Concentration-dependent effects of boron fertilizer on growth, yield, and quality of buckwheat.

Introduction: Boron is an essential trace element for plant growth and development, playing a critical role in flowering, fruit setting, nutrient transport, and stress resistance in crops. Buckwheat is‌ an important coarse grain crop, ‌and‌ its yield and quality are easily affected by boron nutritional status.

Methods: In order to identify the optimal concentration of boron fertilizer to maximize growth and yield of buckwheat, pot experiments and field trials were carried out at different mass concentrations of boron fertilizer solution: 0, 12, 24, 48, and 72 mg/L.

Response mechanism of ecological environment quality variation to multifactor coupling at Spatiotemporal scales.

The remote sensing ecological index (RSEI) serves as a pivotal metric for evaluating the regional ecological environment quality (EEQ). Nevertheless, accurately quantifying and identifying its response to multi-factor coupling remain a considerable challenge. Therefore, in this study, an improved Remote Sensing Ecological Index with Local Adaptability (RSEILA) method was employed to analyze the EEQ's spatiotemporal distribution pattern using the Google Earth Engine platform.

Projection of ENSO using observation-informed deep learning.

The El Niño-Southern Oscillation (ENSO) profoundly impacts global climate, but its sea surface temperature (SST) variability projected by climate models remains uncertain, with a substantial inter-model spread in 21st-century projections. Model-observation discrepancies in ENSO physics contribute to this uncertainty, necessitating observational constraints to refine projections. However, methods to achieve this constraint remain unclear.

The westerlies-monsoon interaction shaped asymmetric lake expansions over the Tibetan Plateau in warming periods.

Modelling studies and recent observations suggest that the westerlies-monsoon interactions (WMI) have led to a spatial imbalance in lake expansions across the Tibetan Plateau (TP) during the past decades under anthropogenic warming. However, whether such imbalance reflects a short-term phenomenon or a long-term climatic trend remains unclear. Here, we present a 240,000-year lake level reconstruction from the inner TP to examine the extent to which WMI also operated for previous interglacials, natural warm regimes with large-scale climatic boundary changes.

Interpreting reservoir computing through the equivalent visualization of its loss landscape.

Reservoir computing (RC) has been recognized as a promising ultra-lightweight model, but its black-box nature renders its predictions less interpretable. As a result, interpreting the underlying prediction mechanism of RC is attracting increasing attention. Among the interpretability methods, visualization serves as an intuitive approach for interpreting RC, enabling even novices to directly observe RC loss landscape and its dependency on the parameter.

Dual-function electrodes for photo-rechargeable batteries: mechanisms, designs, and applications.

Photo-rechargeable batteries emerge as an integrated solution for simultaneous solar energy harvesting and storage. Central to these systems are dual-function electrode materials that intrinsically combine photoactivity and ion storage capabilities. Through synergistic interactions with electrolytes, these materials enable efficient energy conversion and storage, demonstrating significant potential for next-generation energy technologies.

[Spatiotemporal Evolution and Prediction of Land Surface Thermal Environment in A Typical Ecological City from 1990 to 2020].

Rapid urbanization leads to the exacerbation of the urban heat island （UHI） effect, which significantly increases the climate risk of urban heatwaves. The construction of ecological cities, which aim for harmonious development between humans and nature, can substantially mitigate the UHI effect. However, research on the long-term evolution of the land surface thermal environment in ecological urban areas is relatively scarce.

[Prediction of Heavy Metal Concentrations in PM in the Agricultural Area of Yangtze River Delta Region Based on Machine Learning].

Heavy metals in PM can considerably impact air quality, human health, and the ecological environment. However, studies on heavy metals in PM in agricultural areas are relatively limited. In this study, observational data on heavy metal concentrations in PM in the Yangtze River Delta Region from 2000 to 2020 were collected.

A big anti-lipopolysaccharide factor showing sexual dimorphism is involved in anti-WSSV immunity in crayfish.

Anti-lipopolysaccharide factor (ALF) is an important active substance in crustacean innate immunity with broad-spectrum antimicrobial and antiviral effects. In this study, we identified a big ALF from Procambarus clarkii, named PcbALF. The full-length cDNAs of two PcbALF isoforms (PcbALF1 and PcbALF2) were 2619 bp and 2142 bp with an open reading frame of 1074 bp and 1317 bp that encodes 357 and 438 amino acids, respectively.

