bluuebunny/crossref_metadata_2025_split huggingface dataset:
PMotion: an advanced markerless pose estimation approach based on novel deep learning framework used to reveal neurobehavior
Authors: Lv Xiaodong , Liu Haijie , Chen Luyao , Dai Chuankai , Wei Penghu , Hao Junwei , Zhao Guoguang 
Published: May 2023

Abstract

Objective. The evaluation of animals’ motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation methods are unreliable, unpractical, and inaccurate. Approach. Data augmentation (random scaling, random standard deviation Gaussian blur, random contrast, and random uniform color quantization) is adopted to augment image dataset. For the key points recognition, we present a novel efficient convolutional deep learning framework (PMotion), which combines modified ConvNext using multi-kernel feature fusion and self-defined stacked Hourglass block with SiLU activation function. Main results. PMotion is useful to predict the key points of dynamics of unmarked animal body joints in real time with high spatial precision. Gait quantification (step length, step height, and joint angle) was performed for the study of lateral lower limb movements with rats on a treadmill. Significance. The performance accuracy of PMotion on rat joint dataset was improved by 1.98, 1.46, and 0.55 pixels compared with deepposekit, deeplabcut, and stacked hourglass, respectively. This approach also may be applied for neurobehavioral studies of freely moving animals’ behavior in challenging environments (e.g. Drosophila melanogaster and openfield-Pranav) with a high accuracy.
A method for density fluctuation measurements using pulse reflectometry
Authors: Krutkin O. , Brunner S. , Coda S. 
Published: May 2023

Abstract
An analytical 1D model based on the WKB approximation is used to study the properties of a short pulse reflectometry diagnostic in a magnetic fusion device. Expressions linking the pulse delay with the parameters of the turbulence near the cut-off layer are derived for both ordinary and extraordinary polarizations of the probing beam. These results are used to develop a method for measuring the turbulence amplitude and the radial correlation length. The analytical conclusions and the proposed method are validated using full-wave numerical modelling. The latter is also used to study the limitations of the method and potential experimental effects not included in the reduced model.
Steel surface defect detection based on Improved YOLOX Algorithm
Authors: Li Guohao , Mokhtarzadeh Amir Ali 
Published: May 2023

Abstract
As one of the very common industrial products, strip steel plays a very important role in various fields. Steel production has received extensive attention. In industrial production, scratches are unavoidable due to numerous forces beyond human control. Cracks and other flaws appear, and their presence has a direct impact on the product’s quality. Therefore, it is of great significance to develop efficient and accurate strip surface defect detection. In this study, the most widely used metal material strip steel is selected as the research object, and its surface defect detection problem is studied accordingly. The activation function and loss function are improved on the basis of the YOLOX model. Resolve the problem of overexposure and content distortion of the image caused by the high reflecting characteristics of the strip, which degrades the image quality, and the embedding of the sub-attention mechanism to enhance the regional characteristics of hidden small targets. The experimental results show that the mAP of the improved model for strip surfaces defect detection is 80.7%, which is 4.1% higher than that of the original YOLOX algorithm, and the improved model can reduce the false detection and missed detection rate of defects.
A deep neural network based feature fusion panoramic segmentation method
Authors: Fei Hongyan , Mokhtarzadeh Amir Ali , Zhou Jiayu , Geng Junzuo 
Published: May 2023

Abstract
Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality.
Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning
Authors: Gao Han , Mokhtarzadeh Amir Ali , Li Shaofan , Fei Hongyan , Geng Junzuo , Wang Deye 
Published: May 2023

Abstract
In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images.
A Small Sample Photovoltaic Hot Spot Identification Method Based on Deep Transfer Learning
Authors: Zhou Jincheng , Luo Xiao 
Published: May 2023

Abstract
A small-sample photovoltaic hot spot identification method based on deep transfer learning has been proposed as a solution to the problem that traditional deep learning models require a substantial amount of training data, whereas the number of hot spot effect samples is relatively low and difficult to collect. This issue can be resolved by the development of a solution that employs deep transfer learning. The Inception-v 4 model serves as the basis for building a deep transfer learning model. After finishing the training procedure using a small-sample hot spot dataset of negative sample multi-classification, a network model for hot spot detection is built. Experiments reveal that even when the number of accessible samples is restricted, the model trained using the deep transfer learning technique has great identification accuracy, a low false detection rate, and strong generalization capability.
Pore extraction method of rock thin section based on Attention U-Net
Authors: Li Mingjiang , Zhang Pan , Hai Tao 
Published: May 2023

