bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid soil strength prediction model for geotechnical ground investigation using convolutional neural network and ensemble learning
Authors: Zhang Xuqun , Li Zhili , Sui Yaohua , Liu Chengjun , Li Zhaofeng 
Published: August 2024

Abstract
A hybrid model of soil strength prediction from the image data and feature data of soil was proposed, which is aimed at providing an efficient solution in geotechnical ground investigation by leveraging the power of AI. In this model, CNN purely with the encoder was used to establish the relationship between image data and strength parameters of soil, while ensemble learning stacked of three base learners, i.e., KNN, LASSO, and LSVM, was applied to the feature data. Then, the CNN part and the ensemble learning part were integrated into the loss function. Results show that the convergence of the hybrid model was slower than that of the ensemble learner. However, with the aid of soil image which has more features of soil in essence, the predicted soil strengths by the hybrid model better matched with the actual ones, compared with the ensemble learner and base learners. This hybrid model offers an effective framework of multi-modal data fusion for geotechnical engineering, by leveraging the synergy of the high-dimensional feature extraction capabilities of CNN and the generalization abilities of ensemble learning.
A study on civil engineering construction simulation technology based on artificial intelligence for civil buildings
Authors: Yi Jiachen , Liu Qian , Fan Liwei , Zhang Jiahao , Sun Haoyun 
Published: August 2024

Abstract
This research delves into utilizing Artificial Intelligence (AI) in civil engineering, specifically construction simulations. The primary objective is to reconcile the discrepancies between conventional structural analysis techniques and the actual performance of structures. By integrating AI technologies—neural networks, cellular automata, and support vector machines— with data mining, this research proposes a novel approach for simulating civil building constructions. Traditional methods, often constrained by underlying assumptions, fail to capture the complex behaviors of engineering structures accurately. The developed AI simulation framework utilizes experimental and on-site data to construct digital models, enabling precise prediction of structural behaviors without the limitations of traditional assumptions. Results indicate that AI-enhanced simulations can significantly improve the accuracy of structural analysis, demonstrating potential for wider application in civil engineering. This advancement promises to enhance the predictability, efficiency, and safety of civil constructions, marking a significant step forward in the field.
Stability analysis and anti-windup design of saturated switching systems based on multiple Lyapunov functions
Authors: Guo Rui , Zhang Xinquan 
Published: August 2024

Abstract
In this paper, employing advanced multiple Lyapunov functions (MLF) technology, we conduct a thorough investigation into the fault-tolerant characteristics of actuator saturation-based systems, particularly those prone to failures. Based on this comprehensive analysis, we carefully design an anti-windup (AW) controller, incorporating an efficient AW compensator and precisely calibrated gains for the dynamic state feedback controller, tailored to meet the specific needs of practical control systems. To further demonstrate the effectiveness of our approach, we also provide a detailed numerical example.
Detection of pedestrians and vehicles on foggy roads based on improved YOLOv7
Authors: Geng Shuai , Niu Datian , Qin Changjie , Bai Caiyun , Hao Zeran 
Published: August 2024

Abstract
Nowadays, object detection technology plays an indispensable role in the direction of automatic vehicle driving. However, inclement weather, such as fog and haze, poses severe challenges to the ability to understand visual perception and scene understanding of automatic driving technology, making it extremely difficult. Therefore, this paper proposes an improved YOLOv7 algorithm. Firstly, Gaussian blur and Gaussian noise are used to process the original data set, reducing the sensitivity of our model to image information, improving the ability to adapt to changing environments and tasks, and enhancing the generalization performance. Then, using the SIOU loss function, we optimize the algorithm to accelerate the convergence speed while considering the vector angle problem. Add Coordinate Attention integrates accurate position information into the coding network, optimizes the recognition accuracy of the model for a specific position, significantly improves its overall feature extraction ability, and captures key features more accurately, thereby improving the overall performance, capturing key information, and reducing the possibility of missed detection. The introduction of CoordConv into ELAN in the backbone allows for a more sensitive perception of the position of objects during image processing, thus improving the accuracy of its recognition and positioning. The RepGhostNet structure is introduced into the neck to realize the implicit reuse of features. According to the experiments in this paper, our improved YOLOv7 algorithm achieves 87.3% map in the RTTS data set and partial synthetic fog KITTI data set, which improves by 3.9% based on the original model.
Research on named entity recognition method for high-speed railway technology transformation project text
Authors: Lian Xiaoqin , Bao Kexin , Gao Chao , Gong Yonggang , Wu Yanhua , Cheng Zhibo 
Published: August 2024