Integrated neural network framework for multi-object detection and recognition using UAV imagery.

Introduction: Accurate vehicle analysis from aerial imagery has become increasingly vital for emerging technologies and public service applications such as intelligent traffic management, urban planning, autonomous navigation, and military surveillance. However, analyzing UAV-captured video poses several inherent challenges, such as the small size of target vehicles, occlusions, cluttered urban backgrounds, motion blur, and fluctuating lighting conditions which hinder the accuracy and consistency of conventional perception systems. To address these complexities, our research proposes a fully end-to-end deep learning-driven perception pipeline specifically optimized for UAV-based traffic monitoring.

A Soft-Fault Diagnosis Method for Coastal Lightning Location Networks Based on Observer Pattern.

Coastal areas are prone to thunderstorms. Lightning strikes can damage power facilities and communication systems, thereby leading to serious consequences. The lightning location network achieves lightning location through data fusion from multiple lightning locator nodes and can detect the location and intensity of lightning in real time.

Failure Analysis and Optimized Simulation Design of Silicon Micromechanical Resonant Accelerometers.

To develop solutions to the frequency instability and failure of silicon micromechanical resonant accelerometers, the state characteristics of micromechanical resonant accelerometers are investigated under temperature and vibration stresses. Through theoretical analysis and finite element simulation, the following is found: the Young's modulus of silicon varies with temperature, causing a resonance frequency shift of -1.364 Hz/°C; the residual stress of temperature change affects the resonance frequency shift of the microstructure, causing it to be 5.

A deep learning derived prostate zonal volume-based biomarker from T2-weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia.

Background: Benign prostatic hyperplasia (BPH) and prostate cancer (PCa) share overlapping characteristics on magnetic resonance imaging (MRI), confounding the diagnosis and detection of PCa. There is thus a clinical need to accurately differentiate BPH-Only from BPH-PCa to prevent overdiagnosis and unnecessary biopsies. Although BPH and PCa may share overlapping features, they are distinct clinical entities.

Atomic Layer Deposition-Derived MoS/NbS Nanoheterojunctions Enable High-Density Quantum Dot Loading for Enhanced Signal-On Photoelectrochemical Detection of cTnI.

Acute myocardial infarction (MI) remains a major global health threat, necessitating rapid and sensitive detection strategies. Herein, we report a signal-on quantum dot photoelectrochemical (QD-PEC) biosensor for ultrasensitive detection of cardiac troponin I (cTnI), based on a MoS/NbS nanoheterojunction constructed via atomic layer deposition (ALD). The petal-like MoS/NbS, uniformly grown with atomic precision, offers a high surface area and efficient charge transport, serving as an ideal scaffold for dense CdTe QD loading.

Long-term no-tillage practice with re-used plastic film mulching exacerbates bacterial ecological risks in dryland-based on plastisphere.

Re-used plastic-film (RUPF) mulching has emerged as an innovative and practical agricultural practice to reduce plastic use and the buildup of plastic waste in drylands in China. However, alterations in bacterial community colonisation mechanisms and ecological processes due to plastic contamination in RUPF mulching systems remain unclear. In this study, we examined the distribution and assembly of soil bacterial communities under two RUPF exposure scenarios, weathered plastic film plastisphere (WPS) and soil-buried plastic film plastisphere (BPS), and compared them to no-mulching ambient soil (CK).

Research on prostate brachytherapy puncture control strategy based on adaptive PID control with FBG sensors.

This paper enhances prostate brachytherapy robot accuracy by developing a needle deflection prediction model and a controlled puncturing strategy, addressing current challenges and trends. The study addresses the challenges in needle deflection prediction by proposing a correction force-based prediction model. The puncture control strategy comprises two phases: preoperative needle trajectory planning and intraoperative approach adjustment, both relying on corrective force.

Data-driven global ocean modeling for seasonal to decadal prediction.

