bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization design and simulation of chip-level micro-gas columnar preconcentrator structure based on ANSYS
Authors: Lu Luoping , Ran Yunzheng , Tian Xiangpeng , Zheng Ronghao , Zhong Jianwei , Liao Honghua 
Published: August 2024

Abstract
To achieve the efficient preconcentration of measured sample gas in microfluidic chips, this paper proposes a design and simulation scheme for the chip-level micro-gas columnar pre-concentrated structure based on ANSYS software. Firstly, four kinds of chip-level micro-gas columnar preconcentrators with circular, semi-circular, triangular, and rhombic shapes were designed, and the simulation analysis of gas-phase micro-fluidic dynamics was performed on the chip-level micro-gas columnar preconcentrator by ANSYS software. The influence of different design parameters on the pre-concentrated effect of level chips micro-gas columnar preconcentrator was discussed. The simulation results show that the arrangement is a triangle arranged in reverse parallel, and the preconcentration effect of the chip-level micro gas column preconcentrator is better.
Research on model predictive trajectory tracking control based on particle swarm optimization
Authors: Yu Yu , Shen Xingkui , Wang Weiling 
Published: August 2024

Abstract
This paper focuses on the challenges of low trajectory tracking accuracy in intelligent vehicles caused by different road adhesion coefficients and vehicle speed conditions, aiming to address these challenges with an improved Model Predictive Control (MPC) algorithm. Firstly, a model encompassing vehicle dynamics featuring two degrees of freedom has been formulated, along with a corresponding model to track trajectory errors. Secondly, a Model Predictive Control (MPC) algorithm is designed with trajectory tracking accuracy and control increment as the objective functions. Additionally, incorporating a Particle Swarm Optimization (PSO) algorithm with MPC allows for the dynamic computation of the optimal prediction horizon size. Afterward, a unified simulation model is assembled, employing both CarSim and MATLAB/Simulink, to conduct dual-lane trajectory tracking simulation analysis under diverse road adhesion and vehicle speed conditions. The efficacy of the proposed control algorithms is validated through this process.
Intelligent following car based on Ultra-wideband technology
Authors: Zhao Rujie , Zhang Heng , Qi Fenghua 
Published: August 2024

Abstract
To facilitate convenience and reduce the burden of traveling, this paper introduces an intelligent following car designed with ultra-wideband wireless carrier communication (UWB) technology as the core, combined with a variety of sensor peripherals. The car is controlled by the STM32F407XGT6 microcontroller, and the pressure sensor inside the car box can realize real-time measurements of the goods inside the box. In addition, the intelligent following car is loaded with an ultrasonic automatic obstacle avoidance device around the car, and the bottom of the car is loaded with an automatic trajectory device. The car adopts the function of wireless charging so that the body can achieve better airtightness. The car also has the function of the Internet of Things to transmit data, so that it can realize the diversification of the functions of the transport of goods, which not only greatly facilitates people’s lives but also lays the foundation for the intelligent transportation of goods.
Human action recognition based on smart sensors
Authors: Lin Lanxin , Sun Di 
Published: August 2024

Abstract
This paper focuses on human activity recognition using LSTM (Long Short-Term Memory) for handling time series data issues. Because LSTM can better deal with the correlation and long-term correlation of time-series data, the model is utilized to build a human-computer interaction action recognition system. The experimental steps include the following: first, the bracelet sensor obtains the human body acceleration signal to obtain a data set containing a variety of motion postures and preprocesses the data. Then, LSTM is used to extract the features of the dataset, and the feature vector is utilized as the input to train the model iteratively. Finally, to improve the accuracy of recognition, a variety of machine learning models will be applied to compare the performance with the LSTM model. The experimental results indicate that the proposed model can better capture the acceleration changes of human motion in various life scenes, and can accurately distinguish various sudden action patterns of the human body in daily life. This also makes the human movement behavior recognition system proposed in this study more comprehensive.
Dual-mode object detection algorithm based on feature enhancement and feature fusion
Authors: Ji Qingbo , Qi Yufei 
Published: August 2024

Abstract
In complex conditions, visible light cannot meet the challenge of high-precision target detection requirements, so we propose a dual-modal target detection algorithm, named Dual-Mode Object Detection Based on Feature Enhancement and Feature Fusion (DEF). The algorithm in this paper is improved based on YOLOv5. First, we build a dual-branch feature extraction network for visible light and infrared images, separately extracting features of the images. Subsequently, the extracted feature maps are input separately into a feature enhancement module, which is constituted by a multi-branch convolutional architecture and the ECA (Efficient Channel Attention) mechanism, to acquire an augmented set of salient features. Finally, a module for feature fusion is devised to merge the modality-specific features that are extracted by the two branches. The integrated feature maps are then sent into the subsequent module to achieve dual-mode image target detection. The effectiveness of the proposed algorithm is confirmed through a series of experiments conducted using the FLIR and LLVIP datasets, showcasing its superior performance in practical applications.
Hand-object pose estimation based on HOPE-net and attention mechanism
Authors: Li Pangshan 
Published: August 2024

