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In the process of agricultural modernization, irrigation has the potential to promote utility of water and energy. It is essential to optimal use of resource sustainability. However, smart irrigation is faced with high running cost problems, which cannot meet demand of modern agricultural development. In this study, a low-cost smart irrigation system based on NB-IoT applied to agriculture is proposed. The system introduces three-layer module architecture to improve efficiency. And the experiment introduces multiple methods such as waveform stability test and communication distance test to optimize performance. For NB-IoT stability testing. during operation, the waveform’s carrier frequency offset reached −1.50 MHz (within the standard allowable range) and remained stable, with a peak transmit power of −20 dBm. For communication performance, the optimal communication distance of the system is within 1600 meters, with an ideal packet loss rate of ≤10% (12.8% at 1600 meters). For precision, the average irrigation error of the system is 1.76 (simulation) and 1.74 (field test), which meets industrial accuracy standards. And the total hardware cost of the system is 17.5–29.3% lower than other similar low-cost systems. The proposed NB-IoT-based smart irrigation system enhances practicality, reduces costs, and maintains high accuracy. It provides new insights for advancing smart agriculture and resource sustainability.

Solar-driven electrochemical carbon dioxide (CO 2 ) reduction is capable of producing value-added chemicals and represents a potential route to alleviate carbon footprint in the global environment. However, the ever-changing sunlight illumination presents a substantial impediment of maintaining high electrocatalytic efficiency and stability for practical applications. Inspired by green plant photosynthesis with separate light reaction and (dark) carbon fixation steps, herein, we developed a redox-medium-assisted system that proceeds water oxidation with a nickel-iron hydroxide electrode under light illumination and stores the reduction energy using a zinc/zincate redox, which can be controllably released to spontaneously reduce CO 2 into carbon monoxide (CO) with a gold nanocatalyst in dark condition. This redox-medium-assisted system enables a record-high solar-to-CO photoconversion efficiency of 15.6% under 1-sun intensity, and an outstanding electric energy efficiency of 63%. Furthermore, it allows a unique tuning capability of the solar-to-CO efficiency and selectivity by the current density applied during the carbon fixation. Generating high-energy fuels from sunlight, water, and CO 2 using synthetic materials requires, among many things, the careful separation of reduced and oxidized products. Here, authors employ a zinc-based redox pair to spatially and temporally separate light-driven water oxidation and CO 2 reduction.

The electrochemical conversion of CO 2 to methane provides a means to store intermittent renewable electricity in the form of a carbon-neutral hydrocarbon fuel that benefits from an established global distribution network. The stability and selectivity of reported approaches reside below technoeconomic-related requirements. Membrane electrode assembly-based reactors offer a known path to stability; however, highly alkaline conditions on the cathode favour C-C coupling and multi-carbon products. In computational studies herein, we find that copper in a low coordination number favours methane even under highly alkaline conditions. Experimentally, we develop a carbon nanoparticle moderator strategy that confines a copper-complex catalyst when employed in a membrane electrode assembly. In-situ XAS measurements confirm that increased carbon nanoparticle loadings can reduce the metallic copper coordination number. At a copper coordination number of 4.2 we demonstrate a CO 2 -to-methane selectivity of 62%, a methane partial current density of 136 mA cm −2 , and > 110 hours of stable operation. Electrochemical conversion of carbon dioxide to methane can store intermittent renewable electricity in a staple of global energy. Here, the authors develop a moderator strategy to maintain the catalyst in a low coordination state, thereby enabling stable and selective electrochemical methanation.

Differential interference contrast (DIC) microscopy allows high-contrast, low-phototoxicity, and label-free imaging of transparent biological objects, and has been applied in the field of cellular morphology, cell segmentation, particle tracking, optical measurement and others. Commercial DIC microscopy based on Nomarski or Wollaston prism resorts to the interference of two polarized waves with a lateral differential offset (shear) and axial phase shift (bias). However, the shear generated by these prisms is limited to the rectilinear direction, unfortunately resulting in anisotropic contrast imaging. Here we propose an ultracompact metasurface-assisted isotropic DIC (i-DIC) microscopy based on a grand original pattern of radial shear interferometry, that converts the rectilinear shear into rotationally symmetric along radial direction, enabling single-shot isotropic imaging capabilities. The i-DIC presents a complementary fusion of typical meta-optics, traditional microscopes and integrated optical system, and showcases the promising and synergetic advancements in edge detection, particle motion tracking, and label-free cellular imaging. The authors present a metasurface-assisted isotropic DIC microscopy technique. It is based on an original pattern of radial shear interferometry, that converts rectilinear shear into rotationally symmetric radial shear, enabling single-shot isotropic imaging capabilities.

Distant metastasis is a primary cause of mortality and contributes to poor surgical outcomes in cancer patients. Before the development of organ-specific metastasis, the formation of a pre-metastatic niche is pivotal in promoting the spread of cancer cells. This review delves into the intricate landscape of the pre-metastatic niche, focusing on the roles of tumor-derived secreted factors, extracellular vesicles, and circulating tumor cells in shaping the metastatic niche. The discussion encompasses cellular elements such as macrophages, neutrophils, bone marrow-derived suppressive cells, and T/B cells, in addition to molecular factors like secreted substances from tumors and extracellular vesicles, within the framework of pre-metastatic niche formation. Insights into the temporal mechanisms of pre-metastatic niche formation such as epithelial-mesenchymal transition, immunosuppression, extracellular matrix remodeling, metabolic reprogramming, vascular permeability and angiogenesis are provided. Furthermore, the landscape of pre-metastatic niche in different metastatic organs like lymph nodes, lungs, liver, brain, and bones is elucidated. Therapeutic approaches targeting the cellular and molecular components of pre-metastatic niche, as well as interventions targeting signaling pathways such as the TGF-β, VEGF, and MET pathways, are highlighted. This review aims to enhance our understanding of pre-metastatic niche dynamics and provide insights for developing effective therapeutic strategies to combat tumor metastasis.

