MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9.

With the development of deep learning technology, object detection has been widely applied in various fields. However, in cross-dataset object detection, conventional deep learning models often face performance degradation issues. This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment.

DSC-Net: Enhancing Blind Road Semantic Segmentation with Visual Sensor Using a Dual-Branch Swin-CNN Architecture.

In modern urban environments, visual sensors are crucial for enhancing the functionality of navigation systems, particularly for devices designed for visually impaired individuals. The high-resolution images captured by these sensors form the basis for understanding the surrounding environment and identifying key landmarks. However, the core challenge in the semantic segmentation of blind roads lies in the effective extraction of global context and edge features.

LESA-Net: Semantic segmentation of multi-type road point clouds in complex agroforestry environment.

Point-cloud semantic segmentation is a visual task essential for agricultural robots to comprehend natural agroforestry environments. However, owing to the extremely large amount of point-cloud data in agroforestry environments, learning effective features for semantic segmentation from large-scale point clouds is challenging. Therefore, to address this issue and achieve accurate semantic segmentation of different types of road-surface point clouds in large-scale agroforestry environments, this study proposes a point-cloud semantic segmentation network framework based on double-distance self-attention.

RadarTCN: Lightweight Online Classification Network for Automotive Radar Targets Based on TCN.

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction.

Accurate and fast implementation of soybean pod counting and localization from high-resolution image.

Introduction: Soybean pod count is one of the crucial indicators of soybean yield. Nevertheless, due to the challenges associated with counting pods, such as crowded and uneven pod distribution, existing pod counting models prioritize accuracy over efficiency, which does not meet the requirements for lightweight and real-time tasks.

Methods: To address this goal, we have designed a deep convolutional network called PodNet.

Double-Strip Array-Based Metasurfaces with BICs for Terahertz Thin Membrane Detection.

A double-strip array-based metasurface that supports the sharp quasi-bound states in the continuum (quasi-BICs) is demonstrated in terahertz regions. By tuning the structural parameters of metal strips, the conversion of BICs and quasi-BICs is controllable. The simulated results exhibit an achieved maximum Q-factor for quasi-BICs that exceeds 500, corresponding to a bandwidth that is less than 1 GHz.

AFTR: A Robustness Multi-Sensor Fusion Model for 3D Object Detection Based on Adaptive Fusion Transformer.

Multi-modal sensors are the key to ensuring the robust and accurate operation of autonomous driving systems, where LiDAR and cameras are important on-board sensors. However, current fusion methods face challenges due to inconsistent multi-sensor data representations and the misalignment of dynamic scenes. Specifically, current fusion methods either explicitly correlate multi-sensor data features by calibrating parameters, ignoring the feature blurring problems caused by misalignment, or find correlated features between multi-sensor data through global attention, causing rapidly escalating computational costs.

WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network.

Background: Detection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition.

TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting.

Accurately and rapidly counting the number of maize tassels is critical for maize breeding, management, and monitoring the growth stage of maize plants. With the advent of high-throughput phenotyping platforms and the availability of large-scale datasets, there is a pressing need to automate this task for genotype and phenotype analysis. Computer vision technology has been increasingly applied in plant science, offering a promising solution for automated monitoring of a large number of plants.

A Power System Harmonic Problem Based on the BP Neural Network Learning Algorithm.

At present, due to the large-scale use of different kinds of power electronic devices in the power system, the problem of harmonic pollution in the power grid is becoming more and more serious, which will lead to a serious decline in the production, transmission, and utilization rate of electric energy, overheat electrical devices, generate vibration and interference, and then affect the aging and service life of the lines. In order to effectively reduce the harmonic problems caused by different levels of the power system, it is necessary to analyze the harmonic components. In this paper, the BP neural network learning algorithm is introduced into the harmonic problems of the power system.

Study on the angiogenesis ability of Polymethyl methacrylate-mineralized collagen/Mg-Ca composite material in vitro and the bone formation effect in vivo.

Magnesium (Mg) and its alloys show high degrees of biocompatibility and biodegradability, used as biodegrad able materials in biomedical applications. In this study, Polymethyl methacrylate (PMMA) - mineralized collagen (nano-Hydroxyapatite/collagen; nHAC)/Mg-Ca composite materials were prepared, to study the angiogenesis ability of its composite materials on Human umbilical vein endothelial cells (HUVECs) and its osteogenesis effect in vivo. The results showed that the PMMA-nHAC reinforcement materials can promote the proliferation and adhesion in HUVECs of Mg matrix significantly, it can enhance the migration motility and VEGF expression of HUVECs.

