Browse Articles

Quantized field with excitations of spacetime

Abstract We study a quantized field that can excite its underlying spacetime and has the properties of a bosonic field. A particle in this field is a harmonic oscillator in time, also known as a proper time oscillator, which is an excitation of spacetime. Time in this oscillator flows only forward but with varying rates. In separate analyses, by assuming the same proper time oscillator as a classical object that can remain stationary in space, we show that the spacetime outside is a Schwarzschild field. A classical proper time oscillator mimics the effects of a point mass in general relativity. As shown, a proper time oscillator has the properties of a quantum particle and can act as a gravitational source. Based on these results, if a real particle is an excitation of the corresponding quantum field and its underlying spacetime, the proper time oscillation will allow a real particle to interact directly with spacetime, generating a gravitational field.

Investigating thermal dynamics in cylindrical Li-ion batteries across varied temperatures based on electrochemical principles

Abstract Thermal dynamics in cylindrical Li-ion batteries, governed by electrochemical heat generation, are critical to performance and safety in high-power applications such as electric vehicles and grid storage. Building on our previous work, which introduced and validated both single-layer and multi-layer models, this study focuses exclusively on experimentally validating the multi-layer formulation under a broader range of ambient temperatures. The proposed multi-layer model captures temperature evolution across all internal components, including the electrolyte, electrodes, current collectors, and casing, accurately resolving spatial heat accumulation. Experimental validation is conducted across four temperatures (21 $$^{\circ }$$ C, 0 $$^{\circ }$$ C, 40 $$^{\circ }$$ C, and − 10 $$^{\circ }$$ C), demonstrating strong agreement and highlighting the model’s robustness. These results offer actionable insights into internal thermal behavior and may support the design of advanced thermal management strategies, contributing to the development of safer and more efficient Li-ion batteries for next-generation energy storage systems.

A prediction model based on cfDNA concentration and cfDNA methylation biomarkers for lung cancer detection

Cross-language priming effects in bilingual novel metaphor processing

Abstract This event-related potential study investigates whether semantically related words facilitate the processing of English novel metaphors and how this effect varies across within- and between-language contexts. Spanish-English/English-Spanish bilinguals performed a meaningfulness decision task in response to sentences in English, which included novel metaphors, novel similes, literal, and anomalous sentences. Prime words that were either related to the overall meaning of the target sentences or unrelated were presented either in English (the within-language condition) or Spanish (the between-language condition) before the sentence onset. In the N400 time window, unrelated primes evoked larger amplitudes than related primes in the within-language condition, indicating that conceptual compatibility reduces cognitive effort during early semantic processing. This effect was absent in the between-language condition, likely due to weaker associative links or additional cognitive demands from language switching. In the Late Positive Complex window, related primes imposed higher processing costs, reflecting their incorporation into sentence interpretation, while unrelated primes were dismissed earlier, reducing later-stage cognitive effort. These findings highlight the role of linguistic context in semantic processing, showing that within-language priming enhances efficiency, whereas between-language priming is less effective, particularly for complex novel metaphorical meanings.

Study on the mechanisms associating community outdoor public spaces with elderly behavior

Enhancing adipogenesis in Wharton’s jelly multipotent mesenchymal stromal cells through lipidomic insights and fatty acid supplementation

Abstract Wharton’s Jelly multipotent mesenchymal stromal cells (WJ-MSCs) hold potential for regenerative medicine, particularly in soft tissue engineering. However, their adipogenic differentiation capacity is inferior to adipose tissue-derived MSCs (AT-MSCs). This study aimed to optimize adipogenic differentiation for WJ-MSCs by leveraging insights from the comparative analysis of WJ- and AT-MSC lipidomic profiles. Lipidomic profiles of non-induced cells were compared, and adipogenic differentiation was induced with and without exogenous oleic or linoleic acid supplementation. Differentiation efficiency was determined based on lipid droplet formation, triglyceride (TG) content quantification, and the expression of adipogenic markers. Significant differences in TG composition were observed, with WJ-MSCs showing higher levels of 52-carbon TGs and AT-MSCs having more 56-carbon species. Both cell types had similar fatty acid (FA) profiles, with 18-carbon FAs making up over 50%. Adding oleic acid to the differentiation medium significantly enhanced lipid droplet formation and upregulated adipogenic markers in WJ-MSCs, aligning their adipogenic capacity more closely with AT-MSCs. In contrast, linoleic acid showed no significant benefits. The study underscores the critical role of the initial lipidomic profile in the adipogenic differentiation of MSCs. Supplementation with oleic acid represents a promising approach for improving adipogenic differentiation of WJ-MSCs and their utility in soft tissue engineering.

