A lightweight Color-changing melon ripeness detection algorithm based on model pruning and knowledge distillation: leveraging dilated residual and multi-screening path aggregation.

Color-changing melons are a kind of cucurbit plant that combines ornamental and food. With the aim of increasing the efficiency of harvesting Color-changing melon fruits while reducing the deployment cost of detection models on agricultural equipment, this study presents an improved YOLOv8s network approach that uses model pruning and knowledge distillation techniques. The method first merges Dilated Wise Residual (DWR) and Dilated Reparam Block (DRB) to reconstruct the C2f module in the Backbone for better feature fusion.

YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture.

Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained.

OPG gene-modified adipose-derived stem cells improve bone formation around implants in osteoporotic rat maxillae.

Background: Osteoporosis is a significant barrier to the use of dental implants in the elderly for the treatment of tooth defects. Adipose derived stem cells (ADSCs) have demonstrated extensive potential for tissue repair and regeneration. The present study aimed to investigate the effectiveness of ADSCs engineered to express high levels of osteoprotegerin (OPG) for the treatment of bone loss in implant dentistry caused by estrogen deficiency.

A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs.

Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field.

Detection and Recognition Algorithm of Arbitrary-Oriented Oil Replenishment Target in Remote Sensing Image.

In view of the fact that the aerial images of UAVs are usually taken from a top-down perspective, there are large changes in spatial resolution and small targets to be detected, and the detection method of natural scenes is not effective in detecting under the arbitrary arrangement of remote sensing image direction, which is difficult to apply to the detection demand scenario of road technology status assessment, this paper proposes a lightweight network architecture algorithm based on MobileNetv3-YOLOv5s (MR-YOLO). First, the MobileNetv3 structure is introduced to replace part of the backbone network of YOLOv5s for feature extraction so as to reduce the network model size and computation and improve the detection speed of the target; meanwhile, the CSPNet cross-stage local network is introduced to ensure the accuracy while reducing the computation. The focal loss function is improved to improve the localization accuracy while increasing the speed of the bounding box regression.

R-YOLO: A YOLO-Based Method for Arbitrary-Oriented Target Detection in High-Resolution Remote Sensing Images.

In view of the existence of remote sensing images with large variations in spatial resolution, small and dense objects, and the inability to determine the direction of motion, all these components make object detection from remote sensing images very challenging. In this paper, we propose a single-stage detection network based on YOLOv5. This method introduces the MS Transformer module at the end of the feature extraction network of the original network to enhance the feature extraction capability of the network model and integrates the Convolutional Block Attention Model (CBAM) to find the attention area in dense scenes.

CGR-Block: Correlated Feature Extractor and Geometric Feature Fusion for Point Cloud Analysis.

Point cloud processing based on deep learning is developing rapidly. However, previous networks failed to simultaneously extract inter-feature interaction and geometric information. In this paper, we propose a novel point cloud analysis module, CGR-block, which mainly uses two units to learn point cloud features: correlated feature extractor and geometric feature fusion.

Topographical organization of mammillary neurogenesis and efferent projections in the mouse brain.

The mammillary body is a hypothalamic nucleus that has important functions in memory and spatial navigation, but its developmental principles remain not well understood. Here, we identify progenitor-specific Fezf2 expression in the developing mammillary body and develop an intersectional fate-mapping approach to demonstrate that Fezf2 mammillary progenitors generate mammillary neurons in a rostral-dorsal-lateral to caudal-ventral-medial fashion. Axonal tracing from different temporal cohorts of labeled mammillary neurons reveal their topographical organization.

piR-823 demonstrates tumor oncogenic activity in esophageal squamous cell carcinoma through DNA methylation induction via DNA methyltransferase 3B.

Piwi-interacting RNAs (piRNAs) dysregulation occurs frequently in extensive cancers. However, there was no report about piRNA expression in esophageal cancer (EC). In this study, the expression levels of piR-823 and DNMT1, DNMT3A, DNMT3B were detected in 54 pairs of ESCC tissues and adjacent normal tissues using the quantitative real-time polymerase chain reaction method.

Association between polymorphisms in the genes, and smoking, alcohol and upper digestive tract carcinomas in a high-incidence area of northern China.

Metabolic gene variants, smoking, and alcohol consumption are important upper digestive tract cancer (UDTC) risk factors. However, the gene-gene and gene-environment interactions remain unclear. A case-control study in a high incidence area for upper digestive tract cancer was conducted in China.

[Research on electroencephalogram emotion recognition based on the feature fusion algorithm of auto regressive model and wavelet packet entropy].

Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band.

[Research on analysis method of multi-fractal de-trended fluctuation of electroencephalogram focus on mental stress evaluation].

The multi-fractal de-trended fluctuation analysis was used to estimate the mental stress in the present study. In order to obtain the optimal fractal order of the multi-fractal de-trended fluctuation analysis, we analyzed the relationship between singular index and Hurst index with order. We recorded the electroencephalogram (EEG) of 14 students, compared the relationship between singular index, Hurst index and quality index, ensured the optimal order being [-5, 5] and achieved the estimation of mental stress with the β wave in the EEGs.

Strategies and Tools for Combinatorial Targeting of GABAergic Neurons in Mouse Cerebral Cortex.

Systematic genetic access to GABAergic cell types will facilitate studying the function and development of inhibitory circuitry. However, single gene-driven recombinase lines mark relatively broad and heterogeneous cell populations. Although intersectional approaches improve precision, it remains unclear whether they can capture cell types defined by multiple features.

[Applications of Wavelet Transform Combining Empirical Mode Decomposition in EEG Analysis with Music Intervention].

In the present paper,wavelet transform and empirical mode decomposition(EMD)are combined to extracted the features of electroencephalogram(EEG)signal with music intervention,and to achieve a better classification accuracy rate and reliability in emotional assessment in order to provide a support for music therapy.The data were from Database for Emotion Analysis using Physiological Signals(DEAP).Based on wavelet transformα,βandθrhythms were extracted at frontal(F3,F4),temporal(T7,T8)and central regions(C3,C4).

[Affective Stress Rating Method Based on Improved Hidden Markov Model].

To solve the defect which is recognizing but not rating the stress,or rating but not considering the influence of the previous stress state to the current state of the existing affective stress evaluation method,this paper proposes an approach of affective stress rating model on electrocardiogram(ECG).An affective stress rating algorithm based on hidden Markov model(HMM)was established with the theory of affective computing.The individual’s affective stress was rated using this affective rating model combining the investigation questionnaire.

[Methods and Applications of Psychological Stress State Assessment].

In this paper, the response of individual's physiological system under psychological stress state is discussed, and the theoretical support for psychological stress assessment research is provided. The two methods, i.e.

[Affective computing--a mysterious tool to explore human emotions].

Perception, affection and consciousness are basic psychological functions of human being. Affection is the subjective reflection of different kinds of objects. The foundation of human being's thinking is constituted by the three basic functions.