Variation of surface solar radiation components from 2016 to 2020 in China: Perspective from geostationary satellite observation with a high spatiotemporal resolution.

At present, traditional satellite datasets still grapple with inadequacies in terms of capturing solar radiation with fine spatiotemporal granularity. This study utilizes the high spatiotemporal resolution of CARE data, which is developed based on geostationary satellite observations, and employs multivariate analysis techniques to conduct an in-depth investigation into the multidimensional spatiotemporal variations of different types of solar radiation across various regions in China from 2016 to 2020. In addition, the potential of solar energy resources was also assessed using cluster analysis method. The results revealed an upward trend in different components of solar radiation across most of China, with shortwave radiation exhibiting a significantly negative correlation with PM2.5 concentrations (R = -0.91, p < 0.05). This finding suggests that the increase in SWR may be attributed to the effective implementation of China's Air Pollution Prevention and Control Action Plan. It suggests that China's endeavors to mitigate air pollution have not only resulted in improvements in national air quality but have also had an indirect positive effect on enhancing the potential for photovoltaic power generation. The assessment of solar energy resources potential indicated, with 99 % statistical confidence, that western China constitutes the core region for solar energy resources development, whereas northeastern and southeastern regions face certain constraints in solar energy resources utilization. Furthermore, we examined the specific influence of atmospheric circulation patterns on solar energy resources, uncovering that the El Niño phenomenon, by altering cloud distribution and variability, indirectly affects solar radiation intensity in specific regions. This study aids in understanding China's solar radiation variations, crucial for shaping effective energy policies towards carbon neutrality.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

A novel RAV transcription factor from pear interacts with viral RNA-silencing suppressors to inhibit viral infection.

In plants, RNA silencing constitutes a strong defense against viral infection, which viruses counteract with RNA-silencing suppressors (RSSs). Understanding the interactions between viral RSSs and host factors is crucial for elucidating the molecular arms race between viruses and host plants. We report that the helicase motif (Hel) of the replicase encoded by apple stem grooving virus (ASGV)-the main virus affecting pear trees in China-is an RSS that can inhibit both local and systemic RNA silencing, possibly by binding double-stranded (ds) siRNA. The transcription factor related to ABSCISIC ACID INSENSITIVE3/VIVIPAROUS1 from pear (PbRAV1) enters the cytoplasm and binds Hel through its C terminus, thereby attenuating its RSS activity by reducing its binding affinity to 21- and 24-nt ds siRNA, and suppressing ASGV infection. PbRAV1 can also target p24, an RSS encoded by grapevine leafroll-associated virus 2 (GLRaV-2), with similar negative effects on p24's suppressive function and inhibition of GLRaV-2 infection. Moreover, like the positive role of the PbRAV1 homolog from grapevine (VvRAV1) in p24's previously reported RSS activity, ASGV Hel can also hijack VvRAV1 and employ the protein to sequester 21-nt ds siRNA, thereby enhancing its own RSS activity and promoting ASGV infection. Furthermore, PbRAV1 neither interacts with CP, an RSS encoded by grapevine inner necrosis virus, nor has any obvious effect on CP's RSS activity. Our results identify an RSS encoded by ASGV and demonstrate that PbRAV1, representing a novel type of RAV transcription factor, plays a defensive role against viral infection by targeting viral RSSs.

Engineering of MnTe/MnO Heterostructures with Interfacial Electric Field Modulation for Efficient and Durable Li-O2 Batteries.

Design and synthesis of highly active and robust bifunctional cathode catalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are of vital significance for practical applications of lithium-oxygen (Li-O2) batteries. Herein, a built-in electric field (BIEF) strategy is reported to fabricate MnTe/MnO heterostructures with a large work function difference (ΔΦ) as a bifunctional cathode catalyst in Li-O2 batteries. The MnTe/MnO heterostructures with nanosheets and microporous structures result in an abundance of exposed active sites and facilitate mass transfer. More importantly, the heterogeneous MnTe/MnO nano-interface region provides a BIEF that can trigger interfacial charge redistribution, fine-tune the adsorption energy of oxygen intermediates, and alter the morphology of discharge products to accelerate ORR/OER kinetics. Impressively, the fabricated Li-O2 batteries with MnTe/MnO cathode showcases exhibit excellent electrochemical performances, including low charging overpotential, a high specific capacity of 11930 mA h g-1, and good cycle stability over 350 cycles even with a fixed specific capacity of 500 mA h g-1 at a current density of 500 mA g-1. This work provides an avenue for the rational design of high performance heterostructure electrocatalysts toward practical applications for rechargeable Li-O2 batteries.