Abstract
Hand-object pose estimation is an important part of studying hand pose, focusing on the joint detection of hand and object poses. Although CNN has shown advantages in various hand pose estimation, due to various problems such as the complex structure of the hand pose and self-occlusion, there are limitations in the extraction of image features and the amount of calculation. Existing studies have shown that GCNs have a good performance in dealing with such problems. In this paper, we propose to combine the attention module with two adaptive GCNs, and perform feature enhancement operations on hand features. While ensuring the accuracy of feature extraction, it can effectively improve the network calculation speed and avoid some questions such as self-occlusion. Experimental results show that, through end-to-end training, the algorithm has a great performance on the problem of hand-object pose estimation.
Argo data anomaly detection algorithm based on selective ensemble of fuzzy clustering
Authors: Zhu Wanlu , Jiang Yongguo , Zhang Meng 
Published: August 2024

Abstract
Argo profiling float data is a crucial data source for fundamental research and predictive forecasting operations in oceanography and environmental science. However, compiling and organizing such datasets demands considerable time and human resources. Therefore, the quest for effective methods of detecting anomalies in Argo data is of paramount importance. In this regard, we propose three improvement strategies within the stacking ensemble framework: preserving the original training set, weighting base model outputs, and combining the two former methods. The aim is to explore implicit relationships within the data, enhance model prediction diversity, and improve Accuracy. Additionally, in the selection of base models, to address the challenge of conventional clustering-based ensemble algorithms in achieving high levels of both diversity and accuracy among base learners, we introduce a selective ensemble method based on C-means clustering. This method selects base learners for the ensemble based on weighted scores derived from membership and performance evaluation metrics. Both of these enhancement approaches demonstrate effective application and improved detection performance when applied to Argo data.
Orthogonalized layer features with wavelet for multi-object tracking
Authors: Ban Yandeng , Yang Jinlong 
Published: August 2024

Abstract
Multi-object tracking (MOT) in crowded scenes presents challenges such as target occlusion and interference from similar objects. The detection models that rely on the target center as positive samples often struggle with noise introduced by ambiguous data annotations. To address these issues, we propose to segregate the target features of distinct frequency tiers by leveraging wavelet decomposition. Features obtained from two-dimensional wavelet decomposition can exhibit orthogonality and complementarity along the horizontal and vertical directions. Low-frequency components are usually related to visible targets, while high-frequency energy often emanates from targets undergoing occlusion, enabling discrimination between multi-layered objectives. Experimental evaluations are conducted on the MOT17 and MOT20 benchmarks (motchallenge.net), which demonstrate that our approach has better competitive performance compared to the current state-of-the-art methods.
Research and application of rough set attribute reduction algorithm based on genetic algorithm in pipeline network
Authors: Li Hong , Zhou kai , Zhuang Zhao 
Published: August 2024

Abstract
Explosion accidents often occur in urban underground pipeline network systems, which has caused safety issues in public life and industrial production. GIS technology can visually manage pipeline equipment and solve the problem of valve closure analysis in the event of current accidents, avoiding the escalation of the situation, but it cannot make predictions. Attribute reduction is one of the core contents of rough set theory and is an NP-hard problem. This article proposes a custom attribute reduction algorithm based on an algorithm and uses the algorithm in the historical burst pipeline dataset of the water supply pipeline industry. The results have shown that this method combined with a decision tree can make predictions before water pipe leakage. The algorithm provides the best result of attribute reduction for decision tree methods, which will improve the speed and accuracy of decision-making.
Enhancing Robot Learning with Transformer-based Morphology Modeling
Authors: Wang Yanying 
Published: August 2024

Abstract
The transformer model has made significant progress in various areas through large-scale training. In contrast, the traditional robot performs a single task, and there is an issue with migrating the strategic model. In this study, a Robot Morphology Learning (RML) method is proposed to enhance efficiency and generalization performance by learning multiple tasks in a transformer model. RML constructs the robot’s morphology as a graph and utilizes a graph neural network to handle graphs of arbitrary connections and sizes, addressing the disparity in state and action space dimensions. RML breaks through the limitation of non-migration of models, realizes efficient training, and improves the generalization performance of models, enabling quick adaptation to new tasks. Experimental results show that the proposed method outperforms previous methods in both multi-task learning and transfer learning experiments.
Compliance control of a rehabilitation massage robot in dynamic scenes
Authors: Wang Fan , Chen Chao , Di Chong , Liu Zhaoyang , Wang Zengfa 
Published: August 2024