Abstract
This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue improving the network by optimizing the parameters, with the number of network layers and the total number of parameters remaining unaltered. This requirement must be satisfied before moving on to the next stage. The capability of generalization enhances the impact of segmentation on particles as well as their accuracy. Experiments show that this method is significantly better than the traditional method for segmenting rock flakes with manual operation and has better results in the segmentation and extraction of fine particles compared with the mainstream convolutional neural network.
Indoor location recognition device and method based on indoor robot
Authors: Wang Zhiqiang , Mokhtarzadeh Amir Ali 
Published: May 2023

Abstract
This paper describes an indoor location recognition device and method based on an indoor robot. The device includes a computer Raspberry Pi, a lidar, a CMOS camera, an ultrasonic sensor, a first microcontroller, and a servo motor; the lidar is connected to the computer Raspberry Pi. Pie, the lidar is driven by a servo motor, the ultrasonic sensor is connected to the computer Raspberry Pi through the first microcontroller, and the computer Raspberry Pi controls the lidar to scan the location to measure the width and length of the current location, and controls the ultrasonic sensor to the location. Perform up and down scanning to measure the distance between the ultrasonic sensor and the ground and the ceiling respectively, and obtain the height of the room; the CMOS camera is connected to the computer Raspberry Pi, and the computer Raspberry Pi sends an instruction to control the CMOS camera to take pictures of objects and places and use them Opencv and other related technologies achieve the purpose of identification. The device and method can continuously identify indoor locations under various complex conditions, and at the same time improve the accuracy and reliability of location recognition.
Quantifying the contribution of major carbon producers to increases in vapor pressure deficit and burned area in western US and southwestern Canadian forests
Authors: Dahl Kristina A , Abatzoglou John T , Phillips Carly A , Ortiz-Partida J Pablo , Licker Rachel , Merner L Delta , Ekwurzel Brenda 
Published: May 2023

Abstract
Increases in burned forest area across the western United States and southwestern Canada over the last several decades have been partially driven by a rise in vapor pressure deficit (VPD), a measure of the atmosphere’s drying power that is significantly influenced by human-caused climate change. Previous research has quantified the contribution of carbon emissions traced back to a set of 88 major fossil fuel producers and cement manufacturers to historical global mean temperature rise. In this study, we extend that research into the domain of forest fires. We use a global energy balance carbon-cycle model, a suite of climate models, and a burned area (BA) model to determine the contribution of emissions traced to the major carbon producers to the long-term increase in VPD during 1901–2021 and to cumulative forest fire area during 1986–2021 in the western US and southwestern Canada. Based on climate model data, we find that emissions traced to these carbon producers contributed 48% (interquartile range (IQR) 38%–63%) of the long-term rise in VPD between 1901 and 2021. BA modeling indicates that these emissions also contributed 37% (IQR 26%–47%) of the cumulative area burned by forest fires between 1986 and 2021 in the western US and southwestern Canada. The increase in VPD in this region is linked to both increased fire activity and the region’s current and prolonged megadrought. As loss and damage from these hazards mounts, this research can inform public and legal dialogues regarding the responsibility carbon producers bear for addressing past, present, and future climate risks associated with fires and drought in the western US and southwestern Canada.
Impact of volcanic eruptions on extratropical atmospheric circulations: review, revisit and future directions
Authors: Paik Seungmok , Min Seung-Ki , Son Seok-Woo , Lim Eun-Pa , McGregor Shayne , An Soon-Il , Kug Jong-Seong , Yeh Sang-Wook 
Published: May 2023

Abstract
Understanding the impacts of volcanic eruptions on the atmospheric circulations and surface climate in the extratropics is important for inter-annual to decadal climate prediction. Previous studies on the Northern Hemisphere climate responses to volcanic eruptions have shown that volcanic eruptions likely induce northern Eurasian warming through the intensified Arctic polar vortex in the stratosphere and the positive phase of Arctic Oscillation/North Atlantic Oscillation in the troposphere. However, large uncertainties remain and the detailed physical processes have yet to be determined. The circulation responses in the Southern Hemisphere also remain controversial with large differences between the observed and model-simulated results. In this paper, we review previous studies on the extratropical circulation and surface climate responses to volcanic eruptions and update our understanding by examining the latest observational datasets and climate model simulations. We also propose new insights into the crucial role of the latitude of volcanic eruptions in determining the extratropical circulation changes, which has received less attention. Finally, we discuss uncertainty factors that may have important implications to the extratropical circulation responses to volcanic eruptions and suggest future directions to resolve those issues through systematic model experiments.
Achieving net-zero emissions in agriculture: a review
Authors: Rosa Lorenzo , Gabrielli Paolo 
Published: May 2023