Machine learning enables electrical resistivity modeling of printed lines in aerosol jet 3D printing.

Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2-5 mm) and higher resolution (~ 10 µm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

Long-Term Straw Incorporation under Controlled Irrigation Improves Soil Quality of Paddy Field and Rice Yield in Northeast China.

Soil quality is an indicator of the ability to ensure ecological security and sustainable soil usage. The effects of long-term straw incorporation and different irrigation regimes on the yield and soil quality of paddy fields in cold regions remain unclear. This study established four treatments: controlled irrigation + continuous straw incorporation for 3 years (C3), controlled irrigation + continuous straw incorporation for 7 years (C7), flooded irrigation + continuous straw incorporation for 3 years (F3), and flooded irrigation + continuous straw incorporation for 7 years (F7). Analysis was conducted on the impact of various irrigation regimes and straw incorporation years on the physicochemical characteristics and quality of the soil. The soil quality index (SQI) for rice fields was computed using separate datasets for each treatment. The soil nitrate nitrogen, available phosphorus, soil organic carbon, and soil organic matter contents of the C7 were 93.51%, 5.80%, 8.90%, and 8.26% higher compared to C3, respectively. In addition, the yield of the C7 treatment was 5.18%, 4.89%, and 10.32% higher than those of F3, C3, and F7, respectively. The validity of the minimum data set (MDS) was verified by correlation, Ef and ER, which indicated that the MDS of all treatments were able to provide a valid evaluation of soil quality. The MDS based SQI of C7 was 11.05%, 11.97%, and 27.71% higher than that of F3, C3, and F7, respectively. Overall, long-term straw incorporation combined with controlled irrigation increases yield and soil quality in paddy fields in cold regions. This study provides a thorough assessment of soil quality concerning irrigation regimes and straw incorporation years to preserve food security and the sustainability of agricultural output. Additionally, it offers a basis for soil quality diagnosis of paddy fields in the Northeast China.

Self-Catalyzed Growth of Co4N and N-Doped Carbon Nanotubes toward Bifunctional Cathode for Highly Safe and Flexible Li-Air Batteries.

The practical application of high-energy density lithium-oxygen (Li-O2) batteries is severely impeded by the notorious cycling stability and safety, which mainly comes from slow kinetics of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) at cathodes, causing inferior redox overpotentials and reactive lithium metal in flammable liquid electrolyte. Herein, a bifunctional electrode, a safe gel polymer electrolyte (GPE), and a robust lithium anode are proposed to alleviate above problems. The bifunctional electrode is composed of N-doped carbon nanotubes (N-CNTs) and Co4N by in situ chemical vapor deposition self-catalyzed growth on carbon cloth (N-CNTs@Co4N@CC). The self-supporting, binder-free N-CNTs@Co4N@CC electrode has a strong and stable three-dimensional (3D) interconnected conductive structure, which provides interconnectivity between the active sites and the electrode to promote the transfer of electrons. Furthermore, the N-CNT-intertwined Co4N ensures efficient catalytic activity. Hence, the electrode demonstrates improved electrochemical properties even under a large current density (2000 mA g-1) and long cycling operation (250 cycles). Moreover, a highly safe and flexible rechargeable cell using the 3D N-CNTs@Co4N@CC electrode, GPE, and robust lithium anode design has been explored. The open circuit voltage is stable at ∼3.0 V even after 9800 cycles, which proves the mechanical durability of the integrated GPE cell. The stable cable-type Li-air battery was demonstrated to stably drive the light-emitting diodes (LEDs), highlighting the reliability for practical use.

Advantages of transformer and its application for medical image segmentation: a survey.

PURPOSE: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. METHODS: This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. RESULTS: To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. CONCLUSION: Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.

