A neural machine translation method based on split graph convolutional self-attention encoding.

With the continuous advancement of deep learning technologies, neural machine translation (NMT) has emerged as a powerful tool for enhancing communication efficiency among the members of cross-language collaborative teams. Among the various available approaches, leveraging syntactic dependency relations to achieve enhanced translation performance has become a pivotal research direction. However, current studies often lack in-depth considerations of non-Euclidean spaces when exploring interword correlations and fail to effectively address the model complexity arising from dependency relation encoding.

An accurate irrigation volume prediction method based on an optimized LSTM model.

Precise prediction of irrigation volumes is crucial in modern agriculture. This study proposes an optimized long short-term memory (LSTM) model-based irrigation prediction method that combines bidirectional LSTM networks. The model provides farmers with more precise irrigation management decisions, facilitating optimal utilization of water resources and effective crop production management.

LCFFNet: A Lightweight Cross-scale Feature Fusion Network for human pose estimation.

Human pose estimation is one of the most critical and challenging problems in computer vision. It is applied in many computer vision fields and has important research significance. However, it is still a difficult challenge to strike a balance between the number of parameters and computing load of the model and the accuracy of human pose estimation.

Effect of incomplete line defect size on energy localization and harvesting in phononic crystals.

The high electrical output performance of the phononic crystal (PnC)-based piezoelectric energy harvesting (PEH) system is of great research value in self-powered applications. This work presents the effect of incomplete line defect size on elastic wave energy localization and harvesting. The results show that for a given 7 × 5 supercell when the incomplete line defect reaches the second to sixth layer, the energy localization and harvesting performance show a changing trend of first increasing and then decreasing; when the incomplete line defect reaches the 4th, 5th, 3rd, 2nd, and 6th layers of the supercell, respectively, the performance of PEH systems shows a trend from large to small.

Identification of copper and lead pollution elements based on spectra of corn leaves in different leaf layers.

With the development of industrialization, environmental heavy metal pollution has become increasingly serious, and the growth of crops has been seriously affected by heavy metal pollution in the soil environment. Therefore, it is necessary to establish methods for distinguishing and monitoring heavy metal pollution. The application of hyperspectral remote sensing in heavy metal pollution monitoring demonstrates the great potential of using crop leaf spectra to accurately distinguish heavy metal pollution elements.

Improved performance in 0D/2D mixed dimensional homojunction MoSphotodetectors by enhancing light absorption.

Molybdenum disulfide (MoS) possesses excellent potential for applications in the field of optoelectronic detection. However, the atomic-level thickness of the monolayer MoSleads to weak light absorption and a restricted absorption spectrum. The performance of monolayer MoSdevices has reached a bottleneck.

The role of Internet of Things (IoT) technology in modern cultivation for the implementation of greenhouses.

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones.

Design of a hazard prediction system with intelligent multimodal fusion based on artificial intelligence & internet of things technology: taking a crib as an example.

Problem: How to design an intelligent multimodal fusion hazard prediction system using AIoT (Artificial Intelligence & Internet of Things) technology to predict some potential dangers. This article will provide some ideas and methods.

Introduction: This article designs an intelligent multimodal fusion hazard prediction system based on AIoT technology.

Comprehensive economic impact assessment of regional shipping emissions: A case study of Shanghai, China.

Shipping emissions have a direct negative impact on the ecological environment, residents' health and social economy of port cities. However, the relevant literature seldom focuses on the quantitative analysis of economic losses. A Regional CGE (RCGE) model with shipping emissions and diffusion is constructed to evaluate the comprehensive economic impact on Shanghai in this paper.

Metasurface spectrometers beyond resolution-sensitivity constraints.

Conventional spectrometer designs necessitate a compromise between their resolution and sensitivity, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum, where operational range can be altered or extended simply through adjusting geometric parameters.

SE-MAConvLSTM: A deep learning framework for short-term traffic flow prediction combining Squeeze-and-Excitation Network and Multi-Attention Convolutional LSTM Network.

Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars. The prediction of public bicycle flow can be more reasonable to plan the release and deployment of public bicycles.

