bluuebunny/crossref_metadata_2025 huggingface dataset:
Penıngkatan Hasıl Belajar Model Project Based Learnıng Berbasıs Lapbook Kelas III SDN 1 Semplo
Authors: Suweni  , Dianasari  , Prabawati Nurhabibah
Published: January 2024

This research is based on a preliminary study obtained in the initial data on improving math learning outcomes which only reached 45%, so it is necessary to apply a project-based learning model based on lapbook media. This study aims to determine the improvement of student learning outcomes through a lapbook-based project-based learning model in third grade math lessons at SDN 1 Semplo. The type of research used is Classroom Action Research (PTK). The research subjects were third grade students of SD Negeri 1 Semplo with a total of 29 students. The data collection techniques used were observation, interviews, tests and documentation. The data analysis technique used was descriptive quantitative and qualitative. The project-based learning model which is a learning model that uses an activity, project as a medium that results in an increase in student learning outcomes, while the lapbook is a medium produced using project-based learning which can be a portfolio of student work and assignments. The increase in pre-cycle learning outcomes was 45% (13 students), increased in cycle I to 62% (18 students) and in cycle II increased to 86% (25 students). So, by applying the Lapbook-based Project Based Learning (PjBL) learning model can improve the learning outcomes of third grade Mathematics students of SDN 1 Semplo.
Pengembangan Bahan Ajar Etnomatematika Bernuansa Baju Pengantin Adat Betawi
Authors: Rizkyh Tamara , Desi Setiyadi  , Meilina
Published: January 2024

This research aims to develop an ethnomathematics teaching material with nuances of Betawi traditional wedding dresses. as a means to improve understanding of geometry for fourth grade students in elementary schools (SD). In the world of education which is increasingly growing and developing, education about culture is increasingly decreasing because it is being replaced by foreign culture. The research is based on the lack of relevant and contextual teaching materials in this material so that it has the aim of distributing ethnomathematics teaching material with nuances of traditional Betawi wedding dresses by applying flat shape material in elementary schools. The research respondents were 32 class IV students at SDN Petir 4 Tangerang City. This research uses the method, Research And Development (R and D), developing a 4-D model. The results of the resulting research and product development show that (1) Ethnomatics teaching materials with traditional Betawi wedding dress nuances can significantly improve students' understanding of geometry, (2) the validity of ethnomatics teaching materials is demonstrated by the results of the geometry material expert test and media expert test of 4.53 with a very worthy category
Implementasi Model Blended Learning pada Mahasiswa PGSD di Universitas Bina Nusantara
Authors: Rina Patriana Chairiyani  , Dasim Budimansyah  , Kama Abdul Hakam  , Yadi Ruyadi
Published: January 2024

Blended learning is a learning method that integrates face-to-face learning and online learning. This research aims to investigate the implementation of the blended learning model in Primary School Teacher Education (PGSD) students at Bina Nusantara University. By focusing on aspects of curriculum, technology, and student-teacher interaction, this research reveals the impact and effectiveness of this model on student learning experiences. This research used a qualitative approach with an open questionnaire method. Data was collected from teachers and students involved in the PGSD program. The research results show that the implementation of the blended learning model has provided significant benefits in enriching material, developing technology skills, and student engagement. However, the research results also highlight several challenges, including access to technology, lack of training for teachers, and effective time management. This research provides valuable insight into the successes and obstacles of implementing the blended learning model for PGSD students at Bina Nusantara University. This study provides recommendations for improving the quality of the blended learning model by considering the needs and characteristics of PGSD students. The results of this research can be a guide for higher education institutions in designing, implementing and improving blended learning methods to support quality education in the future.
Peran Kepala Sekolah, Guru Dan Siswa Dalam Menerapkan Pendidikan Karakter di MI Hidayatul Ulum Kisik
Authors: Fahimatul Anis  , Trapsila Siwi Hutami  , Prasetyo Adi Nugroho
Published: January 2024

