Risk regulation of water allocation in irrigation areas under changing water supply and demand conditions.

The uncertainty of the hydrological environment and unbalanced water resource allocation result in a high risk of irrigation water shortages in regional agriculture, which seriously affects the sustainable development of agricultural systems. In this paper, we propose a risk regulation based modeling approach for the optimal allocation of agricultural water resources in a complex stochastic environment. The approach includes a conditional value-at-risk (CVaR) model, two-stage stochastic programming (TSP) model, two-dimensional joint distribution probability (JP) model, fractal criteria, and a multiple forms of chance-constrained programming (CCP) model. The model can weigh the contradiction between the intended target and associated penalties attributed to unknown hydrological events, measure the risk between system benefits and expected losses in agricultural water allocation at different confidence levels, and address the randomness in the objective function and constraints (including the left end term, right end term, and left and right end terms). To verify the applicability of the method, it is applied to the Jinxi Irrigation District in China to optimize the allocation and risk regulation of limited water resources under the variable runoff conditions of the Songhua River and crop water demands in the irrigation area. By adjusting parameters such as risk preference and probability of violation, the risk of water shortages in the irrigation area can be regulated, and the multidimensional impacts of different water allocation schemes on agricultural economic benefits, social benefits, ecology and environment can be determined. The case study reveals that the CTSP-CCJP method is sensitive, applicable to complex and uncertain environments and important for the efficient use of agricultural water resources and risk reduction.

Amplitude and frequency modulation based on memristor-controlled spin nano-oscillators.

Spin transfer nano-oscillators (STNOs) are a new type of radio frequency (RF) oscillators that utilize the current-induced magnetization precession in a magnetic tunnel junction device to generate high frequency microwave signal. Since both the frequency and the amplitude of STNOs can be tuned by changing the current, they are potentially used for amplitude shift keying and frequency shift keying modulation without the need for an RF mixer, which leads to compact RF components. In this letter, a novel strategy is proposed to modulate the frequency and the amplitude by memristor-controlled spin nano-oscillators, whereby the STNO is responsible for microwave emitting and memristor serves as a current regulator which further modulates the frequency and amplitude. In addition, the I-V curves show that a multilevel resistance behavior can also be achieved in the same architecture.

Electric-field control of nonvolatile resistance state of perpendicular magnetic tunnel junction via magnetoelectric coupling.

Magnetic tunnel junctions (MTJs) are the core elements of spintronic devices. Now, the mainstream writing operation of MTJs mainly relies on electric current with high energy dissipation, which can be greatly reduced if an electric field is used instead. In this regard, strain-mediated multiferroic heterostructure composed of MTJ and ferroelectrics are promising with the advantages of room temperature and magnetic field-free as already demonstrated by MTJ with in-plane magnetic anisotropy. However, there is no such report on the perpendicular MTJs (p-MTJs), which have been commercialized. Here, we investigate electric-field control of resistance state of MgO-based p-MTJs in multiferroic heterostructures. A remarkable and nonvolatile manipulation of resistance is demonstrated at room temperature without magnetic field assistance. Through various characterizations and micromagnetic simulation, the manipulation mechanism is uncovered. Our work provides an effective avenue for manipulating p-MTJ resistance by electric fields and is notable for high density and ultralow power spintronic devices.

Electric field control of perpendicular magnetic tunnel junctions with easy-cone magnetic anisotropic free layers.

Magnetic tunnel junctions (MTJs) are the core element of spintronic devices. Currently, the mainstream writing operation of MTJs is based on electric current with high energy dissipation, and it can be notably reduced if an electric field is used instead. In this regard, it is promising for electric field control of MTJ in the multiferroic heterostructure composed of MTJ and ferroelectrics via strain-mediated magnetoelectric coupling. However, there are only reports on MTJs with in-plane anisotropy so far. Here, we investigate electric field control of the resistance state of MgO-based perpendicular MTJs with easy-cone anisotropic free layers through strain-mediated magnetoelectric coupling in multiferroic heterostructures. A remarkable, nonvolatile, and reversible modulation of resistance at room temperature is demonstrated. Through local reciprocal space mapping under different electric fields for Pb(Mg1/3Nb2/3)0.7Ti0.3O3 beneath the MTJ pillar, the modulation mechanism is deduced. Our work represents a crucial step toward electric field control of spintronic devices with non-in-plane magnetic anisotropy.