Characteristics of Oil-in-Oil Emulsions under AC Electric Fields.

Emulsions have been applied in a number of industries such as pharmaceutics, cosmetics, and food, which are also of great scientific interest. Although aqueous emulsions are commonly used in our daily life, oil-in-oil (o/o) emulsions also play an irreplaceable role in view of their unique physics and complementary applications. In this paper, we investigate typical behaviors of organic droplets surrounded by organic medium (o/o emulsions) with different functional groups controlled by the AC electric field. Droplet behaviors can be catalogued into five types: namely, "no effect", "movement", "deformation", "interface rupture", and "disorder". We identify the key dimensionless number Wee·Ca, combined with the channel geometry, for characterizing the typical behaviors in silicon oil/1,6-hexanediol diacrylate and mineral oil/1,6-hexanediol diacrylate emulsions. Unlike aqueous emulsion, the Maxwell-Wagner relaxation inhibits the electric effect and leads to an effective frequency, ranging from 0.5 to 3 kHz. The increasing viscosity of the droplet facilitates the escalation by promoting the shearing effect under the same flow conditions. Ethylene glycol droplets primarily show the efficient coalescence even at a low Wee·Ca, which is attributed to the attraction of free charges induced by the increasing conductivity. In 1,6-hexanediol diacrylate/silicon oil emulsion, the droplet tends to form a liquid film that expands into the entire channel due to the affinity of the droplet to the channel wall. A variety of elongated columns are observed to oscillate between the electrodes at high voltages. These findings can contribute to understanding the electrohydrodynamic physics in o/o emulsion and controlling droplet behaviors in a fast response, programmable, and high-throughput way. We expect that this droplet manipulation technology can be widely adopted in a broad range of chemical synthesis and biological and material science.

Pan-genome Analysis of WOX Gene Family and Function Exploration of CsWOX9 in Cucumber.

Cucumber is an economically important vegetable crop, and the warts (composed of spines and Tubercules) of cucumber fruit are an important quality trait that influences its commercial value. WOX transcription factors are known to have pivotal roles in regulating various aspects of plant growth and development, but their studies in cucumber are limited. Here, genome-wide identification of cucumber WOX genes was performed using the pan-genome analysis of 12 cucumber varieties. Our findings revealed diverse CsWOX genes in different cucumber varieties, with variations observed in protein sequences and lengths, gene structure, and conserved protein domains, possibly resulting from the divergent evolution of CsWOX genes as they adapt to diverse cultivation and environmental conditions. Expression profiles of the CsWOX genes demonstrated that CsWOX9 was significantly expressed in unexpanded ovaries, especially in the epidermis. Additionally, analysis of the CsWOX9 promoter revealed two binding sites for the C2H2 zinc finger protein. We successfully executed a yeast one-hybrid assay (Y1H) and a dual-luciferase (LUC) transaction assay to demonstrate that CsWOX9 can be transcriptionally activated by the C2H2 zinc finger protein Tu, which is crucial for fruit Tubercule formation in cucumber. Overall, our results indicated that CsWOX9 is a key component of the molecular network that regulates wart formation in cucumber fruits, and provide further insight into the function of CsWOX genes in cucumber.

Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding.

Background: Brain-computer interface (BCI) systems based on motor imagery (MI) have been widely used in neurorehabilitation. Feature extraction applied by the common spatial pattern (CSP) is very popular in MI classification. The effectiveness of CSP is highly affected by the frequency band and time window of electroencephalogram (EEG) segments and channels selected. Objective: In this study, the multi-domain feature joint optimization (MDFJO) based on the multi-view learning method is proposed, which aims to select the discriminative features enhancing the classification performance. Method: The channel patterns are divided using the Fisher discriminant criterion (FDC). Furthermore, the raw EEG is intercepted for multiple sub-bands and time interval signals. The high-dimensional features are constructed by extracting features from CSP on each EEG segment. Specifically, the multi-view learning method is used to select the optimal features, and the proposed feature sparsification strategy on the time level is proposed to further refine the optimal features. Results: Two public EEG datasets are employed to validate the proposed MDFJO method. The average classification accuracy of the MDFJO in Data 1 and Data 2 is 88.29 and 87.21%, respectively. The classification result of MDFJO was significantly better than MSO (p < 0.05), FBCSP32 (p < 0.01), and other competing methods (p < 0.001). Conclusion: Compared with the CSP, sparse filter band common spatial pattern (SFBCSP), and filter bank common spatial pattern (FBCSP) methods with channel numbers 16, 32 and all channels as well as MSO, the MDFJO significantly improves the test accuracy. The feature sparsification strategy proposed in this article can effectively enhance classification accuracy. The proposed method could improve the practicability and effectiveness of the BCI system.