Abstract
Medical robots encounter challenges when interacting with people or operating in complex and dynamic environments due to the variability of human morphology and the unpredictability of environmental changes. Compliance of human-robot interaction is the primary goal of medical robots when in contact with the human body. Therefore, robots must be able to adaptively adjust their forces and actions to ensure safety and comfort during the contact process. This paper focuses on the compliance control of rehabilitation massage robots in dynamic scenes. We propose a mechanical arm compliance control method based on the Soft Actor-Critic (SAC) algorithm. We construct a simulated massage environment in a dynamic scene according to the task requirements and design a massage path covering the entire back. Under the framework of deep reinforcement learning, the optimal reward function is designed to achieve constant force control under dynamic scenes. Through numerous simulation experiments, we have verified that the robotic arm can move along the predetermined path under the massage while maintaining a constant contact force with the body simulation module. The actual contact force and target contact force control are realized within 0.1 N.
Probiotics and Vegetable Oil Association: A Review
Authors: Hussain Abrar , Parveen Sara , Riaz Mahwish , Zia Amina , Abid Ali Syed 
Published: August 2024

Abstract
Probiotics are live microorganisms that confer a health benefit on the host when administered in adequate amounts. Currently, seven genera are used for their probiotic’s potential. Strains having the desired characteristics are associated with plenty of health benefits. Recent literature shows that different plant and vegetable oils are associated with probiotics. Other reports indicated their various aspects. For instance, the effect of probiotics on plant-based oil, edible oil, probiotics, the use of oils for probiotics encapsulation, etc., are documented. It is also reported that probiotics from vegetable sources and plant oil have some advancements, such as vegetarian and lactose-intolerant people using them. The multidimensional association between probiotics and vegetable oils attracts researchers to explore it. This research area is growing fast, but more limited research data is needed. Hence, this literature study was performed to identify the relationship, pros, and cons and provide recent insight into the literature for the researchers. Materials were collected by searching the related terminologies on different websites, finding the downloaded literature, and analyzing it critically. The results were presented as described by the authors, and a correlation was found that helps to enhance the field. Collectively, it was found that probiotics and plant and vegetable oils have a multifactorial relationship, and both can increase the effects of one another.
Detection of new oil palm fruit bunches based on deep learning
Authors: Harmiansyah  , Sembiring E P , Oviana E T , Supriyanto  
Published: August 2024

Abstract
The Indonesian oil palm plantation experiences a production rate of fresh fruit bunches and crude palm oil (CPO) influenced by the quality of fruits with excellent and uniform maturity accepted by the palm oil processing factory. The uniformity of fruit quality produced from the harvesting process through fruit maturity criteria, namely fractions 00, fraction 1, fraction 3, and fraction 4, necessitates the need for maturity detection based on deep learning with a pre-trained YOLOv5 model capable of detecting the maturity of new oil palm fruit bunches. The aim is to develop a deep learning-based maturity detection model based on visual images and analyse the deep learning algorithm’s performance by classifying the maturity level of new oil palm fruit bunches. The method collects a dataset of new oil palm fruit bunches with 4180 images. The collected dataset will be annotated with Roboflow, so the annotation results will undergo training, validation, and testing processes using the deep learning script. The result obtained for the data found to detect the ripeness levels of Fresh Fruit Bunches (FFB), shows that the YOLOv5 model demonstrates strong performance with an accuracy reaching 72.5% in testing, which falls into the category of fair annotation. These results indicate that the system created and used is functioning well.
The effect of mycorrhiza on the growth of Ylang-Ylang seedlings
Authors: Anita Nugraheni Y M M , Mansur Irdika , Sulistiani Erina 
Published: August 2024

Abstract
Ylang-ylang (Cananga odorata forma genuina Steenis) is a plant with high economic value and many benefits. Mycorrhizal application is necessary to obtain a quality mother plant. Therefore, a study on the mycorrhiza effect on ylang-ylang growth was needed, especially in the seedling stage. This study aimed to analyse the impact of mycorrhiza infection on the development of ylang-ylang seedlings. The method used in this study was the isolation of Claroideoglomus etunicatum spores. Ten of each spore was inoculated onto the sterilised roots (using 5% Natrium hypochlorite) of the three-month-old ylang-ylang seedlings and then planted on zeolite media and soil in a 6 x 10 cm pottery, which was then transferred to each pot 13 cm in diameter and 16 cm in height. For comparison, zeolite and soil media were used without the addition of mycorrhiza. The result of this study showed that the application of mycorrhizae using soil media had the highest increase in height (4.19 cm), diameter (0.72 mm), number of leaves (2.6 strands), and number of buds (0.8 leaf buds) compared to other treatments.
Evaluation of environmental impact of small-scale oil palm cultivation in Ghana: A life cycle assessment of Juaben Municipal District
Authors: Biney M , Yudha Y S , Agbolosoo J A 
Published: August 2024