Dynamic and harmonious allocation of irrigation water resources under climate change: A SWAT-based multi-objective nonlinear framework.

Efficient allocation of water resources in irrigation districts can alleviate regional water shortages and promote sustainable irrigated agriculture development. However, existing research on water resource allocation in irrigation districts does not address the lack of coordination within the "diversion-delivery-irrigation" chain for multiple water sources and users in a changing environment. Hence, poor water supply and demand matching, low efficiency and poor climate change responses pose challenges for efficient water resource allocation in irrigation districts. Therefore, this study couples the SWAT runoff simulation model with a multiobjective nonlinear programming model and proposes a weather-driven dynamic and optimal allocation model for multiple water sources. This model accounts for fluctuations in water supply and fine-tunes the allocation of water resources to different water sources, different channels and different crop fertility periods in the irrigation area. The model is designed to achieve synergistic improvements in water supply and demand, economic efficiency, equity in water distribution and efficiency in water use. The model was applied to the Qindeli Irrigation District in Heilongjiang Province. The results show that an increase in water supply at the head of the channel promotes a synergistic increase in economic efficiency and water supply and demand matching. This model can improve water use efficiency under water scarcity by reasonably optimizing the water use structure of the irrigation district. Compared with the traditional irrigation method, the optimized model saves 4 % of water and increases yield by 399 kg/ha, economic efficiency by 0.2 yuan per cubic meter of water, water use efficiency by 9 %, and water supply and demand matching by >80 % at all stages of fertility. The model ensures that water resources are allocated in an equitable manner at all levels.

Role of Systemic Immunoinflammation Landscape in the Overall Survival of Patients with Leptomeningeal Metastases from Lung Cancer.

Purpose: Several biomarkers, such as baseline neutrophil-to-lymphocyte ratio (NLR), have been more investigated in patients with brain metastases (BM), while their role in patients with leptomeningeal metastases (LM) has not been clarified. Considering the difference between the clinical behaviour of BM and LM, there is the need for addressing the role of these biomarkers in LM. Methods: The present study retrospectively analyzed 95 consecutive patients with LM from lung cancer who were diagnosed at the National Cancer Center, Cancer Hospital of Chinese Academy of Medical Sciences between January 2016 and December 2019. Baseline NLR, platelet-to-lymphocyte ratio (PLR), systemic immunoinflammation index (SII), and lymphocyte-to-monocyte ratio at diagnosis of LM were calculated based on complete blood count and correlated, along with other characteristics, with overall survival (OS) using univariate and multivariate analyses. The best cutoff values for systemic immunoinflammation biomarkers were derived using the surv_cutpoint function in R software, which optimized the significance of the split between Kaplan-Meier survival curves. Results: Median OS of patients with LM was 12 months (95% CI 9-17 months). On univariate analysis, NLR, PLR, SII, LMR, sex, smoking history, ECOG performance status (PS) scores, histological subtypes and targeted therapy were all significantly associated with OS. Only NLR (P=0.034, 95% CI 1.060-4.578) and ECOG PS scores (P=0.019, 95% CI 0.137-0.839) maintained a significant association with OS on multivariate analysis. Furthermore, patients with baseline NLR >3.57 had significantly worse OS than patients with NLR ≤3.57 (median OS 7 vs 17 months), as did patients with ECOG PS scores >2 vs ≤2 (median OS 4 vs 15 months). Conclusion: Both baseline NLR and PS scores at the time of LM diagnosis are helpful and available prognostic biomarkers for patients with LM from lung cancer.

Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty.

With the rapid growth of population and economy, shortage and mismatch of land and water resources have deepened the need for cropping pattern optimization. In the context of the sustainable development of agriculture, cropping pattern optimization should not only pursue economic benefits, but the consequent environmental effects also deserve equal attention. Meanwhile, climate change increases the complexity of balancing conflicts of economic-environmental system by cropping pattern optimization. Therefore, this paper builds a multi-objective programming model for Economic-Environmental Synergistic Optimization for Cropping Pattern under Climate Change (EESO-CP-CC) model, with the goals of economic benefit increment and environmental pollutants emission reduction. The EESO-CP-CC model couples a non-point source pollution input-output model, a one-dimensional water quality model and an economic benefit function into an integrated framework. Fuzzy method was used to solve the optimization model, and the stochastic uncertainty of water supply under climate change was quantified by the integration of Bayesian approach and interval linear regression. The model was applied to Jinxi Irrigation District (JXID) in Heilongjiang Province, northeast of China. Results show that by adjusting the acreage of rice, corn and soybean, the harmony degree of economy-society-environment system increased by 10.7% compared to the current situation, indicating that the model tends to achieve the best possible economic benefits while ensuring the environmental effects. Compared with actual cropping pattern, the pollutants emissions reduced by 24.7% and 3% from corn and soybean, respectively. However, this led to a decrease of economic benefit by 8% in exchange, showing the trade-off between environmental pollution reduction and economic benefits improvement. The output coefficients of nitrogen and phosphorus pollutants were optimized, with the optimal output reducing by 20% compared to the standard. Cropping pattern and water resources allocation vary with different climate change conditions, however, the amplitude of variation is modest, indicating that the model can cope well with the changing environment. The developed model can help achieve synergistic development of economic benefits and environmental effects, and thus promote sustainable development of irrigation areas, and improve the coping capacity of agricultural water and land under climate change, by cropping pattern optimization and planning.

High On/Off Ratio Spintronic Multi-Level Memory Unit for Deep Neural Network.

Spintronic devices are considered as one of the most promising technologies for non-volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi-level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi-level memory unit with high on/off ratio is proposed by integrating several series-connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing-in-memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high-accuracy and robust artificial intelligence applications.

Growth and Strain Modulation of GeSn Alloys for Photonic and Electronic Applications.

GeSn materials have attracted considerable attention for their tunable band structures and high carrier mobilities, which serve well for future photonic and electronic applications. This research presents a novel method to incorporate Sn content as high as 18% into GeSn layers grown at 285-320 °C by using SnCl4 and GeH4 precursors. A series of characterizations were performed to study the material quality, strain, surface roughness, and optical properties of GeSn layers. The Sn content could be calculated using lattice mismatch parameters provided by X-ray analysis. The strain in GeSn layers was modulated from fully strained to partially strained by etching Ge buffer into Ge/GeSn heterostructures . In this study, two categories of samples were prepared when the Ge buffer was either laterally etched onto Si wafers, or vertically etched Ge/GeSnOI wafers which bonded to the oxide. In the latter case, the Ge buffer was initially etched step-by-step for the strain relaxation study. Meanwhile, the Ge/GeSn heterostructure in the first group of samples was patterned into the form of micro-disks. The Ge buffer was selectively etched by using a CF4/O2 gas mixture using a plasma etch tool. Fully or partially relaxed GeSn micro-disks showed photoluminescence (PL) at room temperature. PL results showed that red-shift was clearly observed from the GeSn micro-disk structure, indicating that the compressive strain in the as-grown GeSn material was partially released. Our results pave the path for the growth of high quality GeSn layers with high Sn content, in addition to methods for modulating the strain for lasing and detection of short-wavelength infrared at room temperature.

Experimental demonstration of skyrmionic magnetic tunnel junction at room temperature.