Abstract
Agriculture accounts for 12% of global annual greenhouse gas (GHG) emissions (7.1 Gt CO2 equivalent), primarily through non-CO2 emissions, namely methane (54%), nitrous oxide (28%), and carbon dioxide (18%). Thus, agriculture contributes significantly to climate change and is significantly impacted by its consequences. Here, we present a review of technologies and innovations for reducing GHG emissions in agriculture. These include decarbonizing on-farm energy use, adopting nitrogen fertilizers management technologies, alternative rice cultivation methods, and feeding and breeding technologies for reducing enteric methane. Combined, all these measures can reduce agricultural GHG emissions by up to 45%. However, residual emissions of 3.8 Gt CO2 equivalent per year will require offsets from carbon dioxide removal technologies to make agriculture net-zero. Bioenergy with carbon capture and storage and enhanced rock weathering are particularly promising techniques, as they can be implemented within agriculture and result in permanent carbon sequestration. While net-zero technologies are technically available, they come with a price premium over the status quo and have limited adoption. Further research and development are needed to make such technologies more affordable and scalable and understand their synergies and wider socio-environmental impacts. With support and incentives, agriculture can transition from a significant emitter to a carbon sink. This study may serve as a blueprint to identify areas where further research and investments are needed to support and accelerate a transition to net-zero emissions agriculture.
Numerical Investigation the Effects of Cone Diameters on the Flow Pattern and Separation Efficiency in a Cyclone Separator
Authors: Ajarostaghi Seyed Soheil Mousavi , Mousavi Seyed Sina , Bhojaraju Chandrasekhar 
Published: May 2023

Abstract
In this work, the impact of cone diameters on the flow field and separation efficiency in a cyclone separator is examined numerically employing Reynolds Stress Model for eight various geometries of cyclone separators. The motion of solid particle in the flow field is modelled utilizing the Eulerian-Lagrangian method. Three-dimensional simulation of the air flow with solid particles in the cyclone is carried out by a commercial computational fluid dynamics software, ANSYS Fluent 18.2. The outcomes depict that by augmenting the small cone diameter, the maximum tangential velocity, static pressure, and collection efficiency decrease, however, cut-off diameter rises. Moreover, the axial velocity has steady trend throughout the cyclone. In addition, according to the obtained results, increasing the large cone diameter causes an increase in the tangential velocity and static pressure and improves the collection efficiency. While, the higher large cone diameter, the lower cut-off diameter. Growing the cyclone’s cone small diameter leads to a decrease in the collection efficiency. At a constant particle diameter, four microns, as the cone small diameter ratio augments by 150%, collection efficiency declines by about 10.42%. Augmentation the cyclone’s cone small diameter causes an increase in the cutoff diameter. By increasing the cone small diameter by about 150%, the cut-off diameter value increases by about 75%. Furthermore, the collection efficiency rises by growing the cyclone’s cone large diameter. At a constant particle diameter, four microns, as the cone large diameter augments by 50%, the collection efficiency increases by about 18.75%. Also, in all considered cone large diameter, the collection efficiency increases as the particle diameter rises. The cut-off diameter decreases by growing the cyclone’s cone large diameter. By augmenting the cone large diameter by about 50%, the cut-off diameter value declines by about 57.69%.
The Slope Instability Potential in the Balaroa area based on the Finite Element Method using Shear Strength Reduction
Authors: Ramadhani S , Martini  , Ihzan M , Basnur  
Published: May 2023

