Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels.

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions.

Enhancing Open-Set Domain Adaptation through Optimal Transport and Adversarial Learning.

Open-Set Domain Adaptation (OSDA) is designed to facilitate the transfer of knowledge from a source domain to a target domain, where the class space of the source is a subset of the target. The primary challenge in OSDA is the identification of shared samples in the target domain to achieve domain alignment while effectively segregating private samples in the target domain. In attempts to address this challenge, numerous existing methods leverage weighted classifiers to mitigate the negative transfer issue induced by private classes in the target domain and recognize all these samples as a whole unknown class.

Leisure and cultural identity: an empirical study based on root-seeking summer camp for ethnic Chinese new generation.

This study examines the role of root-seeking summer camps in enhancing Chinese cultural identity among the new ethnic Chinese generation (ECNG) amidst the challenges of globalization. Semi-structured interviews with thirty campers revealed that ECNG often lack clear ethnic consciousness, experience conflict in community relationships, and have limited opportunities for traditional cultural experiences. The study found that summer camps exert both intrinsic and extrinsic influences on campers.

Analysis of different standards for life support technology in manned submersible.

Life support technology is one of the most critical technologies in manned submersibles, acting for the pilots of the submersible and thus directly affecting its underwater safety. Due to its importance, many countries and international organizations have proposed standards and specifications for life support technology. This paper presents an overview of the life support technology of manned submersibles, comparing the standards proposed for it.

A Ru(II) complex-based COX-2 targeting type I photosensitizer evokes ferroptosis and apoptosis.

Photodynamic therapy (PDT) often faces challenges such as oxygen dependence and limited tumour specificity. We report a tumour-targeting photosensitizer (PS), RuCXB, which enhances uptake by cancer cells by targeting overexpressed cyclooxygenase-2 enzyme in tumours. RuCXB also reduces oxygen dependence a type I PDT mechanism and achieves a strong therapeutic effect through the synergistic induction of ferroptosis and apoptosis.

First Principles Study of Electronic and Optical Properties of Al-P Co-Doped ZnO in the Presence of Zn Vacancies.

The BP neural network optimized by the Adam algorithm was used to predict the defect formation energy of Al-P co-doped ZnO systems with different concentrations of P replacing O under the presence of different concentrations of V. It was found that the easily formed AlPo-1V, AlP-2V, and Al2P-1V systems. The first principles of density function were used to study the geometric, electronic, and optical properties of each system.

Effect of accelerated aging on the thermo-mechanical behavior and biotribological properties of an irradiation cross-linked GO/UHMWPE nanocomposite after VE diffusion.

In this work, the influence of accelerated aging on the thermo-mechanical behavior and biotribological properties of an irradiation cross-linked GO/UHMWPE nanocomposite after VE diffusion was investigated, including through differential scanning calorimetry (DSC), gel content, FT-IR characterization, oxidation index, ball indentation hardness, and especially the biotribological properties. The results show that accelerated aging increased the melting point and crystallinity of the nanocomposite, but resulted in a decrease in thermal stability and gel content. The oxidation index increased by 60.

Co-firing characteristic prediction of solid waste and coal for supercritical CO power cycle based on CFD simulation and machine learning algorithm.

The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO (S-CO) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, and reduce pollutant emission. This study focuses on the co-firing characteristics of CSW and coal under S-CO power cycle, and simulations are conducted by employing Multiphase Particle-in-cell (MP-PIC) method integrated with the comprehensive chemical reaction models in a 300 MW S-CO CFB boiler. Effects of operating parameters including fuel mixture proportion and first stage stoichiometry on the gas emission characteristics are further analyzed.

Understanding the Processing Quality Problem for Cutting Ceramic Materials Using the Thermal-Controlled Fracture Method Induced by a Single-Surface Heat Source.

The thermal-controlled fracture method has been increasingly focused upon in the high-quality cutting of advanced ceramic materials due to its excellent characteristics. The successful application of this method in cutting ceramics mainly depends on the volumetric heating effect. However, most ceramics are treated using the surface heating mode.

Prediction of Two-Dimensional Janus Transition-Metal Chalcogenides: Robust Ferromagnetic Semiconductor with High Curie Temperature.

Two-dimensional (2D) ferromagnetic semiconductors (FM SCs) provide an ideal platform for the development of quantum information technology in nanoscale devices. However, many developed 2D FM materials present a very low Curie temperature (T), greatly limiting their application in spintronic devices. In this work, we predict two stable 2D transition metal chalcogenides, VSeX (X = S, Te) monolayers, by using first-principles calculations.

Machine learning-aided understanding of the structure-activity relationship: a case study of MoS supported metal-nonmetal pairs for the hydrogen evolution reaction.

Understanding the structure-performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge. Using MoS supported metal-nonmetal atom pairs (XTM@MoS, TM = Sc-Ni, and X = B, C, N, O, P, Se, Te, and S) for the hydrogen evolution reaction (HER) as an example, we successfully uncovered the structure-activity relationship with the help of density functional theory (DFT) calculations and integrated machine learning (ML) methods. An ML model based on random forest regression was used to predict the activity, and the trained model exhibited excellent performance with minimal error.