Chiral magnetic skyrmions are topological swirling spin textures that hold promise for future information technology. The electrical nucleation and motion of skyrmions have been experimentally demonstrated in the last decade, while electrical detection compatible with semiconductor processes has not been achieved, and this is considered one of the most crucial gaps regarding the use of skyrmions in real applications. Here, we report the direct observation of nanoscale skyrmions in CoFeB/MgO-based magnetic tunnel junction devices at room temperature. High-resolution magnetic force microscopy imaging and tunneling magnetoresistance measurements are used to illustrate the electrical detection of skyrmions, which are stabilized under the cooperation of interfacial Dzyaloshinskii-Moriya interaction, perpendicular magnetic anisotropy, and dipolar stray field. This skyrmionic magnetic tunnel junction shows a stable nonlinear multilevel resistance thanks to its topological nature and tunable density of skyrmions under current pulse excitation. These features provide important perspectives for spintronics to realize high-density memory and neuromorphic computing.

Antiferromagnetic spintronics: An overview and outlook.

Over the past few decades, the diversified development of antiferromagnetic spintronics has made antiferromagnets (AFMs) interesting and very useful. After tough challenges, the applications of AFMs in electronic devices have transitioned from focusing on the interface coupling features to achieving the manipulation and detection of AFMs. As AFMs are internally magnetic, taking full use of AFMs for information storage has been the main target of research. In this paper, we provide a comprehensive description of AFM spintronics applications from the interface coupling, read-out operations, and writing manipulations perspective. We examine the early use of AFMs in magnetic recordings and conventional magnetoresistive random-access memory (MRAM), and review the latest mechanisms of the manipulation and detection of AFMs. Finally, based on exchange bias (EB) manipulation, a high-performance EB-MRAM is introduced as the next generation of AFM-based memories, which provides an effective method for read-out and writing of AFMs and opens a new era for AFM spintronics.

Spin-Torque Memristors Based on Perpendicular Magnetic Tunnel Junctions for Neuromorphic Computing.

Spin-torque memristors are proposed in 2009, and can provide fast, low-power, and infinite memristive behavior for neuromorphic computing and large-density non-volatile memory. However, the strict requirements of combining high magnetoresistance, stable domain wall pinning and current-induced switching in a single device pose difficulties in physical implementation. Here, a nanoscale spin-torque memristor based on a perpendicular-anisotropy magnetic tunnel junction with a CoFeB/W/CoFeB composite free layer structure is experimentally demonstrated. Its tunneling magnetoresistance is higher than 200%, and memristive behavior can be realized by spin-transfer torque switching. Memristive states are retained by strong domain wall pinning effects in the free layer. Experiments and simulations suggest that nanoscale vertical chiral spin textures can form around clusters of W atoms under the combined effect of opposite Dzyaloshinskii-Moriya interactions and the Ruderman-Kittel-Kasuya-Yosida interaction between the two CoFeB free layers. Energy fluctuation caused by these textures may be the main reason for the strong pinning effect. With the experimentally demonstrated memristive behavior and spike-timing-dependent plasticity, a spiking neural network to perform handwritten pattern recognition in an unsupervised manner is simulated. Due to advantages such as long endurance and high speed, the spin-torque memristors are competitive in the future applications for neuromorphic computing.

In-memory direct processing based on nanoscale perpendicular magnetic tunnel junctions.

Perpendicular magnetic tunnel junctions (p-MTJs) provide advantages such as infinite endurance, high thermal stability, and fast and low-power switching. They are considered as a promising non-volatile memory device to build non-von Neumann computing paradigms and definitively overcome the power bottleneck. Numerous design proposals have been made for p-MTJ logic, but a few physical realizations have been reported. In this paper, we present the experimental implementation of universal stateful logic gates such as "OR", "AND", and material implication ("IMP") by connecting two nanoscale p-MTJs in parallel. Owing to the voltage dependence of switching probability for the spin transfer torque mechanism, the same structure can be reconfigured to different logic gates with only electrical signals. One single-cycle operation is thus required for all the basic Boolean functions. Such in-memory direct processing has great potential to meet some key requirements such as a high energy/areal efficiency and high speed for future computing hardware.