Abstract
High-speed railway technology transformation projects are crucial to the layout of high-speed rail transportation production, high-speed rail safety, and high-speed rail operation and management. However, the data of high-speed railway technology transformation projects is mainly stored in the form of unstructured text, and the relevant personnel of high-speed railroad often need to manually go through the form to extract the key information of the project data and enter it into the system, with a low degree of automation and intelligence. Therefore, to improve the efficiency and quality of processing the data of high-speed railway technology transformation projects, and help the intelligent development of high-speed railways, this paper constructs the RoBERTa-BiLSTM-CRF fusion model to automate the identification of key information entities in the data of high-speed railway technology transformation project. The experimental data were selected as technology transformation project documents of a railway bureau from 2013 to 2022, and six defined entity types were identified. Experimental results show that the constructed model can effectively identify six types of defined entities, and the F1 value of each type of entity is above 90%. In addition, compared with other commonly used models, the F1 value has been significantly improved. The fusion model proposed in this paper can be used as a method to automatically extract key information for high-speed rail technology transformation projects.
Experiment and path optimization on Large-Space Multi-Area crowd evacuation
Authors: Chen Peizhu , Zeng Li , Zhong Yaojun 
Published: August 2024

Abstract
In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.
Research on key algorithm optimization strategy of oscillographic blood pressure measurement
Authors: Wang Long , Luo Yiqun , Tang Zhenghua 
Published: August 2024

Abstract
In this paper, adaptive filtering and wavelet de-baseline drift algorithms are proposed for the signal preprocessing of the oscillographic blood pressure measurement algorithm. On feature extraction, fuzzy logic classification and dynamic programming tracking methods are designed. The blood pressure model of support vector machine and adaptive neural network is constructed. Active learning and cross-domain modeling are introduced to correct individual differences. This study comprehensively optimizes the key algorithm of oscillographic blood pressure measurement, and significantly improves the measurement accuracy, stability, and individual adaptability of the system.
A Human Activity Recognition model based on CNN and Transformer
Authors: Wang Man , Liu Rutong , Xiong Yong 
Published: August 2024

Abstract
This study aims to utilize data from built-in sensors in smartphones for human activity recognition. By analyzing the three-dimensional accelerometer and gyroscope data in user behavior, accurate classification of eight common activity states is achieved, including walking, standing, sitting, squatting, going up stairs, going down stairs, climbing ladders, and descending ladders. To enhance the model’s generalization capability, a method combining Transformer neural networks with one-dimensional Convolutional Neural Networks (CNNs) is employed, along with data sample augmentation. Experimental results demonstrate a significant improvement in recognition accuracy compared to traditional models, indicating the potential for real-time application on smartphones and other devices. This approach provides essential technical support for predictive human-computer interaction on smart devices and holds extensive application prospects.
Speech emotion recognition method based on time-aware bidirectional multi-scale network
Authors: Zhang Liyan , Du Jiaxin , Li Jiayan , Wang Xinyu 
Published: August 2024

Abstract
In response to the difficulty of traditional speech emotion recognition models in capturing long-distance dependencies in speech signals and the impact of changes in speaker pronunciation speed and pause time, this paper proposes a new time emotion modeling method called Time Perceived Bidirectional Multi-scale Network (TIM-Net), which is used to learn Multi-scale contextual emotion expression in different time scales. TIM-Net starts by acquiring temporal emotional representations using time-aware blocks. Subsequently, information from different time points is combined to enhance contextual understanding of emotional expression. Finally, it consolidates various Timescale features to better accommodate emotional fluctuations. The experiment shows that the network can focus useful information on features, and the WAR and UAR of TIM-Net are significantly better than other models on RAVDESS, EMO-DB, and EMOVO datasets.
Drone inspection path planning with self-organizing neural network of elastic Hebbian learning rule
Authors: Li Rui , Zhang Lei , Yi Chenying , Lu Yufeng , Chen Liangyuan , Su Yi , Huang Wei 
Published: August 2024

Abstract
As the scale of the power grid continues to expand, inspection scenarios become increasingly complex, and inspection and maintenance work in the power industry faces huge challenges. The traditional manual inspection mode has problems such as high cost, low efficiency, and poor safety. Using electric drones to inspect all equipment nodes in the area can effectively reduce operation and maintenance costs and ensure personnel safety. As a key technology to realize autonomous power inspection, UAV path planning needs to overcome difficulties such as long solution time and intersection of inspection paths. Therefore, this article conducts research on UAV path planning algorithms for large-scale power inspections, which is of great significance to improving the efficiency of power inspections. To avoid the occurrence of path intersections and improve the path planning effect, this paper uses the convex hull property in graph theory to design elastic Hebbian learning rules and proposes a self-organizing neural network algorithm based on elastic Hebbian learning rules.
Land morphology analysis with LiDAR technology to increase oil palm production
Authors: Wijayanti S I , Hadi I P , Tanjung A A , Islami J D , Adilah A H A , Pulungan N A H J 
Published: August 2024