Review on the COVID-19 pandemic prevention and control system based on AI.

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic.

ARDformer: Agroforestry Road Detection for Autonomous Driving Using Hierarchical Transformer.

Road detection is a crucial part of the autonomous driving system, and semantic segmentation is used as the default method for this kind of task. However, the descriptive categories of agroforestry are not directly definable and constrain the semantic segmentation-based method for road detection. This paper proposes a novel road detection approach to overcome the problem mentioned above.

Regulation of Magnesium Matrix Composites Materials on Bone Immune Microenvironment and Osteogenic Mechanism.

Despite magnesium based metal materials are widely used in bone defect repair, there are still various deficiencies, and their properties need to be optimized. Composites synthesized with magnesium based metal as matrix are the research hotspot, and the host immune response after biomaterial implantation is very important for bone binding. By studying the immunoregulation of bone biomaterials, it can regulate the immune response in the process of osteogenesis and create a good local immune microenvironment, which is conducive to biomaterials to reduce inflammatory response and promote good bone binding.

Design and Characterization of a Microfluidic Circuit for Air Particulate Matter Separation.

Air microfluidic circuits have been widely concerned in the separation of atmospheric particulate matter, especially for portable particulate matter separation detection devices. Currently, no systematic approach for the design and optimization of an air-microfluidic system for PM separation has been reported in the literature. In this paper, a two-stage air microfluidic circuit is designed.

Surface Modification of Reduced Graphene Oxide Beads: Integrating Efficient Endotoxin Adsorption and Improved Blood Compatibility.

As a pathogenic toxin, endotoxins are the culprit for endotoxemia and can be generally removed from the blood by hemoperfusion. Reduced graphene oxide (rGO) is a promising endotoxin sorbent for hemoperfusion owing to its excellent adsorption capacity, but it has the side effect of nonspecific adsorption and low blood compatibility. Polymyxin B (PMB) acts as an organic affinity ligand that can specifically bind endotoxins.

Multi-angle head pose classification with masks based on color texture analysis and stack generalization.

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification.

Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis.

In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 h of germination were continuously collected at one-hour intervals.

Effect of magnesium ions/Type I collagen promote the biological behavior of osteoblasts and its mechanism.

Type I collagen (Col I) is a main component of extracellular matrix (ECM). Its safety, biocompatibility, hydrophilicity and pyrogen immunogenicity make it suitable for tissues engineering applications. Mg also control a myriad of cellular processes, including the bone development by enhancing the attachment and differentiation of osteoblasts and accelerating mineralization to enhance bone healing.

Stability and infectivity of coronaviruses in inanimate environments.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus that can transmit through respiratory droplets, aerosols, or contacts. Frequent touching of contaminated surfaces in public areas is therefore a potential route of SARS-CoV-2 transmission. The inanimate surfaces have often been described as a source of nosocomial infections.

Fear can be more harmful than the severe acute respiratory syndrome coronavirus 2 in controlling the corona virus disease 2019 epidemic.

The current corona virus disease 2019 outbreak caused by severe acute respiratory syndrome coronavirus 2 started in Wuhan, China in December 2019 and has put the world on alert. To safeguard Chinese citizens and to strengthen global health security, China has made great efforts to control the epidemic. Many in the global community have joined China to limit the epidemic.

A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

Service perception analysis is crucial for understanding both user experiences and network quality as well as for maintaining and optimizing of mobile networks. Given the rapid development of mobile Internet and over-the-top (OTT) services, the conventional network-centric mode of network operation and maintenance is no longer effective. Therefore, developing an approach to evaluate and optimizing users' service perceptions has become increasingly important.

La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks.

In emerging energy-harvesting wireless sensor networks (EH-WSN), the sensor nodes can harvest environmental energy to drive their operation, releasing the user's burden in terms of frequent battery replacement, and even enabling perpetual sensing systems. In EH-WSN applications, usually, the node in energy-harvesting or recharging state has to stop working until it completes the energy replenishment. However, such temporary departures of recharging nodes severely impact the packet routing, and one immediate result is the routing loop problem.

A review on the exploitation of biodegradable magnesium-based composites for medical applications.

In recent years, materials science research based on magnesium (Mg) alloys has increased significantly due to their notable advantages over traditional metals. However, magnesium alloys are susceptible to excessive degradation and subsequent disruption of mechanical integrity; this phenomenon limits the utility of these materials. Mg alloys can thus be combined with other materials to form composites for medical applications.