Single-atom catalysts for electrocatalytic nitrate reduction into ammonia.

Ammonia (NH) is a versatile and important compound with a wide range of uses, which is currently produced through the demanding Haber-Bosch process. Electrocatalytic nitrate reduction into ammonia (NRA) has recently emerged as a sustainable approach for NHsynthesis under ambient conditions. However, the NRA catalysis is a complex multistep electrochemical process with competitive hydrogen evolution reaction that usually results in poor selectivity and low yield rate for NHsynthesis.

Investigation of the effects of irradiation and aging on the tribological behavior of ultra-high molecular weight polyethylene/graphene oxide composites under water lubrication.

Ultra-high molecular weight polyethylene/graphene oxide (PE-UHMW/GO) composites have demonstrated potential in artificial joint applications. The tribological behavior of irradiated PE-UHMW/GO composites under water lubrication remained unclear, which limited their application range. In this study, the PE-UHMW/GO composites were gamma irradiated at 100 KGy in a vacuum and subsequently aged at 80 °C for 21 days in air.

A Load-Adaptive Driving Method for a Quasi-Continuous-Wave Laser Diode.

A quasi-continuous-wave (QCW) laser diode (LD) driver is commonly used to drive diode bars and stacks designed specifically for QCW operations in solid-state lasers. Such drivers are optimized to deliver peak current and voltage pulses to LDs while maintaining low average power levels. As a result, they are widely used in laser processing devices and laser instruments.

Composite polymer electrolyte based on poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) for solid-state batteries.

Using solid-state electrolytes (SSEs) with excellent thermal and electrical stability to replace liquid electrolytes, and assembling solid-state lithium-ion batteries (SSLIBs) is considered the best solution to these safety issues. However, it is difficult for a single electrolyte to have the characteristics of high ionic conductivity, low interface resistance, and high stability of the counter electrode at the same time. In this work, the composite polymer electrolyte membrane (CPE) of inorganic LiAlTi(PO) (LATP) and organic poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) polymer was successfully prepared by traditional casting method.

Coexistence of Ferromagnetism and Ferroelectricity in Cu-Intercalated Bilayer CrI.

Design of two-dimensional (2D) multiferroic materials with two or more ferroic orders in one structure is highly desired in view of the development of next-generation electronic devices. Unfortunately, experimental or theoretical discovery of 2D intrinsic multiferroic materials is rare. Using first-principles calculation methods, we report the realization of multiferroics that couple ferromagnetism and ferroelectricity by intercalating Cu atoms in bilayer CrI, Cu@bi-CrI ( = 0.

High-throughput and simultaneous inertial separation of tumor cells and clusters from malignant effusions using spiral-contraction-expansion channels.

Tumor cell clusters are regarded as critical factors in cancer pathophysiology, and increasing evidence of their higher treatment resistance and metastasis compared to single tumor cells has been obtained. However, existing cell separation methods that are designed for single tumor cells cannot be used to simultaneously purify tumor cell clusters. To address this problem, we demonstrated a microfluidic approach for the high-throughput, continuous-flow ternary separation of single tumor cells, tumor cell clusters, and WBCs from clinical pleural or abdominal effusions by coupling slanted spiral channels and periodic contraction-expansion arrays.

A Low-Noise Amplifier for Submarine Electric Field Signal Based on Chopping Amplification Technology.

In the exploration of ocean resources, the submarine electric field signal plays a crucial role through marine electromagnetic methods. However, due to the field signal's low-frequency and weak characteristics, it often encounters interference from the instrument's own 1/f noise during its acquisition. To address this issue, we developed a low-noise amplifier for the submarine electric field signal based on chopping amplification technology.

Optimization design of brushless DC motor based on improved JAYA algorithm.

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation.

Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?

This article investigates whether higher classification accuracy can always be achieved by utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep-Visual-Words (BoDVW) classification model, as opposed to directly using the new classification layer of the pre-trained model for classification. Considering the multiple factors related to the feature extractor -such as model architecture, fine-tuning strategy, number of training samples, feature extraction method, and feature encoding method-we investigate these factors through experiments and then provide detailed answers to the question. In our experiments, we use five feature encoding methods: hard-voting, soft-voting, locally constrained linear coding, super vector coding, and fisher vector (FV).

Tribological behavior and in vitro biocompatibility of powder metallurgical Ti-15Mo/HA composite for bone repair.

Ti-15Mo/HA composite was prepared by powder metallurgy, and the influence of Hydroxyapatite (HA) on the microstructure, tribological behavior and in vitro biocompatibility was studied by comparison with TC4. The results show that the Ti-15Mo/HA composite consists of increased α-Ti, decreased β-Ti and a variety of ceramic phases (CaTiO, Ca(PO), CaO, etc.) with the increase of HA content.

Data-based bipartite formation control for multi-agent systems with communication constraints.

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions.

Generation, division and training: A promising method for source-free unsupervised domain adaptation.

Conventional unsupervised domain adaptation (UDA) methods often presuppose the existence of labeled source domain samples while adapting the source model to the target domain. Nevertheless, this premise is not always tenable in the context of source-free UDA (SFUDA) attributed to data privacy considerations. Some existing methods address this challenging SFUDA problem by self-supervised learning.