Abstract
Balaroa area is an alternative road connecting Palu City with Sigi and Donggala Regencies which experienced landslides due to the September 2018 earthquake. The landslide formed a steep slope and was prone to landslide. This study is to determine slope stability and design of optimal and safe slope geometry against landslides. Input and data analysis based on the Finite Element Method using Shear Strength Reduction with the help of RS2 software. The analysis resulted safety factor (SF) value on the existing slope without an earthquake load at locations 1 and 2 is in a stable condition with SF>1, after being given an earthquake load of 0.35g, the SF<1, while location 2 remained safe under the earthquake load with SF> 1. The total value of displacement on existing slope without earthquake load at location 1 was 0,006-0,040m and location 2 was 0,01-0,1m, after being given an earthquake load, it decreased 0,0045-0,030m at location 1 and increased 0,006-0,15m at location 2. This illustrates the existence of an earthquake greatly affects SF and total displacement. Safe slope geometry design for location 1 is slope height must be 10,2m with angles of 46° for level 1and 32° for level 2, with SF value 1.25.
Effect of soybean variety and pest management on the sustainable production
Authors: Aryati V , Ulina E S 
Published: May 2023

Abstract
Pests are one of the limiting factors in increasing soybean production. Pest control carried out by farmers using pesticides is feared to threaten the sustainability of soybean production in Indonesia. This study aimed to identify the effect of different pest management systems and various soybean varieties on pest population dynamics, abundance and diversity of natural enemies, plant damage, and soybean production. A split plot design with three replications was applied, where the main plot was pest management systems i.e. (1) control (which received neither chemicals nor integrated pest management), (2) chemical control, and (3) integrated pest management, and the sub-plot was soybean varieties i.e. (1) Anjasmoro, (2) Demas 1, and (3) Dering 1. Results indicated that variety affects pest diversity and abundance, however, it affected leaf damage, pod damage, and production. The pest management systems influenced the diversity and abundance of pests and natural enemies, plant damage, and production. Interaction between variety and pest management system affected plant damage. We conclude that the use of resistant varieties combined with integrated pest management could reduce plant damage, maintain the natural enemy population, and optimize soybean production.
Analysis of Volatile Metabolites from Papaya Seeds as Potential Organic Insecticides against Myzus persicae Sulz
Authors: Indratmi D , Haryanto C T , Rachmawan MD , Zakia A 
Published: May 2023

Abstract
Myzus persicae is a toxic pest that acts as a limiting factor in increasing chili production in Indonesia. Symptoms of leaf curl are always found in every chili growing season along with the presence of these pests. In the field M.persicae controlled with chemical insecticides that trigger pest resistance. The aim of the study was to determine the effectiveness of papaya seed extract in controlling M. persicae , its effect on chili yields, and the content of volatile toxic compounds in papaya seeds which have potential as organic insecticides. Efficacy test in the form of 7 treatments, 5 replicates, namely application of papaya seed 10, 30, 50, 70, and 90 g/l. Analyzed data using analysis of variance with the F test and Duncan Multiple Range Test to determine the best treatment. Analysis of volatile metabolites was performed using Gas Chromatography-Mass Spectrometry to obtain specific metabolites. Papaya seed extract contains several volatile metabolites that act as organic insecticides, namely Heptasiloxane, 1,1,3,3, 5,5,7,7,9,9,11,11,13,13 tetradecamethyl compounds. The efficacy test in the field showed that papaya seed extract with a concentration of 50-70 g/l was able to suppress Myzus persicae attacks to the low attack category level, and increase chili production up to 2 times (100%). The use of papaya seed waste as an organic insecticide is in accordance with the concept of sustainable agriculture because it is environmentally friendly and improves food safety.
Above ground biomass after 39 years logging in Kintap Research Forest, South Kalimantan
Authors: Qirom Muhammad Abdul , Halwany Wawan , Lestari Fajar , Rachmanadi Dony 
Published: May 2023

Abstract
Land degradation causes the fragmentation of forest areas. The fragmentation can be in the form of a decrease in species diversity and carbon storage in an area including logged-over areas. This study aims to obtain stand abundance, basal area, and potential of above ground biomass that can be used in estimating natural recovery in logged-over areas. Stand measurements were carried out in 3 different areas with the distance of the plot from the road as a differentiating factor. The measurement parameters include tree species, diameter, and tree height with the smallest diameter being 10 cm. Making plots at each location covering an area of 1 ha with an observation sub-plot measuring 20 m x 20 m (400 m2). Estimation of carbon storage used the allometric model compiled by Brown, Ketterings, and Basuki. The results showed that the biomass potential reached 190 tones/hectares – 290 tones/hectares. This potential is mostly contributed by trees with a diameter of more than 20 cm. Trees with a diameter of 20 cm accounted for more than 50% of the total biomass. This condition indicates that silvicultural technique in the form of maintaining the remaining stands after felling are very necessary for accelerating the growth of small-diameter trees so that natural succession can occur more quickly.