Abstract
Palm oil has become increasingly popular in recent years due to its various uses in the food, cosmetics, and biofuel industries, resulting in a high global demand. In the 2021/22 marketing year, palm oil production reached approximately 73.8 million metric tons, up from 73 million in 2020/21. Palm oil yields more per area than other oil crops such as sunflower, soybean, and rapeseed. It is also economically feasible to grow in areas where other crops typically do not thrive, providing a source of income for many people. Palm oil cultivation has also contributed to economic growth, rural community development, and poverty reduction in disadvantaged regions, especially in Indonesia, Ghana, and Malaysia. The study’s findings indicate that Fresh Fruit Bunches (FFB) production significantly contributes to the global warming index. The study also shows that the eutrophication effect resulting from fertiliser usage was lower, with a total value of 1.8 kg PO4 equivalent, which is lower than the values reported in previous studies. This is attributed to the common usage of fertilisers in the study area due to financial constraints. An in-depth analysis of small-scale farmers’ local vegetable oil production was also studied, focusing on the challenges encountered during oil palm cultivation and processing.
The position of Indonesia’s palm oil in the vegetable oil international market
Authors: Nugrahapsari R A , Harianto  , Nurmalina R , Fariyanti A 
Published: August 2024

Abstract
The increase in global crude oil prices and awareness of energy sustainability are driving the development of the biofuel industry. Palm oil has high productivity and competitive prices compared to other vegetable oils, encouraging competition among major palm oil exporters and significant vegetable oils. The research examines factors influencing Indonesia’s position in the global vegetable oil competition and assesses how economic shocks impact Indonesian palm oil competitiveness. This study utilized the Almost Ideal Demand System model on secondary data in the time series period of 1990-2022. The research results showed that the increase in global income is the most influential factor in the rise in the market share of Indonesian palm oil. The increase in crude oil prices favored the position of Indonesian palm oil compared to Malaysia. Fluctuations in Malaysian palm oil prices could affect the competitiveness of Indonesian palm oil. Malaysia’s palm oil, soybean oil, and sunflower oil are more complementary than Indonesia’s. Indonesia needs to collaborate with significant vegetable oil exporters, to create a palm oil commodity exchange, and to synchronize policies from upstream to downstream to ensure the fulfillment of domestic and export palm oil needs.
Structure of Social Forestry Service Area in Cenrana District, Maros Regency
Authors: Ali A , Supratman  , Syamsu A 
Published: August 2024

Abstract
Damaged road access, absence of markets in some villages, and lack of access to technology are challenges for forest communities in the Cenrana District. Optimising service centres in social forestry (SF) locations can support social forestry activities. Based on scalogram and gravity analysis, the structure of social forestry areas in the Cenrana Sub-district shows Limampoccoe Village as the sub-district level service centre, providing warehousing, commodity distribution, quality control, and packaging and branding services. Labuaja Sub-Village (Laiya Village), Malaka Sub-Village (Cenrana Baru Village), Bululohe Sub-Village (Rompegading Village), and Nahung Sub-Village (Labuaja Village) serve as commodity auction market locations, local trials, counselling, honey cultivation facilities, and processing technology for derivative products such as aren palm, candlenut, porang, and coffee. Road infrastructure and tourist attraction signposts are in Malaka Hamlet (Cenrana Baru Village). Conflicts in Labuaja Village require conflict resolution in social forestry management.
Current status of Indonesia’s palm oil products and their competitiveness in the global market
Authors: Setiajiati F , Nurrochmat D R , van Assen B W. , Purwawangsa H 
Published: August 2024

Abstract
Oil palm is recognised as contributing significantly to crucial economic growth in Indonesia. Still, it is often considered a severe threat to tropical forest ecosystems due to the large amount of forest conversion for oil palm cultivation. Also, oil palm business and marketing processes have many opportunities and challenges. In response, this research examines and reveals in depth the current state of Indonesia’s palm oil business and marketing in the global market. By interviewing, reviewing the literature, and conducting a stakeholder analysis, we found that Indonesia has high competition for global palm oil, with processed products such as Refined Palm Oil and Oleochemical Mix dominating the world market and high export value growth. However, the export value for crude oil, such as palm oil and palm kernel oil, in 2020 - 2021 is low due to government policies for downstream and industrialisation programs. The government, as policymakers, the oil palm funding agency (BPDPKS), and the oil palm companies are key players with high interest and influence in the sustainable development of the oil palm business. European Union and Press can be context setters with low interest but high influence on business and marketing processes in Indonesia.