Producing liquid fuels such as ethanol from CO 2 , H 2 O, and renewable electricity offers a route to store sustainable energy. The search for efficient electrocatalysts for the CO 2 reduction reaction relies on tuning the adsorption strength of carbonaceous intermediates. Here, we report a complementary approach in which we utilize hydroxide and oxide doping of a catalyst surface to tune the adsorbed hydrogen on Cu. Density functional theory studies indicate that this doping accelerates water dissociation and changes the hydrogen adsorption energy on Cu. We synthesize and investigate a suite of metal-hydroxide-interface-doped-Cu catalysts, and find that the most efficient, Ce(OH) x -doped-Cu, exhibits an ethanol Faradaic efficiency of 43% and a partial current density of 128 mA cm −2 . Mechanistic studies, wherein we combine investigation of hydrogen evolution performance with the results of operando Raman spectroscopy, show that adsorbed hydrogen hydrogenates surface *HCCOH, a key intermediate whose fate determines branching to ethanol versus ethylene. Producing ethanol from carbon dioxide, water, and renewable electricity offers a route to sustainable energy. Here, the authors enhance electrocatalytic activity for carbon dioxide reduction by tuning adsorbed hydrogen in a class of copper catalysts with oxide- and hydroxide-modified surfaces.

The high concentration of hazardous sources in chemical parks, which is prone to cause chain accidents, puts forward the demand for dynamic cooperative optimization of emergency resource scheduling. Aiming at the deficiencies of existing studies in the adaptability of dynamic multi-hazard scenarios and the quantification of resource allocation fairness, this paper constructs a three-objective mixed-integer planning model that integrates time efficiency, demand coverage and allocation fairness. Fairness is innovatively quantified as an independent optimization objective, and a standard deviation-based dynamic resource allocation balance index is proposed, which combines multi-warehouse collaborative supply and multi-resource coupling constraint mechanism to systematically solve the problem of trade-offs between timeliness, adequacy and fairness in emergency dispatching in chemical accidents. The improved NSGA-II algorithm is used to solve the Pareto front efficiently, and the search efficiency is improved by the elite reservation strategy and the congestion adaptive adjustment mechanism. In the case study, comparative experiments with the weighted method and the MOGWO algorithm demonstrate that NSGA-II performs superiorly in key metrics, exhibiting excellent convergence, diversity, and stability. Based on this, a case study is conducted using a chemical industrial park in China as an example, generating 41 sets of weights covering extreme preferences, two-objective balance, and three-objective balance. Decision-makers screen solutions based on loss tolerance thresholds and select the optimal solution using a composite score of comprehensive weighted losses. The study further reveals that improvements in demand satisfaction rates are often accompanied by significant increases in transportation time, while pursuing optimal fairness may weaken overall demand satisfaction levels. Sensitivity analysis confirms that resource demand is the key driver determining the number of feasible solutions, while fairness, as an independent optimization objective, holds irreplaceable importance in emergency scheduling decisions.

China's Spring Festival, with consistent ∼30% NOx (NO + NO2) reductions annually, provides a natural experiment to investigate oxidant response to emission reductions. Unlike isolated events such as the COVID‐19 lockdown, the Spring Festivals offer a more robust decade‐long (2015–2024) data set. Analysis of these observations reveals a striking shift in oxidant (Ox = O3 + NO2) response from negative to positive values over time despite similar emission reduction patterns each year. Chemical transport modeling indicates that meteorological factors are the primary drivers of these variations. Machine learning analysis further identifies cloud cover and radiation changes as controlling factors, with strong correlations between ΔOx and meteorological parameters (R = 0.85–0.94) across all regions. These findings challenge conventional assumptions about emission control effectiveness, showing that meteorological variability overrides expected chemical responses. Our results indicate that emission reduction policies must adaptively account for meteorological conditions to effectively mitigate ozone pollution in a changing climate.

Optical humidity sensors have evolved through decades of research and development, constantly adapting to new demands and challenges. The continuous growth is supported by the emergence of a variety of optical fibers and functional materials, in addition to the adaptation of different sensing mechanisms and optical techniques. This review attempts to cover the majority of optical humidity sensors reported to date, highlight trends in design and performance, and discuss the challenges of different applications.

Real-time object detection on embedded unmanned aerial vehicles (UAVs) is crucial for emergency rescue, autonomous driving, and target tracking applications. However, UAVs’ hardware limitations create conflicts between model size and detection accuracy. Moreover, challenges such as complex backgrounds from the UAV’s perspective, severe occlusion, densely packed small targets, and uneven lighting conditions complicate real-time detection for embedded UAVs. To tackle these challenges, we propose AUHF-DETR, an embedded detection model derived from RT-DETR. In the backbone, we introduce a novel WTC-AdaResNet paradigm that utilizes reversible connections to decouple small-object features. We further replace the original global attention mechanism with the PSA module to strengthen inter-feature relationships within each ROI, thereby resolving the embedded challenges posed by RT-DETR’s complex token computations. In the encoder, we introduce a BDFPN for multi-scale feature fusion, effectively mitigating the small-object detection difficulties caused by the baseline’s Hungarian assignment. Extensive experiments on the public VisDrone2019, HIT-UAV, and CARPK datasets demonstrate that compared with RT-DETR-r18, AUHF-DETR achieves a 2.1% increase in APs on VisDrone2019, reduces the parameter count by 49.0%, and attains 68 FPS (AGX Xavier), thus satisfying the real-time requirements for small-object detection in embedded UAVs.