Application of improved ant-lion algorithm for power systems.

An improved ant-lion algorithm is proposed to solve the load allocation problem to improve the efficiency of load allocation in the power system. The global search capability and optimization performance of the algorithm have been significantly improved by introducing elite weights and chaotic search mechanisms. The innovation of the research lies in not only optimizing economic goals, but also considering environmental goals, achieving dual optimization of economy and environment.

Federated cross-view e-commerce recommendation based on feature rescaling.

As big data technologies continue to evolve, recommendation systems have found broad application in domains such as online retail and social networking platforms. However, centralized recommendation systems raise numerous data privacy concerns. Federated learning addresses these concerns by allowing model training on client devices and aggregating model parameters without sharing raw data.

BS-CP: Efficient streaming Bayesian tensor decomposition method via assumed density filtering.

Tensor data is common in real-world applications, such as recommendation system and air quality monitoring. But such data is often sparse, noisy, and fast produced. CANDECOMP/PARAFAC (CP) is a popular tensor decomposition model, which is both theoretically advantageous and numerically stable.

Cooperation resonance based on link strategy reinforcement learning and conformity.

We propose a game model that integrates reinforcement learning (RL) with link strategies and conformity behavior to investigate the emergence and maintenance of cooperation. The model operates on a lattice network with periodic boundaries and includes two types of nodes: RL nodes with link strategies and conformist nodes. Simulation results reveal a range of critical mass.

Rate adaptive compressed sampling based on region division for wireless sensor networks.

Image acquisition and transmission in wireless sensor networks (WSN) are core issues for some resource-deficient multimedia sensing applications. Reducing sampling rates and data transmission lowers sensor node costs and energy, addressing communication bottlenecks. Block compressed sampling (BCS) can meet the above requirements.

Computer-assisted diagnosis for axillary lymph node metastasis of early breast cancer based on transformer with dual-modal adaptive mid-term fusion using ultrasound elastography.

Accurate preoperative qualitative assessment of axillary lymph node metastasis (ALNM) in early breast cancer patients is crucial for precise clinical staging and selection of axillary treatment strategies. Although previous studies have introduced artificial intelligence (AI) to enhance the assessment performance of ALNM, they all focus on the prediction performances of their AI models and neglect the clinical assistance to the radiologists, which brings some issues to the clinical practice. To this end, we propose a human-AI collaboration strategy for ALNM diagnosis of early breast cancer, in which a novel deep learning framework, termed DAMF-former, is designed to assist radiologists in evaluating ALNM.

Application of a sEMG hand motion recognition method based on variational mode decomposition and ReliefF algorithm in rehabilitation medicine.

Hand motion intention recognition has been considered as one of the crucial research fields for prosthetic control and rehabilitation medicine. In recent years, surface electromyogram (sEMG) signals that directly reflect human motion information are ideal input sources for prosthetic control and rehabilitation. However, how to effectively extract components from sEMG signals containing abundant limb movement information to improve the accuracy of hand recognition still is a difficult problem.

Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition.

The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a 4 × 100 Gbps dense wavelength division multiplexing (DWDM) optical network.

YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images.

A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Based on this reconstruction, the P2 detection layer is established, which optimizes network structure, enhances multi-scale feature fusion performance, and expands the detection network's capacity for multi-scale complicated targets.

A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems.

To address the limitations of traditional optimization methods in achieving high accuracy in high-dimensional problems, this paper introduces the snow leopard optimization (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by the territorial behaviors of snow leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, SLO achieves a balance between exploration and exploitation, to navigate vast and complex search spaces.

MCGAN-a cutting edge approach to real time investigate of multimedia deepfake multi collaboration of deep generative adversarial networks with transfer learning.

The proliferation of multimedia-based deepfake content in recent years has posed significant challenges to information security and authenticity, necessitating the use of methods beyond dependable dynamic detection. In this paper, we utilize the powerful combination of Deep Generative Adversarial Networks (GANs) and Transfer Learning (TL) to introduce a new technique for identifying deepfakes in multimedia systems. Each of the GAN architectures may be customized to detect subtle changes in different multimedia formats by combining their advantages.