One of the goals of Indonesian national education is to implement character education. Character education needs to be instilled and taught from an early age so that it becomes a habit that leads to the implementation of good educational values. The aim of this research is to analyze the role of school principals, teachers and students in implementing character education. This research method is qualitative research. The data sources for this research are school principals, teachers and students. The data collection technique uses observation and interviews. Meanwhile, data analysis uses data triangulation. In implementing character education at MI Hidayatul Ulum Kisik, the role of the principal, teachers and students has a very important role. The school principal has the main role in directing and leading the implementation of character education at the MI Hidayatul Ulum Kisik school. Teachers are the main agents in transmitting character values to students. Students have an active role in internalizing and applying character values in their lives. Therefore, the role of school principals, teachers and students has a very important role in supporting the implementation of character education in schools. The school principal plays a role in determining the school's vision which will later be used as a step in determining policy. The role of the teacher can be internalized through teaching and learning process activities in the classroom, including the value of honest character education that can be taught to students in answering questions given by the teacher who is teaching. In this way, students will be trained to behave in accordance with the values taught at school.
Pengaruh Kreatıvıtas Mengajar Guru Dan Mınat Belajar Terhadap Hasıl Belajar Sıswa Sekolah Dasar
Authors: D.C Karundeng Johanes , H.F Rorimpandey Widdy , M Krowin Martinus
Published: January 2024

ABSTRACT The low learning outcomes of elementary school students are the basis for evaluation creativity teachers in the learning process in the classroom. Apart from that, interest in learning is also an aspect that needs to be improved for students to achieve good learning outcomes. This research is quantitative research with a correlational type. The population of this study was the fifth-grade students at Posilangon State Elementary School, totaling 32 students. The research sample consisted of 32 students who were determined using techniques non probability sampling. The data collection techniques used were questionnaires and documentation. Test the validity of the instrument items using product moment, test the reliability of the instrument using alpha cronbach. The data analysis technique in this research uses descriptive statistical analysis, research prerequisite tests, and hypothesis testing. Prerequisite tests include normality tests, linearity tests, homogeneity and multicollinearity tests. Hypothesis testing uses simple linear regression tests and multiple linear regression tests. research results show that: 1) creativity Teacher teaching has a positive and significant influence on science learning outcomes with a variable contribution of 24.2%. 2) students' interest in learning has a positive and significant influence on science learning outcomes with a variable contribution of 99.5%. 3) creativity Teacher teaching and student interest in learning simultaneously have a positive and significant influence on science learning outcomes with a variable contribution of 44.5%.
Optımalısası Pembelajaran Teks Prosedur Kompleks Dalam Bahasa Indonesıa Melaluı Penerapan Project-Based Learnıng Dengan Medıa Audıovısual
Authors: Triandyah Dwi Sasi Wardani  , Fida Pangesti  , Sudjalil
Published: January 2024

ABSTRACT
This research is motivated by the implementation of the project-based learning model (PjBL) in the context of learning complex procedural texts at SMPN 2 Grati. This research aims to describe three main aspects, namely the application and impact of the PjBL model, the challenges faced, and students' responses to this learning model. This research used a qualitative descriptive approach with 35 class VIIA students as subjects. Data was collected through observation and interviews, documented in the form of documents and field notes. The process of data reduction, data presentation, and drawing conclusions was carried out to analyze the findings. The research results show that the PjBL model is implemented through six stages, including practicing basic questions, creating a project plan, preparing an implementation schedule, tracking student and project progress, testing results, and experience assessment. The main objective of the research reveals that the project-based learning model has a significant impact on collaboration and student learning outcomes, with the highest student average reaching 90 and the lowest 70. The proposed solution involves productive use of time, support from the homeroom teacher, and the use of informative media as an effort to overcome these difficulties. This research contributes to the practical understanding of the implementation of the PjBL model in the context of learning complex procedural texts at the secondary school level. The implications of these findings can be used as a guide for teachers and education stakeholders to increase the effectiveness and efficiency of learning in the classroom.
Integrasi Karakter Peduli Lingkungan Dalam Program Sekolah Adiwiyata di SDN Cipondoh 5
Authors: Fortuna Dewi , Muhammad Fauzan Muttaqin  , Pebrisa Amrina
Published: January 2024