Current-induced magnetization switching in atom-thick tungsten engineered perpendicular magnetic tunnel junctions with large tunnel magnetoresistance.

Perpendicular magnetic tunnel junctions based on MgO/CoFeB structures are of particular interest for magnetic random-access memories because of their excellent thermal stability, scaling potential, and power dissipation. However, the major challenge of current-induced switching in the nanopillars with both a large tunnel magnetoresistance ratio and a low junction resistance is still to be met. Here, we report spin transfer torque switching in nano-scale perpendicular magnetic tunnel junctions with a magnetoresistance ratio up to 249% and a resistance area product as low as 7.0 Ω µm2, which consists of atom-thick W layers and double MgO/CoFeB interfaces. The efficient resonant tunnelling transmission induced by the atom-thick W layers could contribute to the larger magnetoresistance ratio than conventional structures with Ta layers, in addition to the robustness of W layers against high-temperature diffusion during annealing. The critical switching current density could be lower than 3.0 MA cm-2 for devices with a 45-nm radius.

Skyrmions in Magnetic Tunnel Junctions.

In this work, we demonstrate that skyrmions can be nucleated in the free layer of a magnetic tunnel junction (MTJ) with Dzyaloshinskii-Moriya interactions (DMIs) by a spin-polarized current with the assistance of stray fields from the pinned layer. The size, stability, and number of created skyrmions can be tuned by either the DMI strength or the stray field distribution. The interaction between the stray field and the DMI effective field is discussed. A device with multilevel tunneling magnetoresistance is proposed, which could pave the ways for skyrmion-MTJ-based multibit storage and artificial neural network computation. Our results may facilitate the efficient nucleation and electrical detection of skyrmions.

Optically Tunable Magnetoresistance Effect: From Mechanism to Novel Device Application.

The magnetoresistance effect in sandwiched structure describes the appreciable magnetoresistance effect of a device with a stacking of two ferromagnetic layers separated by a non-magnetic layer (i.e., a sandwiched structure). The development of this effect has led to the revolution of memory applications during the past decades. In this review, we revisited the magnetoresistance effect and the interlayer exchange coupling (IEC) effect in magnetic sandwiched structures with a spacer layer of non-magnetic metal, semiconductor or organic thin film. We then discussed the optical modulation of this effect via different methods. Finally, we discuss various applications of these effects and present a perspective to realize ultralow-power, high-speed data writing and inter-chip connection based on this tunable magnetoresistance effect.

All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work.

Failure Analysis in Magnetic Tunnel Junction Nanopillar with Interfacial Perpendicular Magnetic Anisotropy.

Magnetic tunnel junction nanopillar with interfacial perpendicular magnetic anisotropy (PMA-MTJ) becomes a promising candidate to build up spin transfer torque magnetic random access memory (STT-MRAM) for the next generation of non-volatile memory as it features low spin transfer switching current, fast speed, high scalability, and easy integration into conventional complementary metal oxide semiconductor (CMOS) circuits. However, this device suffers from a number of failure issues, such as large process variation and tunneling barrier breakdown. The large process variation is an intrinsic issue for PMA-MTJ as it is based on the interfacial effects between ultra-thin films with few layers of atoms; the tunneling barrier breakdown is due to the requirement of an ultra-thin tunneling barrier (e.g., <1 nm) to reduce the resistance area for the spin transfer torque switching in the nanopillar. These failure issues limit the research and development of STT-MRAM to widely achieve commercial products. In this paper, we give a full analysis of failure mechanisms for PMA-MTJ and present some eventual solutions from device fabrication to system level integration to optimize the failure issues.