Accurate modeling of ocean dynamics is crucial for enhancing our understanding of complex ocean circulation processes, predicting climate variability, and tackling challenges posed by climate change. Although great efforts have been made to improve traditional numerical models, predicting global ocean variability over multiyear scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), a data-driven three-dimensional ocean model for seasonal to decadal prediction of global ocean dynamics.

All-in-one medical image-to-image translation.

The growing availability of public multi-domain medical image datasets enables training omnipotent image-to-image (I2I) translation models. However, integrating diverse protocols poses challenges in domain encoding and scalability. Therefore, we propose the "every domain all at once" I2I (EVA-I2I) translation model using DICOM-tag-informed contrastive language-image pre-training (DCLIP).

Philippine archipelago and South China Sea monsoon plus ocean cooling buffer Northwestern Pacific super typhoons.

In dramatic contrast to the continuous intensification of strong Atlantic hurricanes moving into the Gulf of Mexico, super typhoons (SuperTYs) originating from the Northwest Pacific significantly weaken after entering the South China Sea (SCS). Using an idealized cyclone intensity model, we show that this striking discrepancy results from the SCS's buffering effects, including persistent topographic blocking by the Philippine archipelago and seasonally shifting atmospheric-oceanic constraints. The archipelago accounts for ~54% of typhoon weakening throughout the season, while environmental factors explain the remaining ~46% with the significant seasonal dependence on strong SCS monsoon-induced vertical wind shear in summer (~30%) and ocean cooling in autumn (~37%).

3D long time spatiotemporal convolution for complex transfer sequence prediction.

Spatiotemporal sequences prediction(SSP) aims to predict the future situation in a period of time based on the spatiotemporal sequences data(SSD) of historical observations. In recent years, deep learning-based models have received more attention and research in SSP tasks. However, two challenges still exist in the existing methods: 1) Most of the existing spatio-temporal prediction tasks focus on extracting temporal information using recurrent neural networks and using convolution networks to extract spatial information, but ignore the fact that the forgetting of historical information still exists as the input sequence length increases.

Multi-modal MRI cascaded incremental reconstruction with coarse-to-fine spatial registration.

Magnetic resonance imaging (MRI) typically utilizes multiple contrasts to assess different tissue features, but prolonged scanning increases the risk of motion artifacts. Compressive sensing MRI (CS-MRI) employs computational reconstruction algorithm to accelerate imaging. Full-sampled auxiliary MR images can effectively assist in the reconstruction of under-sampled target MR images.

Dynamic Confinement and High-Entropy Catalytic Synergy Engineering in Hollow Nano-Metal-Organic Frameworks.

The systematic regulation of the pore size and chemical environment of nano-metal-organic skeletons (n-MOFs) has been challenged, making it difficult to study their structure-property relationships in depth. In this study, a universal dynamic template strategy is proposed and successfully achieves the controllable construction of various hollow n-MOFs (including ZIF-67, Co-BTC, etc.).

Antioxidant action and potential neuroprotection of polyphenolics extracted from residue.

Introduction: Polyphenols, recognized as nutritional supplements, have emerged as promising therapeutic agents for various diseases, particularly brain disorders. However, due to the limitation of the extraction method, residues (AR) retain substantial bound phenolics with unexplored neuronal antioxidant activity.

Methods: In this study, free, esterified, and bound phenolic compounds were sequentially extracted from AR.

Precipitation effects on autumn PM composition in a Yangtze River Delta coastal city: Temporal variation, source apportionment and health risk.

Based on chemical composition data from the Yangtze River Delta in November 2022, this study conducted a comparative analysis of precipitation impacts on PM chemical composition variation, sources, and light extinction using methods such as PMF (Positive Matrix Factorization), while also assessing local health risks. During the observation period, the mean PM concentration was relatively low (25.1 μg/m), primarily composed of SNA (SO, NO, and NH) (67 %) and OC (18.

Adaptive set-level metric for few-Shot image classification.

Few-shot image classification aims to learn a classifier from limited labeled data. Though the existing methods have achieved significant improvement, they are still challenging to accurately differentiate image categories between the confused support and query samples. We observed that the objects belong to same category may exhibit significant image-level appearance difference between the support and query, while the objects belong to different categories may have the similar appearance on them.