This research aims to find out how the character of caring for the environment can be integrated into the Adiwiyata school program at SDN Cipondoh 5. This research uses a descriptive qualitative method, a case study type. Data collection uses observation; interviews with informant sources, the school principal, 2 teachers, 2 people in charge of the Adiwiyata program, and 5 students; and documentation studies. The data validity techniques used by researchers are technical triangulation and source triangulation. Data analysis techniques include data reduction, data presentation, and drawing conclusions. Based on the research results, it shows that the Adiwiyata school program can grow and improve the environmentally caring character of students at SDN Cipondoh 5 as they get used to it at school. Integration is carried out through the curriculum, development of environment-based learning tools, activities to bring your own food and drinking bottles, training to make biopore holes, clean Friday activities. The integration carried out by the school also collaborates with the school community, student parents, health centers and other related agencies. The obstacles faced are related to the lack of coordination between teachers and inadequate land in optimizing the adiwiata program. The implications are for schools in overcoming obstacles through integrating the Adiwiyata program in collaboration with environmental agencies so that they can regain the national level Adiwiyata school certificate award
Peran Media Digital dalam Bingkai Etnopedagogik sebagai Upaya Optimalisasi Pencapaian Kualitas Pendidikan Anak Usia Dini Masa Depan
Authors: Gandana Gilar , Nuraly Masum Aprily  , Aini Loita  , Rifki Ahmad Fauzi  , Chusna Arifah  , Risa Arosyidah
Published: January 2024

The digitalization program for early childhood education media in the era of the industrial revolution 4.0 is an opportunity as well as a challenge for educational inventors in the world. This opportunity can be used as an exploratory land for the development of science for educational media inventors. On the other hand, the challenges are related to the clash of value systems and consistency in strengthening the integration of the conservation function system with the educational innovation function. In this context, challenge does not mean weakness, but for scholars it is a form of motivational strength for the practical development of knowledge to be used as material for increasing self-capacity as a true scientist. Thus, the opportunities and challenges that arise in this study become a source of energy to overcome anxiety about the future of education. With regard to ethno-pedagogy which is the contextual realization of human imagination in terms of human values ​​and norms, it has a very important role in presenting every digital product that is created. So that the process of integrating conservation values ​​with educational innovation values ​​can show logical consequences for achieving the expected quality of early childhood education in the future.
PERAN TRANSFORMATIONAL LEADERSHIP TERHADAP SAFETY INITIATIVES DENGAN MEDIASI OLEH JOB SATISFACTION
Authors: Mende Christian Daniel , Herachwati Nuri , Pramesti Gebrina Ayu
Published: January 2024

Penelitian ini bertujuan untuk untuk menganalisis peran dari transformational leadership dan job satisfaction terhadap safety initiatives yang mana masih belum banyak diteliti pada konteks di rumah sakit apalagi di negara Asia. Metode kuantitatif dengan pendekatan cross sectional digunakan untuk melihat hasil empiris dari 200 responden pada Rumah Sakit tipe A di Surabaya. Teknik analisis data menggunakan structural equation modelling (SEM) dengan aplikasi SmartPLS (VB-SEM). Hasil yang ditemukan adalah adanya pengaruh langsung yang signifikan dan positif dari transformational leadership terhadap job satisfaction, dan job satisfaction terhadap safety initiatives. Pengaruh langsung yang tidak signifikan ditemukan pada transformational leadership terhadap safety initiatives dan dengan ini menunjukkan adanya mediasi full dari job satisfaction. Penelitian ini memberikan bukti empiris bahwa manajemen rumah sakit perlu terus memperbanyak dan melatih kepemimpinan yang transformasional di tempat kerja dan mencari cara-cara yang efektif untuk meningkatkan kepuasan kerja karena hal ini dapat berdampak pada tingkat keselamatan pasien.
Mit Sozialpolitik regieren
Authors: Knecht Alban
Published: January 2024

Wie kann die Arbeitsmarktpolitik Jugendliche beim Berufseinstieg unterstützen? Alban Knecht analysiert politische Diskurse und institutionelle Veränderungen der Beschäftigungsförderung benachteiligter Jugendlicher in Österreich vor dem Hintergrund der Ressourcentheorie. Er diskutiert die Einführung von Maßnahmen wie überbetrieblicher Lehre, Ausbildungsgarantie und Ausbildungspflicht und verdeutlicht dabei die Bedeutung sozialinvestiver, befähigungsorientierter, neoliberaler und rechtspopulistischer Leitbilder für die praktische Arbeit der Fachkräfte und die Jugendlichen.
Essentials for Developing an Educational Course in Artificial Intelligence in Cybersecurity for Managers
Authors: Velev Dimiter , Zlateva Plamena
Published: January 2024

In recent years, cybersecurity threats have become more sophisticated and they have the tendency to be hard to detect and prevent. This has led to a growing interest in the use of Artificial Intelligence (AI) in cybersecurity. However, the adoption of AI in cybersecurity also raises concerns about the risks and ethical implications associated with its use. The aim of the paper is to reveal the potential benefits and challenges of AI in cybersecurity, and also to propose an educational course in AI in Cybersecurity for managers, who should be aware of current developments in this sensitive field.
A Conceptual Framework for Solving Ethical Issues in Generative Artificial Intelligence
Authors: Zlateva Plamena , Steshina Liudmila , Petukhov Igor , Velev Dimiter
Published: January 2024

Generative Artificial Intelligence (AI) offers tremendous potential in various domains but also raises critical ethical concerns. Key issues include the potential for misuse in spreading misinformation, the perpetuation of biases present in training data, the implications for privacy and data rights. It is crucial for developers, regulators and the public to collaborate in establishing robust ethical frameworks. These frameworks should guide the development and deployment of generative models, ensuring responsible use that maximizes benefits while mitigating risks. In this context the transparency, accountability, and public engagement emerge as foundational principles for navigating the ethical landscape of Generative AI. The aim of the paper is to propose a conceptual framework for solving ethical issues in Generative AI, after exploring issues, challenges, and implications for finding a more accountable integration of the technology into society.
A Framework for Multi Institutional Collaboration for Pneumonia Screening by Means of Federated Learning
Authors: Alfiansyah Agung , Widiarti Helena , Reynaldo Vincentius , Widjaja Wilsen , Bobelin Laurent
Published: January 2024

This project addresses in reducing pneumonia-related mortality by involving a development of an automatic screening system that analyzes X-ray images and distributed image data storage. This objective is achieved through a collaborative approach, employing Federated Learning to establish a platform that fosters cooperation of the hospitals. Each of them contributes to the advancement of diagnostics by constructing local models from their respective data. These local models are then transmitted to a central server that aggregate models to create a comprehensive global model. This process ensures that the resulting detection model remains unbiased. Importantly, patient data security is upheld, as the central server stores only global models but not sensitive patient information. Project also introduces an innovative system for archiving medical image data for a multifaceted purpose: it archives, anonymizes, and secures image, while also curating a dataset necessary for training a Computer Assisted Diagnosis systems. We are underlining this work to pushing the boundaries of machine learning especially in term of healthcare domain by a strong emphasis on patient privacy and anonymity.
Design and Measurement Research on Main Bearing Structure of Optical Remote Sensor
Authors: Wang Qinglei , Liang Yanbo , Guo Rongguang , Wang Weizhi , Zhou Xiaohua
Published: January 2024

The main load-bearing structure determines the stability of the space optical remote sensor and is a key structure of the system. It has the characteristics of large structural size, multiple interfaces, high stability and light weight requirements. A main bearing structure composed of aluminum honeycomb composite sandwich panels was designed to meet the high stability requirements of space optical remote sensor. The space optical remote sensor is in an extremely complicated space orbit thermal environment when it works on orbit. In order to solve the problem about the thermal deformation measurement of the main bearing structure of optical remote sensor, a digital photogrammetry system is proposed. The test system is built and the test conditions are designed for test verification. The research results indicate that the stability of the main bearing structure meets the requirements. The accuracy of digital photogrammetry system can reach 0.05mm, which provides a technical basis for the reasonable prediction of optical remote sensor optical structure on-orbit thermal deformation.
Estimation of Drain-Induced Barrier Lowering Variation Due to Random Dopant Fluctuation Effect in Nanometer MOSFETs by Gamma Distribution
Authors: Lyu Weifeng , Han Ying , Zhang Caiyun , Wei Weijie , Chen Dengke
Published: January 2024

Drain-induced barrier lowering (DIBL) and its variations are regarded as significant challenges in nanometer semiconductor device and circuit analysis, design as well as fabrication. This paper investigates the statistical characteristic of DIBL due to random dopant fluctuation (RDF) effect in MOSFETs. The results exhibit that the DIBL variation is more suitably described by a gamma distribution than a Gaussian distribution, based on validation by Monte Carlo simulations. The average error and mean square error of a gamma approximation are –0.14987 and 0.42591, respectively, for the probability density function, and 0.13523 and 0.50584, respectively, for the cumulative density function, which are much smaller than the equivalent measures of a Gaussian approximation. Our study also reveals that DIBL dispersion due to RDF effect in nanometer MOSFET channels is asymmetric with a long tail, which should be described with higher moments such as skewness and kurtosis.
Application Method of CPS (CPS) in Demand Side of New Power System
Authors: Xiong Genxin , Cheng Siyuan , Shi Wenjuan , Li Yun , Ma Jing , Wang Tongle
Published: January 2024

The new power system optimizes the energy structure and energy flow mode of the existing power system. On the power demand side, it is manifested by the increase of power generation equipment such as distributed power generation and virtual power plants and the increase of power trading business. This change has a wide impact on the management mode of the power system demand side. By reviewing the development trend of demand side and the concept and model of CPS(CPS), this paper points out the great advantages of CPS(CPS) in solving the complex problems of power system demand side, mainly the interaction problems of equipment, people and cyber system of complex business system composed of multiple equipment, and puts forward the application scheme and basic idea of future CPS in power system demand side management.
Fast Display Technology of 3D Cloud Image of Large-Scale Finite Element Model Based on Lightweight Processing
Authors: Wang Xiao-Hui , Chang Liang , Xu Xiang-Yan , Nie Xiao-Hua
Published: January 2024

Aiming at the visualization of large-scale finite element mesh models in finite element refined modeling and analysis, the rapid formation and efficient blanking of three-dimensional mesh models, large-scale finite element result data organization and rapid display, and other aspects of the system have overcome the rapid display optimization technology of the super-large-scale finite element mesh model, and realized the rapid display of the mesh model with tens of millions of degrees of freedom. The engineering application verification shows the feasibility and practicability of the method.
Performance Analysis of the Optimal Multitone Jamming for a MFSK-FH System
Authors: Li Wenji , Zheng Zhong , Zheng Hanyu , Luan Shan , Guan Yundi
Published: January 2024

The analysis and evaluation of jamming becomes very important with the strain of spectrum resources and the intensification of electronic countermeasures. In order to deepen the understanding of the mechanism of multitone jamming (MTJ) on multiple frequency shift keying and frequency-hopping (MFSK-FH) system, as well as support the communication equipment to improve the anti-jamming ability, the optimal MTJ pattern for a MFSK-FH system is analyzed in this paper. Firstly, the incoherent demodulation receiving model of MFSK-FH system under MTJ is established. Then, the optimal jamming channel, optimal jamming phase and optimal jamming tone number are discussed in detail. Finally, the simulation results of bit error rate (BER) performance are given.
Research on Smuggling Crimes in Cross-Border E-Commerce Retail Import Mode Based on T-R Model
Authors: Wu Chenjie , Wang Yukai , Zhang Weizheng
Published: January 2024