High-frequency and high-amplitude sounds enhance bird deterrence

Adaptive information-constrained mapping for feature compression in edge AI and federated systems

Abstract This article explores the problem of efficient feature compression in distributed intelligent systems with limited resources, particularly within the context of Edge AI and Federated Learning. The relevance of this study is driven by the growing need to reduce communication overhead under conditions of unstable Quality of Service, limited bandwidth, and high heterogeneity of input data. The scientific novelty lies in the development of a consistent entropy-regularised compression model that combines variational latent mapping, non-negativity-constrained projection design, and stochastic-Boolean transformation of the feature space. A generalised compression quality functional is proposed, integrating the directed Kullback–Leibler divergence, an entropic regularisation component, and a guarantee of preserving the semantic relevance of the compressed representation. Efficient projection-gradient optimisation algorithms have been developed, suitable for implementation in constrained computational environments. The practical effectiveness of the approach has been confirmed through experiments on the HAR and PAMAP2 datasets: a 6–eightfold reduction in entropy load was achieved while maintaining classification accuracy above 94% and a high level of semantic fidelity in the reconstructed data. The models were deployed on low-power devices (Jetson Nano, Raspberry Pi 4), where they demonstrated robustness to noise and loss, as well as superiority over current SOTA solutions (FedEntropy, EDS-FL, SER) in terms of compression efficiency, adaptability to heterogeneous distributions, and stability under unstable transmission conditions.

Hsa_circ_0005571 promotes the proliferation and invasion of colorectal cancer cells

Performance evaluation of heat sinks with calcium nitrate tetrahydrate phase change material for electronic cooling

Spatiotemporal patterns and drivers of coupling coordination between digital technological innovation and economic resilience in the Yangtze river economic belt

Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems

Encapsulation of diflubenzuron in PEG-400 nanoparticles and evaluation pesticide activity against Helicoverpa armigera (Lepidoptera: Noctuidae)

Grain boundary motions of low temperature and low pressure copper to copper direct bonding by electroplating ultra-fine-grain (UFG) Cu

FoT: an efficient transformer framework for real-time small object detection in football videos

The effect of WWTP products amendments on Phaseolus vulgaris rhizosphere and its ability to inactivate clarithromycin

A novel approximation of underwater robotic vehicle controller exploiting multi-point matching

Abstract This proposed work is presenting the approximation of higher-order (HO) underwater robotic vehicle (URV) controller with the help of multi-point matching technique by incorporating greywolf optimization algorithm (GWOA). The performance of URV system is affected by external and internal dynamics. The proper momentum of URV system is achieved by designing a controller. The URV can be effectively operated by control action of controller. The URV controller is approximated to comparatively lower-order (LO) to propose an efficient, effective and economical controller for HOURV system. The approximation is accomplished with the help of expansion parameters of HOURV controller and its desired LOURV controller. The errors between these expansion parameters of HOURV controller and its desired LOURV controller are minimized using multi-point matching. The multi-point matching is depicted in the form of objective function (OF). The constructed OF is minimized by exploiting GWOA by fulfilling the steady-state matching condition and Hurwitz stability criterion, as constraints. The effectiveness of proposed approach of multi-point matching is verified by comparing the proposed LOURV model with LOURV models obtained with the help of other approximation approaches. The applicability of proposed LOURV controller is evaluated and validated by analyzing responses and tabulated data obtained in the results. Additionally, the statistical data of performance error values (PEVs) are provided in tabulated form along with its bar plot.

Near viewing behaviors predict educational system in a machine learning model

Graphene-cobalt hexacyanoferrate modified sensor doped with molecularly imprinted polymer for selective potentiometric determination of bupropion

Abstract This study presents the first application of a graphene/cobalt hexacyanoferrate composite as an ion-to-electron transducer interlayer for the selective electrochemical determination of bupropion. The composite was prepared by homogeneously dispersing graphene with tween 80 and then decorating it with cobalt-hexacyanoferrate nanoparticles. Modifying glassy carbon electrodes with this interlayer improved and stabilized the measured potential by preventing the formation of an aqueous layer beneath the sensing membrane and enhancing charge transfer. The formation of the aqueous layer is a phenomenon commonly associated with solid-contact ion-selective electrodes. These electrodes encounter challenges regarding selectivity affecting their analytical performance in the presence of ions with similar charges and proper lipophilicity. Molecularly imprinted polymer (MIP) approach was employed to resolve the selectivity problem of analyzing bupropion in presence of naltrexone for obesity management. Scanning electron microscope and Fourier-transform infrared were utilized to characterize the fabricated composite and the MIP. Two cationic exchangers were separately integrated with the precipitated MIP to produce sensors for selective determination of bupropion in the dosage form and in spiked human plasma. The sensors exhibited Nernstian slope of 54.66 mV/decade and 55.89 mV/decade with detection limits as low as 2.51 × 10− 7 M and 2.0 × 10− 7 M. Moreover, three different metric ways verified sufficient sustainability of the proposed potentiometric method.

Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals