bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Application of Meteorological Data Based on K-Means Method to Highway Wind-Blown Sand Protection
Authors: Sun Yu , Zhou Yanbin , Hua Jiang , Cai Suri , Ni Cairong
Published: January 2024

Highway wind-blown sand reduces vehicle visibility and is likely to cause vehicle rollover and other accidents. In this paper, k-means algorithm is used to extract the annual maximum wind speed data of 10 stations in the central and western regions of Inner Mongolia and analyze the wind speed in different seasons. Through the results, it can be clearly seen that all stations in Inner Mongolia operate from April to June and from October to December. Wind speed shows strong characteristics. The strong performance of the wind speed is also affected by the topography of the central and western Inner Mongolia, which further increases the unsafe factors of road traffic. In addition, it can also be seen from the results that the highway environment around the meteorological station in Wengeng Town of Bayannur City is more susceptible to the influence of wind-blown sand, sandstorms, sand accumulation and other phenomena. Through the cluster analysis of this paper, it can provide scientific data support for the highway maintenance department to effectively resist the disaster caused by wind-blown sand.
Recent Advances in Battery Binders with an Insight of Predictive Commercial Development
Authors: Su Chang , Gao Xuan
Published: January 2024

Binders are a type of material used in batteries to hold the active materials together, forming a stable and functional battery electrode. Binders are typically made from polymeric materials and are used to obtain good mechanical adhesion, descent conductivity, and excellent overall performance. For their commercial development, this review shows the proportion of cathode/anode binders in different battery costs. In addition, aqueous binders have the tendency to gradually replace PVDF binders in the future by taking advantage of their low cost and create considerable output value. In application, as a new binder under the silicon-based anode, polyacrylic acid (PAA) has important advantages in enterprise product development.
Research and Enlightenment on Aviation Weaponry and Equipment Test and Appraisal
Authors: Pan Jihui , Qu Wenqing , Xue Hao , Zhang Lei , Li Hongkai
Published: January 2024

With the continuous improvement of the performance and complexity of modern aviation weaponry and equipment, traditional equipment testing and evaluation cannot meet the needs of modern warfare for accelerating equipment development and deployment, reducing development risks and costs, and improving economic affordability. The article summarizes the latest developments in the field of US military weapon Aviation weaponry and equipment test and appraisal in recent years, including institutional improvement, conceptual change, and method innovation. It analyzes the future requirements for weapon equipment testing and appraisal, and proposes suggestions to further strengthen test coordination, promote innovation in test appraisal technology, and do a good job in practical assessment based on joint operations.
Research on Network Security Access Based on Intelligent Active Defense
Authors: Fu Yunxi , Liu Zesan , Gao Ziting , Zhang Wenjuan
Published: January 2024

In recent years, with the construction of new power systems, the traditionally relatively closed power grid needs to be opened in an orderly way. The new power system forces more and more heterogeneous mass terminals to be exposed and deployed in the external network, and the power information system interacts frequently with the external system, increasing the attack surface, easy to be external mapping network key information, and increasing pressure of unknown attack. In order to realize the effective protection of network key information, reduce the risk of illegal detection and network attack, and provide technical support for the security protection of new power system. The first part of this paper is to solve the new attack threats faced by the power information system infrastructure by studying the network attack prevention and monitoring technology based on active intelligent defense. The second is to solve the increasingly serious problem of network scanning feature recognition by studying the defense strategies based on feature hiding and information confusion. In this way, the network security protection ability can be improved, and the construction of new power system can be strongly supported.
Research on Identity Authentication Technology for Intelligent Electrical Terminal Interaction
Authors: Zhang Yanrong , Shi Zhentong , Zhang Yanjie , Zhang Haijing , Yuan Yanfang , Wang Hui , Zhang Liang
Published: January 2024

The security risks caused by illegal access increase greatly with the device expansion in the State Grid Intelligent Electrical System. This paper analyzes the necessity of identity authentication of the Intelligent Electrical Terminal with the northern Master Station, the southern end terminal, the same level terminal and other extensive modules in the Intelligent Electrical Terminal System. The paper focuses on the identity authentication method between the Intelligent Electrical Terminal and the Master Station. The session keys are generated while the identity authentication is completed through session negotiation. The session keys are used for cryptography operation for transmission data between the Intelligent Electrical Terminal and the Master station. The two-layer key system is devised and the session keys’ duration can be controlled, the identity authentication must be re-done if duration expired. Thus, the security and timeliness of identity authentication is greatly enhanced and the security protection ability of intelligent electricity system is improved effectively.
Risk Prediction of Tension Pay-Off Method for Overhead Lines Based on Deep Learning and Short-Term Memory
Authors: Xu Zhongbo , Shen Qihao , Lv Weihong
Published: January 2024

With the rapid development of the science and technology era, digital technology has been widely used in power grid infrastructure projects, and intelligent equipment, high-precision sensing technology, and wireless ad hoc network equipment have been used in various fields of power grid infrastructure. Nowadays, the pay-off construction of overhead transmission lines has also realized intelligent visualization of centralized control, which has improved the safety of stringing construction. Overhead tension pay-off is a key infrastructure construction project, and for the safety of overhead tension pay-off, there are major breakthroughs in reducing labor costs, improving the automation degree of tension pay-off, and improving the working environment, which effectively improves safety construction efficiency. In this paper, the long-term and short-term memory network model using deep learning is used to model and quantitatively analyze the overhead tension pay-off, predict its future security risks, take timely measures, and adjust the operation strategy according to the prediction results of the model.
Design and Implementation of Arduino-Based Intelligent Low Temperature Smoke Warning Bedside Lamp
Authors: Bao Yiqin , Wu Yuhan
Published: January 2024

Because the temperature change in many places is particularly fast, especially when sleeping at night, sometimes because the temperature suddenly drops very low, the indoor temperature is lower than the comfortable temperature that the human body can adapt to, at this time if people do not open the air conditioning and do not add the quilt it is easy to catch a cold, and if the smoke is too large, there will be fire, carbon monoxide leakage situation will be very dangerous. To solve these problems, this paper designs an Arduino-based intelligent low-temperature smoke warning bedside lamp. When the temperature is suitable, this intelligent low-temperature smoke warning bedside lamp can be used as an ordinary bedside lamp, which can be turned on by pressing the button and then turned off by pressing the button. When the indoor brightness is very high, when the temperature is too low or the gas is too dense, the lamp can be automatically turned off. The buzzer buzzes, the LCD 16*2 display indicates that the temperature is too low or dangerous, and the LED light is on to facilitate the user’s operation, so as to avoid the user catching a cold or danger when sleeping because the temperature is too low.
Design and Implementation of Natural Language Processing Machine Translation System Based on Seq2Seq Model
Authors: Zhang Wenqi , Bao Yiqin , Xu Wenbin
Published: January 2024

Aiming at the bottleneck of system performance optimization in Machine translation system, long-distance dependence, difficulty in processing low-frequency words and rare words, a Natural language processing Machine translation system based on Seq2Seq model is designed, which effectively realizes multilingual translation and language communication, realizes accurate translation of multiple languages, and improves the efficiency of language communication. Based on the Natural language processing technology of Seq2Seq model, this paper quickly recognizes and translates the source language text, outputs the target language text, and through training the model, selects the task of French translation into English for experiment. The attention weight shows that the system performs well in the translation task.
Construction and Application of Network Ideological and Political Education Platform for Universities Based on Cloud Computing
Authors: Zhang Chunhui , Yao Mengdi
Published: January 2024

With the assistance of digital technology, the network ideological and political education platform of universities based on cloud computing has become an essential initiative in modern higher education. This kind of platform combines the powerful function of cloud computing and the characteristics of ideological and political education. It provides universities with a novel education mode and teaching instruments. Starting from the analysis of the necessity of the innovation of network ideological and political education in universities at the time of the intelligent era, the cloud platform of ideological and political education in universities is constructed, which includes conceptual elements, data elements, technological elements, and functional elements in one. To study the functional application and optimization strategy of the network ideological and political education platform of universities empowered by cloud computing is of great practical significance and theoretical value.
Development and Prospect of UAV Inspection and Certification
Authors: Wang Jian , Liu Wenwen , Pan Jihui
Published: January 2024

With the rapid development of science and technology in today’s world, drones have also “flown” into the homes of ordinary people from the era beyond our reach. In order to ensure the quality and safety of drones and the healthy development of the industry, this paper sorts out China’s industry policies, analyzes the status quo of UAV inspection and detection, analyzes the existing problems, and puts forward suggestions for drones.
Method and Algorithms for Improving Positioning Accuracy for Users with Restricted Signal Bandwidth in 5G NR
Authors: Maltsev Alexander , Serunin Igor , Pudeev Andrey , Hwang Seunggye , Ko Hyunsoo
Published: January 2024

In 5G NR systems, users with restricted signal bandwidth may suffer from poor positioning and range performance. In this paper, we present a detailed performance analysis of the frequency hopping method, allowing such users to cover significantly more frequency bandwidth while having a relatively small signal bandwidth in a given time interval. The performance is investigated in a non-stationary frequency selective channel in the absence of phase continuity between hops using coherent or non-coherent joint full frequency bandwidth signal processing algorithms. It was shown that the correct operation of the coherent algorithm requires the phase errors compensation between each pair of signal frequency hops. The proposed non-coherent signal processing algorithm does not require correction of the signal phases and demonstrates its advantages for high mobility users in comparison with the coherent algorithm.
Frequency-Domain Analysis of Atmospheric Electric Field and Thunderstorm Forecasting
Authors: Xiong Anming , Xu Haibo , Chen Zhenglong
Published: January 2024

The atmospheric electric field (AEF) has significant importance in thunderstorm warning. This paper first analyzed AEF variations during thunderstorms based on 386 samples and then divided the period into four stages, in which a certain time stage provided valuable information for lightning prediction. It was defined as the “Stage T.” Fast Fourier transform was employed to study the frequency-domain patterns of the AEF in this stage. The result showed that there were notable differences in the frequency spectrum distribution between thunderstorm and non-thunderstorm weather conditions. During thunderstorms, the AEF frequency spectrum amplitude was higher and the waveform exhibited a wide range of variations compared to non-thunderstorm conditions. Then we used the Euclidean distance classifier to discriminate between thunderstorm events and non-thunderstorm events in the modeling samples for lightning forecasting. The remaining 256 samples were used to validate and evaluate the effectiveness of the algorithm. For prediction time of 5min, 15min and 25min, probability of detection(POD) is greater than 77.78%, false alarm rate (FAR) is less than 16.67%, critical success index (CSI) is greater than 0.57. These results indicated that this method was effective for short-term thunderstorm forecasting.
Intelligent Assembly Scheduling of Large Laser Devices Based on Neural Network
Authors: Xiong Zhao , Wang Chengcheng , Hu Dongxia
Published: January 2024

Aiming at the dynamic flexible scheduling problem in the integrated installation of large-scale laser devices, a deep learning rule acquisition method based on artificial neural network is proposed. Firstly, the typical example is optimized by genetic algorithm, then the task comparison trajectory and characteristic data are extracted from the optimal solution, and the task priority model is generated by deep learning. Finally, the dynamic flexible scheduling decision mode is constructed based on the algorithm model, so as to realize fast response and accurate scheduling in complex, changeable and uncertain production environment. Data experiments and practical cases verify the effectiveness of this method. With the increase of the number of scheduling objects, the computational efficiency of ANN scheduling algorithm is obviously better than GA algorithm in the case of little difference in calculation results.
Multi-Skeleton Joint Behavior Recognition Method Based on a Dual-Stream Network
Authors: Mou Hongda , Tang Lianggui , Ran Jing , Wang Xue , Peng Song , Zhu Xiuling , Tang Chan
Published: January 2024

Skeleton joint data, as a representation closely related to human actions, has received significant attention in the field of human behavior recognition in recent years. However, current methods for behavior recognition using skeleton data face several challenges, such as insufficient accuracy and reliance on a single modality. To address these challenges, a multi-stream network approach for behavior recognition using multiple skeleton joints is proposed. Firstly, a top-down approach is employed to extract the coordinates of 133 key skeleton joints from the human body, which are then represented as heatmaps. Subsequently, in the PoseRgb model, RGB data is used as input for the RGB channel to capture spatial features, while the heatmap representations of skeleton data are utilized as input for the Pose channel to capture temporal features. Leveraging the fusion capabilities of heatmaps, these two channels are merged using lateral connections, ultimately yielding behavior recognition results. Experimental results demonstrate that on widely-used UCF101 and HMDB51 datasets, the accuracy reaches 99.05% and 86.1%, respectively. Compared to the Temporal Shift Module (TSM) network, there is a 5% and 15% improvement in accuracy, respectively. The proposed method effectively combines spatial and temporal information in videos, resulting in more accurate behavior recognition.
Personalization or Popularity? A Matter of Arrow’s Impossibility Theorem
Authors: Wang Hao
Published: January 2024

Recommendation by personalization is a hot research topic in the field of computer science. The global market cap for the field is approximately 20 billion USD with an annual growth rate of 40%. However, a debate on whether personalization or popularity is the best choice for online recommendation systems is still on-going, and most of the time there is no theoretical argument answering the question. In this paper, we apply Arrow’s Impossibility Theorem to the topic, and demonstrate that recommendation by personalization and recommendation by popularity are two entirely different algorithmic paradigms that are not equivalent to each other.
Deep Multi-Feature Fusion Neural Network for Text Classification
Authors: Yang Jingjing , Deng Feng , Chen Shiqiang
Published: January 2024

Most existing convolutional neural network-based text classification methods have problems such as inadequate feature extraction, long-distance dependency. This study presents a novel deep multi-feature fusion neural network for text classification. The convolutional neural network is employed to extract the local features of the text. An improved encoder with self-attention method is proposed to build the global relationships. Furthermore, a multiple feature fusion strategy is provided to integrate the features learned from the CNN and the encoder, which is beneficial to excavating the potential semantic information in a global perspective. The experimental results demonstrate that our proposed method has better classification performance than the alternative approaches.
Control Input Limited Control of Single-Joint Manipulator with Unknown Load
Authors: Liu Xuehang
Published: January 2024

Mechanical arms play a pivotal role across various domains, yielding significant improvements in automation, precision, efficiency, and safety. For a single-link mechanical arm dealing with unknown loads and constrained control inputs, the rotational inertia J is often unknown and can not be directly designed into control laws. To address this, this study employs adaptive estimation for rotational inertia and designs mapping adaptive laws. As a result, achieving control within limited inputs becomes feasible, with the extent of input limitations adjustable via parameters. Through Lyapunov analysis, the system’s convergence to a compact set of small radius is proven, and the efficacy of the proposed control approach is verified through MATLAB/SIMULINK numerical simulations.
Position and Speed Output Constrained Control of Manipulator Based on Barrier Lyapunov Function
Authors: Liu Xuehang
Published: January 2024

Robotic arms, as automation tools, play a pivotal role in emulating human hand movements and functions to carry out tasks like object grasping and processing. In tackling the challenge of output saturation in the actuator system of robotic arms, this study formulates constrained control laws using barrier Lyapunov functions. These control laws are engineered to achieve accurate tracking of the robotic arm’s position and velocity, all the while keeping the system state within predefined limits. To conclude, the efficacy of the proposed control methodology is substantiated through numerical simulations conducted using MATLAB’s S-functions.
Study of a Stars-Parallel Hyperchaos Laser Synchronization LAN with Two Type of Erbium-Doped Fiber Lasers
Authors: Yan Senlin , Lu Yuzheng
Published: January 2024

This paper examines a novel stars-parallel fiber laser synchronization local area network (LAN) and its hyperchaos synchronization systems. Its network structure was inducted, and its stars were composed of three-ring lasers of an erbium-doped fiber three-ring laser while its network nodes were composed of two types of nodes from two different sets of erbium-doped fiber dual-ring lasers; together, these nodes formed two groups in two links, each made up of two types. The paper illustrates our physical models of the network, including the stars, the nodes, and the parallel synchronization. The network synchronization structure and the laser synchronization cluster are presented. The system has the characteristics of star networks, parallel networks, multi-node networks. Our numerical results demonstrated that two erbium-doped fiber three-ring laser rings can synchronize with two different types of two sets of erbium-doped fiber dual-ring lasers in two links using the anti-phase-drive-response and delayed-time-feedback methods, respectively; this result included stars-parallel synchronizations in two links, the stars synchronizing with the nodes, parallel synchronization, hyperchaos synchronization, and existence of the laser synchronization cluster. Network functions indicated that hyperchaos in stars lead to certain behaviors in both the network and its nodes, and which will help in security and multi-channel secure communication. Our proposed fiber network has the potential to play a key reference role in the study of LAN, optical network, and secure communication.
Benefit Allocation Model for Distributed Electric Vehicle Using a Shapley-Value Approach
Authors: Wang Sike , Yu Liansong , Cao Peng , Ge Xiaohu , Hu Huafeng , Pang Bo , Luo Wei
Published: January 2024

This paper proposes a benefit allocation model for distributed electric vehicle. It considers the cooperative relationships between the stakeholders innovatively. The stakeholders consist of vehicles, charging piles, and power grids. The benefit of each stakeholder is calculated using the shapley-value approach. Based on the order of decision-making among stakeholders, the benefit allocation model includes inner layer and outer layer. The benefits of vehicles and charging piles are calculated by inner layer. The benefits of power grids are calculated by outer layer. Then, the computational flow of the model is presented according to the nested relationships in proposed benefit allocation model. Case studies validate the feasibility of the proposed model. Result shown that the proposed model can increase the gain of trolley compared to conventional.
Simplified Monocular Ranging Anomalous Behaviour Detection Model for Security Monitoring Applications
Authors: Liu Qing , Wu Yu , Nie Jing , Li Yi , Zheng Xiaohu , Liu Kang , Wang Ping
Published: January 2024

A simplified monocular ranging anomalous behaviour detection model is proposed to address the problems of cumbersome calibration of the traditional monocular ranging camera and the difficulty of selecting anomalous behaviour detection models. The model uses the YOLO target detection algorithm to obtain the pixel position of the target ROI region in the image coordinate system, and then converts the pixel position to the world coordinate system by using the vertical and horizontal field of view angles of the camera and the camera resolution. Based on the camera mounting height and tilt angle, the distance between the target and the camera vertical point is calculated and compared with the preset defense zone distance and the model switching distance to determine whether the target needs to be detected for abnormal human behaviour and which abnormal human behaviour detection model should be used for detection. In addition to ranging the target without camera calibration, the model also divides the anomaly detection network for two different human features by the image distortion distance, which improves the convergence speed and recognition accuracy of the anomaly detection network.
Improving Facial Emotional Recognition Using Convolution Neural Network with Minimal Layer
Authors: Tshibangu Rogerant , Tapamo Jules R.
Published: January 2024

Human emotions identification has many applications, including human-computer interaction, illogical analysis, medical diagnosis, data-driven animation, and human-robot interaction. This paper presents a classification model, ConvNet that extracts features from facial images using techniques such as local binary patterns (LBP), convolutional neural networks (CNN), and region-based oriented FAST and rotational BRIEF (ORB). This model converges quickly. Experiment show that ConvNet outperforms existing methods with a precision of 98.13% on the CK+ dataset and 92.05 % on the JAFFE dataset.
Dynamic Talking-Head Generation with Speech Emotion Recognition and Intensity Detection
Authors: Fu Chen , Yu Yang , Zhang Zhiqiang , Weng Wei , Zhou Jinjia
Published: January 2024

In recent years, speech emotion recognition (SER) and emotive talking head generation have drawn significant attention. However, existing methods either focus predominantly on emotion recognition without considering its intensity or produce emotionally consistent talking heads, neglecting the rich expressiveness of human interactions. Addressing these limitations, our study aims to bridge these two areas, proposing an innovative talking-head generation approach that further enhances the realism of the generated video. Within the SER domain, we develop a dual-level model that not only improves the accuracy of identifying emotions in speech data but also assesses their intensity. This dual-capacity model significantly enhances the depth and subtlety of emotional recognition, achieving an industry-leading accuracy rate of 73.5%. On the talking head front, this work generates emotionally expressive talking heads that reflect both emotional state and intensity. Beyond this, the model also simulates human facial dynamics like blinking, thus offering a more comprehensive and realistic representation of human speech. These advances effectively address the shortcomings of existing methods and provide a new avenue for the accurate portrayal of emotional intensity in synthesized talking heads. Extensive experimental results demonstrate the effectiveness and superiority of our approach over existing methodologies in both SER and talking head generation.
Overview and Application of Sparrow Search Algorithm in Deep Learning
Authors: Wang Hongjun , Fei Teng , Liu Lanxue , Wu Kangle
Published: January 2024

A brand-new swarm intelligence optimization algorithm called the Sparrow Search Algorithm (SSA) was put forth in 2020. By simulating the foraging process of sparrows, SSA efficiently solves optimization problems, exhibiting advantages such as faster convergence and excellent optimization abilities. This paper introduces the basic principles of the Sparrow Search Algorithm, analyzes its existing issues, summarizes improvements made to existing Sparrow algorithms, and then discusses the application of SSA in deep learning.
Improving Wearable Solutions with Nudging Actions in the Chronic Care Management: The SENIOR Project
Authors: Castelnuovo Gianluca , Cammisuli Davide Maria , Usubini Anna Guerrini , Marchesi Gloria , Bellocchio Virginia , Vignati Benedetta , Granese Valentina , Pavanello Giada , Pietrabissa Giada
Published: January 2024

In order to avoid that many senior citizens with Mild Cognitive Impairment (MCI) could become chronic patients with a low level of functioning in many cognitive, behavioral and emotional domains, some technological solutions have been improved to better enhance less compromised healthy lifestyle, providing real-time clinically-based suggestions and reducing some dysfunctional behaviors. Moreover another critical problem is the overweight and obesity that can affect many people in Western but also Eastern regions. In order to cope with MCI and obesity, mhealth monitoring solutions (smart-watches based and wearable based) have been developed. The major aim of this paper is to show the rationale behind the SENIOR Project, a Cariplo funded project that will develop and deliver wearable monitoring solutions with tailored nudging feedbacks for patients with obesity an MCI. Scientific background, rationale, aims, outcomes measured and innovations are discussed.
Research on Transmission Line Image Defogging Method with Improved Dehazenet Algorithm
Authors: Feng Siliang , Ni Hongxia , Qi Yinpeng , Zhao Liying , Zhang Haosong , Zhang Zhilong , Chen Dianzhi
Published: January 2024

Intelligent detection methods are gradually being integrated into power grid inspection work. There are many weather factors in the process of intelligent inspection, which can affect data acquisition. In order to address the problems of distortion and imbalanced contrast in images obtained in foggy weather, this paper proposes an improved DehazeNet algorithm. The algorithm introduces parallel convolutional layers conv_a and divides reshape_a to improve feature extraction effectiveness and retain corresponding features while increasing receptive field. Multi-scale image feature extraction is achieved through four convolutional layers of different sizes using the parallel convolutional structure. The bilateral linear rectification unit is used as the activation function to effectively estimate the transmission rate. Experimental results show that the improved DehazeNet algorithm has a 5.5% increase in PSNR, 0.02% increase in SSIM, and 6% increase in information entropy. The algorithm in this paper has better haze removal effect compared to the DehazeNet algorithm.
Research on Grid-Connected Active Control Technology of Photovoltaic Power Generation System
Authors: Lu Guoqiang , Chen Chunmeng , Zhao Dongning , Zhang Jian
Published: January 2024

In this study, the mathematical model of photovoltaic inverter is established, and the principle and structure of two-stage photovoltaic power generation system are analyzed by using coordinate transformation and feedforward decoupling control methods. Under the premise of grid voltage vector orientation, based on the instantaneous power theory, the active power support and reactive power compensation control strategy of photovoltaic power generation is established. Finally, the simulation model is constructed for experimental analysis. The experimental results show that the control strategy can improve the stability and control effect of the output compared with the traditional strategy.
Exploring the Determinants of Semantic Internet of Things in Healthcare
Authors: Amara Fatima Zahra , Groppe Sven , Tiwari Sanju , Hemam Mounir , Djezzar Meriem
Published: January 2024

The Semantic Internet of Things (SIoT) is an Internet of Things (IoT) extension that uses semantic technologies to improve interoperability and understanding of data generated by connected devices. The SIoT can help in smart health by providing a common language and framework for exchanging and integrating health-related data from various sources. This has the potential to improve the accuracy and efficiency of health monitoring and decision-making, allowing for more personalized and effective health interventions. This chapter outlines the Semantic Internet of Things’ core concepts, specifies its focus areas, and lists its various requirements, difficulties, and uses. Through the presentation of a literature review on Semantic Internet of Things healthcare systems, including challenges, prospects, and current work, we highlight the best outcomes for semantics among IoT systems in healthcare, which can serve as a guideline for future research. The chapter also provides an overview of the use of semantic technologies in healthcare IoT, including new projects, frameworks, and specific ontologies that use semantic technologies to address semantic interoperability and heterogeneity issues.
The moderating effect of gender equality consciousness on environmental concern among males and females
Authors: Niu Long , Liu Meng , Fan Lijuan , E. Yiting
Published: January 2024

BACKGROUND: In the discourse on the heterogeneity of gender and environmental concern, gender equality awareness can further illuminate the complexity of gender and environmental concern across different cultural backgrounds. OBJECTIVE: Differing from prior research, our study focuses on exploring the relationship between different genders, gender equality awareness, and environmental concern, as well as investigating whether gender equality awareness has distinct effects on environmental concern among different genders. METHODS: Utilizing data from the 2021 China General Social Survey (CGSS), we conducted a theoretical investigation into gender differences in environmental concern. First, we used descriptive statistics to observe the differences between the samples. Secondly, in order to compare the influence of gender equality awareness on different genders, we conducted t-tests on gender subgroups. Subsequently, an Ordinary Least Squares (OLS) regression model was employed to examine the effects of gender and gender equality awareness on environmental issues. Finally, gender equality awareness was introduced as a moderating variable to explore its potential interaction with gender and investigate whether a moderation mechanism exists between gender and environmental concern. RESULTS: Findings indicate that 1) male respondents in China exhibit greater concern for the environment compared to female respondents; 2) higher levels of gender equality awareness are positively correlated with higher levels of environmental concern; 3) the influence of gender on environmental concern is moderated by the degree of gender equality awareness. Specifically, when gender equality awareness is strengthened, female respondents exhibit a higher degree of environmental concern. CONCLUSION: Enhancing gender equality awareness contributes to improving both social gender equality and equitable concern for the environment. The findings could provide insights for similar developing countries like China.
Investigating the Implementation of IMRaD Structure in Abstracts of Undergraduate Students' Theses
Authors: Pangesti Anggun , Ulfah Badriyah , Hartono Rudi
Published: January 2024

The IMRaD (Introduction, Methods, Results, and Discussion) structure is a crucial framework for organizing information in abstract writing, enabling clear and effective communication of research findings. Hence, this research aims to analyze the adherence of undergraduate students to the IMRaD structure in their thesis abstracts. The study focuses on students who graduated from a private university in 2019. A sample of 77 from 326 thesis abstracts was selected from the population using the simple random sampling technique. Data collection involved a document review technique. The result shows that among the 77 students, all abstracts included the Introduction section, indicating a high level of adherence. However, the Method and Results sections were found in the abstracts of only 70 students, suggesting a relatively lower compliance rate. Surprisingly, the Discussion section was present in the abstracts of only 31 students, representing the lowest adherence to the IMRaD structure. This research reveals a need for interventions to enhance undergraduate students' understanding and implementation of the IMRaD structure in their thesis abstracts. Therefore, lecturers and institutions should provide guidance and support to improve students' adherence to this important academic writing convention. By enhancing students' proficiency in applying the IMRaD structure, their thesis abstracts can become more effective and aligned with scholarly standards.
Student’s Cognitive Engagement in Teaching Vocabulary through Mnemonic Strategy
Authors: Setiawan Agung Gumelar , Hakim Putri Kamalia
Published: January 2024

This research aims to explore students' cognitive engagement and find out student’s perception towards learning vocabulary through mnemonic strategy. This research is a qualitative research with TPT cognitive engagement model as research design. The data of this research was gathered for four meetings by using observation, interview, and documentation. The data of observation were analyzed by interactive analysis supported by quadrant and tally analysis. The researcher take five participants from 10th grades from one of Islamic senior high school MAN 2 Kuningan. The findings showed that students have positive cognitive engagement where all of the participants get in the 4th quadrant except for one. Students Cognition level improved throughout the time until they have high cognition and high participation. Students also showed positive perceptions where they feel happy and fun during learning because they considered mnemonic strategy is like a game. Students also stated that their thinking skill is improved by the learning. Mnemonic strategy is also suggested to be implemented as a method in learning vocabulary in schools because it is effective.
TikTok and Vocabulary Learning: A Survey Study of Indonesian Students from English Departments
Authors: Rita Rita , Subekti Adaninggar Septi
Published: January 2024

The present study intends to investigate the attitudes of English department students on the use of TikTok for English vocabulary learning. The participants of this study were 116 Indonesian students from the English Language Education and English Letters Departments from 26 different universities in 10 provinces. This study employed an online survey as the method of collecting data. It found that generally, the participants reported highly positive attitudes towards using TikTok for learning vocabulary. However, they seemed to have ambivalent attitudes towards the effectiveness of using the platform for vocabulary learning, perhaps attributed to the default nature of TikTok as a social media rather than a learning media. Informed by the findings, the study suggested teachers incorporate TikTok in skill-based and vocabulary-building classes in the first semesters of English department curricula. Limitations and contributions of this study are stated along with the suggested direction of future relevant studies in the field.
A Grounded Theory of Indonesian English Teacher Educators’ Professional and Identity Development in a Blended Professional Development Community
Authors: Zakaria Faishal
Published: January 2024

This study aims to explore engagement that shows evidence of the professional development of Indonesian English language teacher educators converging in a blended learning community pseudonymously called the PL community. Further, the study seeks to explore how professional identities, which are also part of the professional development of teachers and teacher educators, are negotiated and claimed. The data was collected through (1) participant observation (participation in Facebook discussions), (2) interviews, and (3) documents (e.g., Facebook interactions/dialogs, workshop materials, or published papers). Grounded theory was used to analyze the data and to theorize emerging themes. Here, three layers of coding (open, axial, and selective) were done. Data analysis suggests that, by actively interacting in the PL community, members experience professional development activities, particularly in teaching, research, and publication. How professional identities were negotiated and claimed was also discussed.
BUILDING COMPANY IMAGE AND TRUST OF PROSPECTIVE HAJJ AND UMRAH PILGRIMS THROUGH PUBLIC RELATIONS STRATEGIES
Authors: Firdos Elbina , Dermawan Andy
Published: January 2024

Public relations strategy in an era of increasingly rapid business growth, especially in the field of Hajj and Umrah travel agencies, which creates new problems in the form of mushrooming fraud cases. These cases raise concerns for prospective pilgrims about choosing and trusting Hajj and Umrah travel agencies. In practice, PT Freshnel Kreasindo Perkasa Yogyakarta carries out strategies to continue to stand strong amidst business competition and the rise of fraud cases so that the company's image and consumer trust can continue to be built and maintained. This research aims to find out how public relations strategies are in building the company image and trust of prospective Hajj and Umrah pilgrims at PT Freshnel Kreasindo Perkasa Yogyakarta. The theory used in this research is the PENCILS strategy theory developed by Rosady Ruslan. The research method used is descriptive qualitative. The results of this research show that PT Freshnel Kreasindo Perkasa implements the PENCILS strategy in building the image and trust of prospective Hajj and Umrah pilgrims well, while the PENCILS strategy is in the form of publications, events, news, community involvement, information or image, lobbying and negotiating, and social responsibility
Comunidades escolares acogedoras: Recomendaciones para la inclusión de niñas, niños y adolescentes migrantes en las escuelas chilenas
Authors: Canales Urriola Jorge , González Márquez Katherine , Huenchulaf Lagos Daniela , Higuera Arias Ana Luz , Herrera Gutiérrez Macarena , Peña Vivallo María Francisca
Published: January 2024

El importante aumento del número de niñas y niños migrantes en las escuelas chilenas durante los últimos años ha impuesto un tremendo desafío para sus comunidades educativas: la inclusión. A partir de las voces de estudiantes migrantes, recogidas en un estudio realizado en escuelas municipales de la ciudad de Temuco, este libro identifica las problemáticas que para estos niños y niñas resultan ser las más relevantes en su relación con las comunidades escolares, desde donde se desprenden algunas recomendaciones para construir escuelas acogedoras. Estas recomendaciones asumen que la escuela, más allá de su vocación pedagógica y su quehacer didáctico, constituye un espacio social que debiera apuntar a la protección y cuidado de todos sus miembros, considerando sus vínculos afectivos. De esta forma, se proponen una serie de acciones que apuntan a la gestión de las relaciones sociales, a la convivencia escolar y a la valoración de la diversidad cultural.
Pengaruh Promosi dan Pengetahuan terhadap Keputusan Masyarakat Memilih Produk Tabungan Emas di PT. Pengadaian Syariah Cabang Langsa
Authors: Fitriani Sari , Puspita Sari Juli Dwina , Riza Muhammad
Published: January 2024

Penelitian ini bertujuan untuk mengetahui apakah promosi dan pengetahuan berpengaruh terhadap keputusan masyarakat memilih produk tabungan emas di PT. Pegadaian Syariah Cabang Langsa secara parsial dan simultan. Penelitian ini menggunakan metode kuantitatif dengan analisis regresi berganda. Populasi dalam penelitian ini adalah seluruh nasabah investasi emas yang ada di PT. Pegadaian Syariah Cabang Langsa pada tahun 2022 sebanyak 2.500 nasabah. Teknik pengambilan sampel menggunakan simpel random sampling dengan jumlah sampel adalah 96. Hasil penelitian menunjukkan secara parsial promosi berpengaruh terhadap keputusan menggunakan produk tabungan emas dengan nilai thitung dari promosi sebesar 35,005 > dari ttabel sebesar 1,661, dengan nilai signifikansi sebesar 0,000 < alpha 0,05. Pengetahuan memiliki pengaruh terhadap keputusan dalam menggunakan produk tabungan emas dengan nilai thitung dari pengetahuan sebesar 4,792 > dari ttabel sebesar 1,661, dengan nilai signifikansi 0,000 < alpha 0,05. Hal ini bermakna bahwa pengetahuan berpengaruh positif dan signifikan terhadap keputusan menggunakan tabungan emas. Hasil penelitian juga menunjukkan bahwa secata simultan (keseluruhan) promosi dan pengetahuan terhadap keputusan dapat dilihat dari hasil uji F, dengan nilai Fhitung= 212,942 > dari Ftabel 2,47 dengan nilai signifikansi = 0.000. Dengan demikian nilai signifikansi (0.000 < 0.05), maka terdapat pengaruh yang signifikan antara promosi (X1) dan pengetahuan (X2) terhadap keputusan (Y).
RESPON PERTUMBUHAN DAN PRODUKSI TANAMAN SORGUM (Sorghum bicolor L.) PADA LAHAN KERING MASAM TERHADAP PENGATURAN JARAK TANAM DAN PEMBERIAN PUPUK ORGANIK CAIR
Authors: Moelyohadi Yopie , Haitami A , Seprido  , Hadi Nariman , Alatas Andi , Jamalludin
Published: January 2024

Respon Pertumbuhan dan Produksi Tanaman Sorgum (Sorghum bicolor L.) Pada Lahan Kering masam Terhadap Pengaturan jarak Tanam dan Pemberian Pupuk organik cair. Penelitian ini bertujuan untuk mengetahui dan mendapatkan jenis pupuk organik cair dan jarak tanam tertentu yang memberikan pengaruh terbaik terhadap tanaman sorgum (Sorghum bicolor L.). Penelitian ini telah dilaksanakan di Jl. Sukarela, Kelurahan Kebun Bunga, Kec.Sukarami, KM 7 Palembang, Sumatera Selatan. Penelitian ini dilaksanakan dari bulan Maret sampai Juni 2023.  Penelitian ini  menggunakan metode eksperiment, yang disusun berdasarkan Rancangan Petak Terbagi (Split plot design) dengan 3 faktor perlakuan dan 3 ulangan..  Sebagai perlakuan petak adalah jarak tanam dansebagai anak petak pupuk organik cair.Dengan demikian terdapat 27 satuan percobaan. Adapun perlakuan yang dimaksud adalah sebagai berikut : Jarak Tanam J1 K1 =  70 cm x 10 cm, J2 =  70 cm x 20 cm, J3 =  70 cm x 30 cm. Jenis Pupuk Organik Cair (K), K1 = Limbah Tahu, K2 = Urine Sapi, K3 = Kotoran Ayam. Peubah yang diamati dalam penelitian ini adalah Tinggi Tanaman (cm), Jumlah Daun, Panjang Malai, Berat Malai, Berat Biji Pertanaman , Berat Biji Perpetak. Hasil penelitian menunjukan bahwa secara tabulasi kombinasi perlakuan jarak tanam 70 x 40 cm dengan pemberian pupuk organik cair kotoran ayam memberikan pengaruh tertinggi terhadap produksi sorgum sebesar 1,51 kg/petak (setara dengan 2,01 ton/ha).
E-COMMERCE WEBSITE DEVELOPMENT TRAINING AT AZZAM CELLPHONE SHOP IN TEMBILAHAN TO INCREASE SALES
Authors: Prasetyo Dwi Yuli , Bindas Asniati , Rasyid Ridha Muh. , Amin Muhammad , Thaher DS Syafrizal
Published: January 2024

Wabah covid-19 merupakan salah satu faktor yang berkontribusi terhadap pertumbuhan eksplosif pasar dalam transaksi jual beli dalam beberapa tahun terakhir. Belanja telah berubah menjadi model online sebagai akibat dari pembatasan sosial berskala besar (PSBB) yang diberlakukan untuk menghentikan virus covid-19. Di marketplace, orang-orang melakukan transaksi jual beli online. Marketplace adalah tempat di mana vendor dapat berkumpul dan melakukan bisnis virtual dengan klien. Karena popularitas belanja internet, pemilik toko fisik harus beralih ke pasar online. Penurunan omset adalah masalah yang dihadapi pedagang. Oleh karena itu, tujuan dari proyek pengabdian masyarakat ini adalah untuk membuat model sistem informasi e-commerce berbasis web. Pembuatan model sistem informasi e-commerce ini berpotensi untuk meningkatkan pendapatan, mempermudah pengolahan data, dan mempermudah transaksi yang dilakukan melalui media website e-commerce. BMC dan CMS merupakan metode yang digunakan dalam penelitian ini; model BMC digunakan untuk menilai dan membuat model bisnis untuk sistem yang ada. Sebuah website e-commerce dibuat sebagai bagian dari proyek pengabdian masyarakat ini yang akan bermanfaat bagi para pelaku UMKM, khususnya Toko Azzam Ponsel Tembilahan. Model e-commerce berbasis web dibuat dengan menggunakan CMS. Model e-commerce yang memenuhi kebutuhan pelaku UMKM merupakan hasil akhir dari penelitian ini. Platform e-commerce berbasis web yang dibangun telah melalui pengujian langsung kepada pengguna, dan hasilnya cukup menjanjikan.