Browse Articles

Short, Enantioselective Total Synthesis of (+)-Ineleganolide

Investigation of ion acoustic double layers in magnetized plasma with two nonextensive electron species

Modulation of the Electronic and Vibrational Landscape in Lead Organic Chalcogenides

Prevalence and associated factors of overweight and obesity among people living with HIV on antiretroviral therapy in a national hospital in Benin

Adaptive Macromolecular Surfactancy: Dynamic Bottlebrush Polymers Activated by Triggered Interfacial Hydrolysis

A neural network-based automatic semi-variogram modeling approach for geomagnetic map construction in multi-source indoor and outdoor navigation

Abstract High-precision geomagnetic maps are essential for geomagnetic-assisted navigation, yet their construction is constrained by kriging interpolation’s reliance on accurately modeled semi-variogram. Conventional approaches depend heavily on geological expertise, introducing subjectivity and limiting both mapping accuracy and navigation performance. Here, we present geomagnetic map via auto-semi-variogram kriging(GMAS-K), a framework that integrates geomagnetic map via auto-semi-variogram convolutional neural network(GMAS-CNN) to automatically infer semi-variogram parameters. GMAS-CNN adopts an encoder–decoder architecture: the encoder compresses and fuses multi-scale features of geomagnetic samples to enrich semi-variance representations, while the decoder reconstructs latent feature spaces to estimate semi-variogram parameters. To further enhance cross-scale consistency, we introduce a multiple convolutional block attention module (M-CBAM). Experiments show that GMAS-K surpasses ordinary kriging, producing smoother and more accurate geomagnetic maps while streamlining the mapping workflow. These results highlight the promise of coupling deep learning with geostatistical interpolation to advance geomagnetic mapping and improve navigation accuracy.

Accurately Predicting Solubility Curves via a Thermodynamic Cycle, Machine Learning, and Solvent Ensembles

Statistical optimization of crumb rubber modified bitumen performance through material blending analysis

Glucose-Powered Ultrasmall Chemotactic Nanorobots for Retinal Degeneration Treatment

Effect of fines content on liquefaction resistance of soil using laboratory test and SPT

Huge genetic study reveals hidden links between psychiatric conditions

Deep learning-enabled cherry price forecasting and real-time system deployment across multi-market supply chains in India

Characterization of myogenesis in European sea bass (Dicentrarchus labrax) using primary white muscle cell cultures

Impact of conditional cash transfers under the Janani Suraksha Yojana on neonatal outcomes in India’s EAG states

Dynamic monitoring of ecological security patterns in arid zone oases: a remote sensing-based ecological index evolution analysis

A privacy preserving intrusion detection framework for IIoT in 6G networks using homomorphic encryption and graph neural networks

A giant catalogue of microscopic species across Denmark

Effects of different powers of repeated low-level red light on form-deprivation myopia inhibition in guinea pigs

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Experimental study on heat transfer characteristics of plate evaporator under heaving, pitching, and rolling conditions

Abstract To investigate the variation characteristics of heat transfer performance in plate evaporators within the ocean thermal energy conversion (OTEC) systems of underwater unmanned vehicles (UUVs) under marine motion conditions, a flow boiling experimental system integrated with a six-degree-of-freedom motion platform was designed and established. This study examined the effects of sloshing modes (heaving, pitching, and rolling), mass velocity, sloshing amplitude, sloshing frequency, and sloshing intensity on the heat transfer characteristics of the plate evaporator. The results indicate the following: (1) Heaving motion exerts a significant enhancement effect on the heat transfer performance of the plate evaporator. Under conditions of a heaving amplitude of 100 mm and a frequency of 0.6 Hz, the convective heat transfer coefficient of R134a increases by up to 61.8%; (2) Pitching motion exhibits a noticeable enhancement effect on heat transfer performance at small sloshing amplitudes (2.5° amplitude), with the convective heat transfer coefficient of R134a increasing by up to 34.75% at a frequency of 0.2 Hz; (3) Rolling motion demonstrates a significant weakening effect on heat transfer performance, with the convective heat transfer coefficient of R134a decreasing by up to 31.8% under conditions of a rolling amplitude of 7.5° and a sloshing frequency of 1 Hz. It should be noted that the inlet working fluid of the plate evaporator in this experiment is saturated, and the vapor quality of the outlet working fluid is 0.3–0.4. Furthermore, a heat transfer correlation applicable to plate evaporators under heaving, pitching, and rolling conditions was developed in this study. Validation results demonstrate that the proposed correlation exhibits excellent predictive performance, with prediction deviations within ± 15%. It should be noted that the applicable range of the heat transfer correlation established in this study is for pitching and rolling amplitudes of 2.5° to 7.5°, heaving amplitudes of 50 to 100 mm, frequencies of 0 to 1 Hz, mass fluxes of 125 to 225 kg·m-2·s-1, and the working fluid being R134a.