Abstract
The morphological condition of the land plays an important role in determining the quality of growth of oil palm plants. Integrated management of oil palm plantations is a key factor in increasing productivity. Analysis of land morphology in oil palm plantations is a crucial first step. The objectives of this study were (1) to characterize land morphology with LiDAR implementation, (2) to explain the LiDAR mechanism in the NDVI class classification used for determining the oil palm Vegetation Index, and (3) to provide ideas to optimize oil palm productivity. The study was conducted using the image interpretation method from the acquisition of LiDAR data which has a resolution of 3 x 3 cm in the form of DEM and orthophoto to be able to perform land surface morphology analysis (MPL) and NDVI flatfoot. The data needed in this study is Orthophoto, which is used to crosscheck field conditions, plant canopy conditions, and populations of plants per block. NDVI processing is used to determine the Vegetation Index to interpret the health of oil palm plants. The results showed that LiDAR technology can be used to determine the health of oil palm plants. The overall accuracy and reliability value of NDVI reached 88.33% and 88.13%, respectively. This shows that the value of NDVI can predict the health of oil palm plants and can be used to monitor them effectively.
Ecological, Socioeconomic, and Economic Factors Shaping Vegetable Oil Production in the Batak People Community of North Sumatera
Authors: Bernadtua Simanjuntak Marudut , Sawitri Wulandari Retno , Barasa Larsen , Soritua Sijabat Panderaja , Mudakir  , Gunawan Malau April 
Published: August 2024

Abstract
This research delves into the intricate interplay between ecological, socioeconomic, and economic factors in the production of vegetable oils, with a specific focus on the Batak People community in Tapanuli-Toba Samosir, North Sumatra. The research highlights the profound importance of considering these multifaceted aspects within the context of farmers’ labour rights and community empowerment, thereby illuminating the industry’s broader implications. The study is structured into three principal dimensions: ecological consideration, socioeconomic dimensions, and economic viability. The study assesses farmers’ working conditions and wages, their engagement with national and international labour standards, and the extent to which the vegetable oil industry influences the socioeconomic well-being of the Batak People community. It analyses market dynamics, value chain processes, and the influence of government policies and subsidies on the industry’s economic prospects. By doing so, the research aims to discern how the vegetable oil industry can contribute to the region’s broader economic development. To foster knowledge exchange and collaborative problem-solving, an international seminar is proposed. This seminar will bring together academics, government researchers, industry experts, and community representatives. The event’s objectives include sharing research findings, addressing industry challenges, expanding networks, and crafting a sustainable framework for the vegetable oil sector. This research, alongside the international seminar, seeks to enhance our comprehension of encouraging sustainable practices and formulating policies that can serve as models for similar regions.
Socio-economic analysis of oil palm farmers in Kuantan Singingi District, Riau
Authors: Marbun Firdaus 
Published: August 2024

Abstract
Oil Palm is one of the most important sources of vegetable oil today. It has become more critical when utilised as a renewable energy source. These advantages make it a commodity of choice for both farmers and corporations. Despite controversies such as deforestation, oil palm expansion is unstoppable because more and more people invest and convert their agricultural land into oil palm plantations. One of the cases happened in Kuantan Singingi District, where many farmers have converted their agricultural land into oil palm plantations. This study explains the factors influencing farmers to convert their plantation land into oil palm plantations. This study also demonstrates the impact of the conversion on improving their economy. The expectation of high profits in oil palm yields influences the diversion of the land. However, the conversion to oil palm is not always paralleled with improving farmers’ welfare. Through a qualitative approach using FGDs and interviews with oil palm farmers in two villages in Kuantan Singingi District, Land conversion to oil palm plantations carried out by Kuantan Singingi farmers is predominantly influenced by technical factors, market access and prices. The transformation that occurred has not been able to improve their economy. Some indications of this failure are a lack of business capital, limited land ownership, and limited knowledge of oil palm cultivation.
Strategies for improving independent oil palm smallholders’ welfare in Konawe Regency, Southeast Sulawesi
Authors: Purba S F , Witjaksono J , Djaenudin D , Taridala S A , Imran I , Yulianti A , Muslimin M , Fadwiwati A Y , Sitompul R F 
Published: August 2024

