Elastomer-assisted graphene transfer technique for enhanced resonance characteristics in chemisorption-based graphene resonant mass sensors.

A process technology was developed for transferring strained graphene sheets using an elastomer nanosheet to enhance the resonance characteristics of a graphene resonator. The strain-induced graphene resonator demonstrated a 1.80-fold improvement in the product of resonant frequency and -factor compared to the unstrained resonator.

A Zn-doped SbTe flexible thin film with decoupled Seebeck coefficient and electrical conductivity band engineering.

SbTe-based flexible thin films can be utilized in the fabrication of self-powered wearable devices due to their huge potential in thermoelectric performance. Although doping can significantly enhance the power factor value, the process of identifying suitable dopants is typically accompanied by numerous repeating experiments. Herein, we introduce Zn doping into thermally diffused p-type SbTe flexible thin films with a candidate dopant validated using the first-principles calculations.

Multicellular PID control for robust regulation of biological processes.

This article presents the first implementation of a proportional-integral-derivative (PID) biomolecular controller within a consortium of different cell populations, aimed at robust regulation of biological processes. By leveraging the modularity and cooperative dynamics of multiple engineered cell populations, we develop a comprehensive analysis of the performance and robustness of P, PD, PI and PID control architectures. Our theoretical findings, validated through experiments using the BSim agent-based simulation platform for bacterial populations, demonstrate the robustness and effectiveness of our multicellular PID control strategy.

In-situ Polymerization Induced Seed-Root Anchoring Structure for Enhancing Stability and Efficiency in Perovskite Solar Modules.

The escape of organic cations over time from defective perovskite interface leads to non-stoichiometric terminals, significantly affecting the stability of perovskite solar cells (PSCs). How to stabilize the interface composition under environmental stress remains a grand challenge. To address this issue, we utilize thiol-functionalized particles as a "seed" and conduct in situ polymerization of 2,2,3,4,4,4-hexafluorobutyl methacrylate (HFMA) as a "root" at the bottom of the perovskite layer.

Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT.

Background: This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images.

Methods: A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied to fuse PET and CT images.

Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework.

Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure.

An ultrawideband monopulse feed with slant polarization for tracking radar systems.

An ultrawideband slant-polarized monopulse feed is designed and fabricated for microwave applications. The proposed configuration features four end-launched diagonal horns allowing for the production of sum and difference channels in two principal planes. The key advantage of this proposed monopulse antenna over traditional monopulse feeds is its ability to combine the benefits of ultrawideband performance with slant polarization while maintaining acceptable side lobe level.

Superpixel guided spectral-spatial feature extraction and weighted feature fusion for hyperspectral image classification with limited training samples.

Deep learning is a double-edged sword. The powerful feature learning ability of deep models can effectively improve classification accuracy. Still, when the training samples for each class are limited, it will not only face the problem of overfitting but also significantly affect the classification result.

Abstract visual reasoning based on algebraic methods.

Extracting high-order abstract patterns from complex high-dimensional data forms the foundation of human cognitive abilities. Abstract visual reasoning involves identifying abstract patterns embedded within composite images, considered a core competency of machine intelligence. Traditional neuro-symbolic methods often infer unknown objects through data fitting, without fully exploring the abstract patterns within composite images and the sequential sensitivity of visual sequences.

Soliton solutions of the (2 + 1)-dimensional Jaulent-Miodek evolution equation via effective analytical techniques.

In this study, we investigate the [Formula: see text]-D Jaulent-Miodek (JM) equation, which is significant due to its energy-based Schrödinger potential and applications in fields such as optics, soliton theory, signal processing, geophysics, fluid dynamics, and plasma physics. Given its broad utility, a rigorous mathematical analysis of the JM equation is essential. The primary objective of this work is to derive exact soliton solutions using the Modified Sub-Equation (MSE) and Modified Auxiliary Equation (MAE) techniques.

Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting.

Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets.

Dynamic omniphobic surfaces enable the stable dropwise condensation of completely wetting refrigerants.

Condensation is a vital process integral to numerous industrial applications. Enhancing condensation efficiency through dropwise condensation on hydrophobic surfaces is well-documented. However, no surfaces have been able to repel liquids with extremely low surface tension, such as fluorinated solvents, during condensation, as they nucleate and completely wet even the most hydrophobic interfaces.

Artificial Intelligence and Identification of the Deceased: a Narrative Review With Implications in Forensic Science.

Identification of the dead is of utmost importance in mass disasters, war crimes, and forensic examinations. The biological profile, established by a forensic anthropologist is one the necessary steps involved in the identification of the dead. Several parameters can be estimated such as sex, age, stature, biogeographical affinity, and DNA profile of the unknown person.

Interleaflet Translocation of Second-Harmonic-Generation-Active Dye Molecules in Phospholipid Bilayers with Transmembrane Pores.

Second harmonic generation (SHG) measurements using SHG-active dye molecules have recently attracted attention as a method to detect the formation of pores in phospholipid bilayers. The bilayers, in which the dye molecules are embedded in the outer leaflet, exhibit a noncentrosymmetric structure, generating SHG signals. However, when pores form, these dye molecules translocate through the pores into the inner leaflet, leading to a more centrosymmetric structure and the subsequent loss of the SHG signals.

Multimodal sleep signal tensor decomposition and hidden Markov Modeling for temazepam-induced anomalies across age groups.

Background: Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.

New Method: We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.

Occurrence and transport of per- and polyfluoroalkyl substances (PFAS) in the leachate of a municipal solid waste landfill in Tehran, Iran (a Middle-eastern megacity).

Per- and polyfluoroalkyl substances (PFAS) are present in a variety of products that are disposed in landfills as waste and end up in landfill leachate which cause severe problems. The primary aim of this study was to detect PFAS in generated leachate in different sections of a process and disposal complex (called Aradkuh) located in Tehran, Iran. Due to techno economic limitations of measuring PFAS in Iran and easiness of measuring physicochemical parameters to determine PFAS concentration as well as better understanding of the mechanisms of these substances releases from landfills, this research aimed to evaluate the potential relationship between these parameters in landfill leachate.

Selective activation of mesoscale functional circuits via multichannel infrared stimulation of cortical columns in ultra-high-field 7T MRI.

To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable.

bneR: A collaborative workflow for air pollution exposure modeling and uncertainty characterization using the Bayesian Nonparametric Ensemble.

Background: Air pollution is a major public health threat globally. Health studies, regulatory actions, and policy evaluations typically rely on air pollutant concentrations from single exposure models, assuming accurate estimations and ignoring related uncertainty. We developed a modeling framework, bneR, to apply the Bayesian Nonparametric Ensemble (BNE) prediction model that combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty.

Enhancing the ferroelectric performance of HfZrOfilms by optimizing the incorporation of Al dopant.

HfO-based ferroelectric (FE) thin films have gained considerable interest for memory applications due to their excellent properties. However, HfO₂-based FE films face significant reliability challenges, especially the wake-up and fatigue effects, which hinder their practical application. In this work, we fabricated 13.

Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.

Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importance in the clinical diagnosis and therapy. Currently, these duties are extensively carried out by manual examination utilizing the human visual system.

Numerical analysis of sodium diffusion in aluminum electrolysis cathode carbon blocks based on a microstructure multi-factor corrected model.

Current researches on sodium penetration in electrolytic aluminum cathode carbon blocks primarily measure cathode expansion curves, showing mostly macroscopic characteristics. However, the microscopic structure is often underexplored. As a porous medium, the diffusion performance of cathode carbon blocks is closely tied to their internal pore structure.