Expanding the chemical space of enol silyl ethers: catalytic dicarbofunctionalization enabled by iron catalysis.

Enol silyl ethers are versatile, robust, and readily accessible substrates widely used in chemical synthesis. However, the conventional reactivity of these motifs has been limited to classical two electron (2-e) enolate-type chemistry with electrophilic partners or as radical acceptors in one electron (1-e) reactivity leading, in both cases, to exclusive α-monofunctionalization of carbonyls. Herein we describe a mild, fast, and operationally simple one-step protocol that combines readily available fluoroalkyl halides, silyl enol ethers, and, for the first time, hetero(aryl) Grignard reagents to promote selective dicarbofunctionalization of enol silyl ethers. From a broader perspective, this work expands the synthetic utility of enol silyl ethers and establishes bisphosphine-iron catalysis as enabling technology capable of orchestrating selective C-C bond formations with short-lived α-silyloxy radicals with practical implications towards sustainable chemical synthesis.

Effect of acupuncture for patients with knee osteoarthritis: study protocol for a double-dummy randomized controlled trial.

BACKGROUND: Acupuncture has been used to relieve chronic pain in patients with knee osteoarthritis (KOA), but the evidence is contradictory. Therefore, we carefully designed a double-dummy randomized controlled trial (RCT) to explore the therapeutic effect of acupuncture for KOA. METHODS/DESIGN: A total of 138 eligible participants with KOA who consent to participate will be randomly divided into Groups A, B, and C in a ratio of 1:1:1. Participants in Group A will receive verum acupuncture and placebo gel, while those in Groups B and C will be treated with diclofenac diethylammon gel and sham acupuncture, sham acupuncture and placebo gel, respectively. The patients will receive 4 weeks of treatment, five times a week, including acupuncture treatment once a day for 30 min and gel treatment three times a day. The primary outcome will be the change of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at week 4. The secondary outcomes will include visual analog scale (VAS), Arthritis Quality of Life Measurement Scale Simplified Scale (AIMS2-SF), Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI) and Credibility/Expectancy Questionnaire. The evaluation will be performed at baseline, week 4, 8, and 12 after randomization. DISCUSSION: This double-dummy RCT used diclofenac diethylammon gel as a positive control, and the completion of this trial will provide detailed and accurate evidence of the efficacy and safety of acupuncture for KOA. TRIAL REGISTRATION: China Clinical Trials Registry No.ChiCTR2100043947. Registered on September 24, 2020. https://www.chictr.org.cn/showproj.html?proj=122536 .

Compound Droplet Impact on a Thin Hydrophobic Cylinder.

The impact of compound droplets on solid surfaces is a ubiquitous phenomenon that pervades both the natural and technological fields. A comprehensive understanding of the dynamics of the droplet impact on solid surfaces is therefore of paramount importance for a broad range of applications. In this study, we investigate the impact of a water-in-oil compound droplet on a thin hydrophobic cylindrical surface, with regard to the Weber number and cylinder dimensions. Owing to the prewetting effect of the oil, the droplet completely engulfs the cylinder during impact. The ensuing breakups of oil and water engender various unique impact outcomes, which are depicted via a phase map. The phase boundaries are described by analyzing the gravitational and drag forces exerted by the cylinder. A threshold value of the Weber number is found beyond which its effect on the azimuthal spreading process becomes less obvious. The distinctive axial spreading processes of oil and water are illustrated through high-speed imaging from both front and side perspectives, revealing that droplet oscillation is critically influenced by the Weber number. Our work elucidates the impact dynamics of compound droplets on curved surfaces, providing pivotal insights into related thermal management, droplet printing, and coating fabrication applications.

Clinical-functional brain connectivity signature predicts longitudinal symptom improvement after acupuncture treatment in patients with functional dyspepsia.

Whilst acupuncture has been shown to be an effective treatment for functional dyspepsia (FD), its efficacy varies significantly among patients. Knowing beforehand how each patient responds to acupuncture treatment will facilitate the ability to produce personalized prescriptions, therefore, improving acupuncture efficacy. The objective of this study was to construct the prediction model, based on the clinical-neuroimaging signature, to forecast the individual symptom improvement of FD patients following a 4-week acupuncture treatment and to identify the critical predictive features that could potentially serve as biomarkers for predicting the efficacy of acupuncture for FD. Clinical-functional brain connectivity signatures were extracted from samples in the training-test set (100 FD patients) and independent validation set (60 FD patients). Based on these signatures and support vector machine algorithms, prediction models were developed in the training test set, followed by model performance evaluation and predictive features extraction. Subsequently, the external robustness of the extracted predictive features in predicting acupuncture efficacy was evaluated by the independent validation set. The developed prediction models possessed an accuracy of 88% in predicting acupuncture responders, as well as an R2 of 0.453 in forecasting symptom relief. Factors that contributed significantly to stronger responsiveness of patients to acupuncture therapy included higher resting-state functional connectivity associated with the orbitofrontal gyrus, caudate, hippocampus, and anterior insula, as well as higher baseline scores of the Symptom Index of Dyspepsia and shorter durations of the condition. Furthermore, the robustness of these features in predicting the efficacy of acupuncture for FD was verified through various machine learning algorithms and independent samples and remained stable in univariate and multivariate analyses. These findings suggest that it is both feasible and reliable to predict the efficacy of acupuncture for FD based on the pre-treatment clinical-neuroimaging signature. The established prediction framework will promote the identification of suitable candidates for acupuncture treatment, thereby improving the efficacy and reducing the cost of acupuncture for FD.

EVs-mediated delivery of CB2 receptor agonist for Alzheimer's disease therapy.

Alzheimer's disease (AD) is a typical neurodegenerative disease that leads to irreversible neuronal degeneration, and effective treatment remains elusive due to the unclear mechanism. We utilized biocompatible mesenchymal stem cell-derived extracellular vesicles as carriers loaded with the CB2 target medicine AM1241 (EVs-AM1241) to protect against neurodegenerative progression and neuronal function in AD model mice. According to the results, EVs-AM1241 were successfully constructed and exhibited better bioavailability and therapeutic effects than bare AM1241. The Morris water maze (MWM) and fear conditioning tests revealed that the learning and memory of EVs-AM1241-treated model mice were significantly improved. In vivo electrophysiological recording of CA1 neurons indicated enhanced response to an auditory conditioned stimulus following fear learning. Immunostaining and Western blot analysis showed that amyloid plaque deposition and amyloid ß (Aß)-induced neuronal apoptosis were significantly suppressed by EVs-AM1241. Moreover, EVs-AM1241 increased the number of neurons and restored the neuronal cytoskeleton, indicating that they enhanced neuronal regeneration. RNA sequencing revealed that EVs-AM1241 facilitated Aß phagocytosis, promoted neurogenesis and ultimately improved learning and memory through the calcium-Erk signaling pathway. Our study showed that EVs-AM1241 efficiently reversed neurodegenerative pathology and enhanced neurogenesis in model mice, indicating that they are very promising particles for treating AD.

Unresolved Excess Accumulation of Myelin-Derived Cholesterol Contributes to Scar Formation after Spinal Cord Injury.

Spinal cord injury triggers complex pathological cascades, resulting in destructive tissue damage and incomplete tissue repair. Scar formation is generally considered a barrier for regeneration in the central nervous system. However, the intrinsic mechanism of scar formation after spinal cord injury has not been fully elucidated. Here, we report that excess cholesterol accumulates in phagocytes and is inefficiently removed from spinal cord lesions in young adult mice. Interestingly, we observed that excessive cholesterol also accumulates in injured peripheral nerves but is subsequently removed by reverse cholesterol transport. Meanwhile, preventing reverse cholesterol transport leads to macrophage accumulation and fibrosis in injured peripheral nerves. Furthermore, the neonatal mouse spinal cord lesions are devoid of myelin-derived lipids and can heal without excess cholesterol accumulation. We found that transplantation of myelin into neonatal lesions disrupts healing with excessive cholesterol accumulation, persistent macrophage activation, and fibrosis. Myelin internalization suppresses macrophage apoptosis mediated by CD5L expression, indicating that myelin-derived cholesterol plays a critical role in impaired wound healing. Taken together, our data suggest that the central nervous system lacks an efficient approach for cholesterol clearance, resulting in excessive accumulation of myelin-derived cholesterol, thereby inducing scar formation after injury.

AC-electric-field-controlled multi-component droplet coalescence at microscale.

Droplet coalescence with fast response, high controllability and monodispersity has been widely investigated in industrial production and bioengineering. Especially for droplets with multiple components, programmable manipulation of such droplets is crucial for practical applications. However, precise control of the dynamics can be challenging, owing to the complex boundaries and the interfacial and fluidic properties. AC electric fields, with their fast response and high flexibility, have attracted our interest. We design and fabricate an improved flow-focusing microchannel configuration together with a non-contact type of electrode featuring asymmetric geometries, based on which we conduct systematic investigations of the AC-electric-field-controlled coalescence of multi-component droplets at the microscale. Parameters such as flow rates, component ratio, surface tension, electric permittivity and conductivity were given our attention. The results show that droplet coalescence in different flow parameters can be achieved in milliseconds by adjusting the electrical conditions, which shows high controllability. Specifically, both the coalescence region and reaction time can be adjusted by a combination of applied voltage and frequency, and unique merging phenomena have appeared. One is contact coalescence with the approach of paired droplets, while the other is squeezing coalescence, which occurs in the start position and promotes the merging process. The fluid properties, such as the electric permittivity, conductivity and surface tension, present a significant influence on merging behavior. The increasing relative dielectric constant leads to a dramatic reduction of the start merging voltage from the original 250 V to 30 V. The range of effective voltage for coalescence decreases with the addition of surfactant, offering a stricter and yet higher selectivity on electrical conditions, about 1500 V. The conductivity presents a negative correlation with the start merging voltage due to the reduction of the dielectric stress, from 400 V to 1500 V. Finally, we achieve the precise fabrication process of the Janus droplet via implementation of the proposed method, where the components of the droplets and the coalescence conditions are well controlled. Our results can serve as a potent methodology to decipher the physics of multi-component droplet electro-coalescence and contribute to applications in chemical synthesis, bioassay and material synthesis.

Irrigation Scheduling for Maize under Different Hydrological Years in Heilongjiang Province, China.

Appropriate irrigation schedules could minimize the existing imbalance between agricultural water supply and crop water requirements (ETc), which is severely impacted by climate change. In this study, different hydrological years (a wet year, normal year, dry year, and an extremely dry year) in Heilongjiang Province were calculated by hydrological frequency methods. Then, the single crop coefficient method was used to calculate the maize ETc, based on the daily meteorological data of 26 meteorological stations in Heilongjiang Province from 1960 to 2020. Afterward, the CROPWAT model was used to calculate the effective precipitation (Pe) and irrigation water requirement (Ir), and formulate the irrigation schedules of maize in Heilongjiang Province under different hydrological years. The results showed that ETc and Ir decreased first and then increased from west to east. The Pe and crop water surplus deficit index increased first and then decreased from west to east in Heilongjiang Province. Meanwhile, the average values of the Ir in were 171.14 mm, 232.79 mm, 279.08 mm, and 334.47 mm in the wet year, normal year, dry year, and extremely dry year, respectively. Heilongjiang Province was divided into four irrigation zones according to the Ir of different hydrological years. Last, the irrigation quotas for the wet year, normal year, dry year, and extremely dry year were 0~180 mm, 20~240 mm, 60~300 mm, and 80~430 mm, respectively. This study provides reliable support for maize irrigation practices in Heilongjiang Province, China.