Abstract
The oil palm plantation sector in Konawe Regency, Southeast Sulawesi, plays a vital role in the regional economy: absorbing labour, encouraging economic growth, and improving oil palm smallholders’ welfare. The welfare of independent oil palm smallholders (farmers) has not been maximized, even though smallholders are faced with fulfilling their basic household needs. This research aims to identify strengths, weaknesses, opportunities, and threats and provide strategies for improving the welfare of independent palm oil smallholders in Konawe Regency. This study used primary data (interviews, observations, focus group discussions, and documentation) and secondary data (literature review). SWOT analysis was used to determine the factors of opportunities, threats, strengths, and weaknesses and formulate strategies to improve independent oil palm smallholders’ welfare. The research findings show that weaknesses and opportunities are the main priorities for strengthening smallholder welfare. Factors affecting the welfare of smallholders are the quality and selling price of fresh fruit bunches, oil palm workers and farmers’ skills, production costs, maintenance of oil palm plantations, and oil palm farming institutions. As for the results of the SWOT analysis, the primary strategy is the formation and enhancement of the role of independent oil palm smallholder institutions in Konawe Regency.
Vegetable oils marketing strategies: Multimodal discourse analysis on advertising in Indonesian television
Authors: Reflinaldi  , Triana H W , Hadi S , Tafiati  , Wartiman  , Ilham M 
Published: August 2024

Abstract
This study aims to map the marketing strategies for vegetable oils in Indonesian television advertisements. Qualitative-explanatory research was conducted on vegetable oil advertisements from 1992-2023. Theo van Leeuwen’s multimodal discourse analysis is used as an analytical framework to describe linguistic signs, images, and sounds in data sources. The results showed that among the marketing strategies for vegetable oils are exploring the features of palm fruit as a raw material, the oil processing process, which involves the latest technology, the chemical properties of the oil, and the vitamins and nutrients in the oil. Another strategy is to describe the delicious cooking results and the positive psychological effects consumers feel. Oil producers also elaborated on the economic aspects of consumers by promising low prices and lottery prizes. Environmental and health issues emphasise vegetable oils’ long-term positive impact. Several experts in relevant fields appeared to strengthen arguments for the features of each commodity. The marketing strategy through advertising is quite interesting, with stunning visual illustrations, sounds, and words. Paying attention to today’s digital world, this marketing strategy will continue according to the trends and media consumers widely use.
Jurisdictional risk assessment and implementation of theory of change to sustainable palm oil in Pelalawan District, Riau Province
Authors: Juniyanti L , Puspitaloka D , Setiyo O Y , Azzahra M , Ali M 
Published: August 2024

Abstract
The jurisdictional approach has been widely used to address multifaceted issues associated with commodity-driven deforestation. Risk assessment at the jurisdictional level is required to implement jurisdictional programs. We developed a risk framework and indicator to assess the risks associated with achieving sustainable palm oil, using Pelalawan District as a case study. Pelalawan scored 0.48 out of 1, with the highest risks in the percentage of deforestation driven by oil palm plantation in mineral forest and pressure due to demand by global trade/export. Risks that are not mitigated will hamper the district’s effort to achieve sustainability. Following the assessment, we brought together multistakeholder in a workshop to develop a common vision using the theory of change framework. This study introduces new ways to approach sustainable palm oil initiatives in the palm oil sector using risk assessment and a theory of change framework.
Phenomenological Study of the Simirah Information System in Addressing Cooking Oil Scarcity (2021-2023)
Authors: Toni A , Jauharotul M 
Published: August 2024

Abstract
This research explores the government policy regarding the surge in cooking oil prices in 2022, a topic of ongoing debate among stakeholders. The findings reveal that the direct cash assistance (BLT) program’s regulatory impact assessment underscores lower-class economic principles’ volatility and the challenges of simplifying government policies, often perceived as unfeasible. On the other hand, the monitoring of bulk cooking oil (MGCR) producers benefits entrepreneurs and does not directly alleviate poverty. However, the practical implementation of the Simirah application system, a significant tool in this context, presents substantial and urgent hurdles for small-scale vendors. Their limited understanding of the system, coupled with their lack of technological knowledge and insufficient socialisation about the monitoring procedures related to cooking oil distribution, severely impede their effective use of the system. Large traders and distributors of cooking oil, with their relatively good technological knowledge, often step in as support and representatives for these small-scale vendors.