using fundamental state vectors, and can achieve deadbeat control of four switching states multilevel converters with multiple control objectives. Compared with conventional finite control set model predictive control (FCS-MPC) method, VAMPC can achieve excellent spectrum characteristics in the case of a slight increase in computational burden. The VAMPC method is experimentally validated on a 100-kHz GaN-based three-level flying capacitor (FC) converter. Compared with the FCS-MPC, VAMPC leads to significant reduction in steady-state control errors. For example, the inductor current total harmonic distortion of the FC converter is now reduced by more than 60%.

在全面落实国家"双碳"目标的大背景下,地热资源高质量开发利用已愈来愈重要.天津地热资源禀赋好,开发利用已初具规模,但利用仍然较为粗放,没有形成集约型全产业链发展模式,仍需在资源勘查、地热回灌、动态监测和科学管理等方面全面提升.文章分析了当前天津地热资源开发利用现状和存在问题并提出具体工作措施和建议,未来应加大天津地热资源的整装勘查力度,摸清6000 m以浅地热资源家底,保障资源供给,分区分层确定开采方案;协同攻关地热资源回灌关键技术,在地热资源承载力评价基础上,加大回灌力度,提升资源利用效率;同时做好技术规范和法规标准制定,建成地热资源监测一张网,利用监管机制创新和信息化手段提升管理水平;加大政府扶持力度,"放管服"相结合,开辟多元化融资渠道,保障地热资源勘查开发资金,促进地热产业健康有序融合发展.

There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC.

Varying gate delay time of the Si insulated-gate bipolar transistor/SiC MOSFET hybrid switch is the key to achieving high efficiency of Si/SiC-hybrid-switch-based power converters because of time-varying characteristics of its junction temperature and operation current. Based on the swarm intelligent algorithm, a novel adaptive delay-time control method of the Si/SiC hybrid switch in an inverter is proposed to achieve higher efficiency than the traditional fixed delay time. In addition, by evaluating the fitness values, the proposed method can easily achieve this goal without establishing the physics-based analytical model of device loss and additional hardware support. The platform of the Si/SiC-hybrid-switch-based single-phase inverter is established and tested, and the particle swarm optimization algorithm is taken as example. Experimental results demonstrate that the proposed technique yields 6.2% reduction in the total loss of the single-phase inverter compared to a fixed delay time.

人类活动的不断加剧已逐步影响到地球的健康状况,急需发展有效的地球健康诊断、评估与识别技术.为获悉地球健康状态,需要对地球进行全面的体检.谱遥感技术因具有动态、快速、大范围应用等特点,综合了地物波谱、地学图谱、地表时空演化谱信息,是监测和分析资源、环境乃至生态状况的最佳手段之一,是地球健康状况检测的核心技术.本文在遥感地物波谱特征的基础上,结合遥感揭示地学图谱和地表时空演化谱的优势,提出了谱遥感的定义、谱遥感地球体检应用的内容及其关键技术,总结了实现健康地球的谱遥感应用需求,归纳了天、空、地一体化的谱遥感平台构建方法,并探讨了提高地球体检效果的技术体系,最后对利用谱遥感技术开展地球体检提出了思路和展望.

For a flying capacitor multilevel converter (FCMC), prompt detection of power switch failures is crucial for fault-tolerant operation. This article presents a new technology for identifying and locating faulty cells in FCMC. Mathematical derivation points out that different fault types and locations will present different high-frequency harmonics. The new technology extracts high-frequency harmonics in the switch node voltage and looks up a preestablished table to identify the fault type and location. The discrete Fourier transform (DFT) can be easily performed to analyze the harmonic using the built-in IP core in field-programmable gate array (FPGA). For an FCMC with any number of levels, the new approach requires only one voltage sensor and can accurately locate the faulty cell after one carrier cycle when the fault occurs. Both simulation and experimental results validate the proposed concept on a five-level 100-kHz GaN FET-based FCMC prototype. Furthermore, we have experimentally demonstrated the fault-tolerant operation of the FCMC after a switch short and open fault is detected and identified by the new technique.

Prompt detection of power switch failure is crucial for fault-tolerant operation of multilevel converters. This article presents a new technique of detecting, identifying, and locating a device fault in a multilevel dc-dc flying capacitor (FC) buck converter (FCBC). This is realized by continuously analyzing the magnetic component (inductor or transformer) voltage harmonics for failure detection and subsequently identifying failure type and location with a look up table method. The harmonics analysis is performed by the existing FPGA controller using discrete Fourier transform (DFT). This new approach uses an auxiliary winding in the existing inductor core without adding any new hardware and can identify the location and fault type with good accuracy and speed. The experimental results validate the proposed concept on a three-level GaN-based FCBC prototype.

In this paper, a simple model predictive control (MPC) based on state space averaging model for three-level flying capacitor boost converter (FCBC) is presented. The proposed MPC with constant switching frequency is used as the inner loop controller to control the inductor current and flying capacitor voltage. A new outer loop control strategy for the output voltage is proposed to provide a suitable inductor current reference to improve the dynamic response of the output voltage. Compared with the previous control method for three-level FCBC, the proposed control method shows better dynamic performance. The proposed control method is demonstrated through simulations and experimental results.