With the development of cross-border e-commerce model and the rapid growth of import trade volume, endless methods are being employed to use the cross-border e-commerce retail import channels for committing smuggling crimes. The process of handling relevant data by the customs anti-smuggling department is often very difficult. Based on the intelligence thinking of three-lists matching, the present study investigates the nature of smuggling crimes by using cross-border e-commerce retail import channels, proposes the idea of identifying the nature of cases by matching orders, payment orders and logistics orders, and realizes the risk classification method by combining the principle of three-lists data and RFM model with cluster analysis. Further, by developing a T-R model, the intelligent risk classification, simplification of case nature identification and the visualization of data was realized, so as to improve the text processing ability and intelligence analysis ability of customs anti-smuggling department in investigating and handling relevant cases. Additionally, the results of the study can assist the customs anti-smuggling department to carry out the supervision and anti-smuggling work of cross-border e-commerce retail import.
Lightweight Radio Frequency Fingerprint Identification for LoRa
Authors: Li Zhongliang , He Chunlong , Liao Riqing , Zhang Chiya
Published: January 2024

Radio frequency fingerprinting is a key technology that plays an important role in enhancing the security of Internet-of-Things applications. In this paper, we present a new radio frequency fingerprinting system based on a novel feature extraction technique. We first convert the collected steady-state signals to grayscale images by byte without the need for any prior knowledge. Next, the collected data is fed into a lightweight neural network called MobileNet for training and classification. To evaluate the performance of the proposed system, we then conduct experiments with 10 Long Range (LoRa) devices and a general software radio receiver. Experimental results show that the proposed model outperforms some mainstream models. Moreover, we input mobile phone device data into our system. Experimental results demonstrate that our proposed model can achieve a significant classification accuracy of 99.23%.
Equivalent Circuit Simulation of Transverse Nonlinear Vibration of a Single-Axis Bogie
Authors: Hao Feng , Wang Zheng , Bao Zhen , Xu Ping , Lei Yuanpeng , Wang Sheng
Published: January 2024

Numerical simulation of non-linear vibration systems requires efficient programs and high performance computers as a means to improve computational efficiency. Based on the similarity between the mechanical system dynamics model and the simulated integral circuit, an equivalent circuit model of a 2-degree-of-freedom elastic single wheel pair with transverse non-linear vibration considering the wheel-rail gap is established, and the circuit model is simulated and analysed using Multisim. The results of the study show that the equivalent circuit simulation is consistent with the numerical simulation analysis results, except that the equivalent circuit simulation is fast and efficient overall, and the parameters can be modified in real time during the simulation run to dynamically observe the simulation analysis process. Equivalent circuit simulation provides a viable and efficient reference method for the study of non-linear system dynamics.
Short-Term Forecast of Ionospheric TEC Based on CSA-LSTM
Authors: Zhu Tong , Yuan Chao , Chen Xialan , Fang Zhibo
Published: January 2024

When satellite signals pass through the ionosphere, the total electron content (TEC) is the main physical quantity that causes errors in the ionosphere, Moreover, the short-term prediction of TEC is an important research object in the field of ionospheric monitoring, and the accurate calculation and short-term prediction of ionospheric TEC is one of the most important elements of space weather monitoring and early warning. In this paper, a new crow search algorithm (CSA) is proposed to improve the LSTM neural network by optimizing the initial weights and parameters of long and short-term memory neural network, and to forecast the TEC values in the future period. The experimental results show that in the prediction of the ionosphere at single station for 48 h in 2023 in the Chinese region, the forecast effect at mid-latitudes; the CSA-LSTM forecast model is 0.72 TECu smaller than the average root-mean-square error of the single LSTM forecast model, while the forecast effect at low latitudes; the CSA-LSTM forecast model is smaller than the average root-mean-square error of the single LSTM forecast model by 2.67 TECu, overall, the CSA-LSTM forecast model has higher forecast accuracy and better forecasts at lower latitudes.
The Development and Application Research of Grey Wolf Optimization Algorithm
Authors: Chai Jihua , Zhang Liyi , Liu Ting
Published: January 2024

The Grey Wolf Optimization (GWO) algorithm is an emerging swarm intelligence optimization technique known for its simplicity, minimal control parameters, fast convergence, and ease of implementation. This paper investigates the search mechanism and implementation process of the GWO algorithm, analyzing its shortcomings, including poor population diversity, slow convergence in later stages, and susceptibility to local optima. It provides an overview of improvement strategies for the Grey Wolf Optimization algorithm, encompassing enhancements to the population, parameters, search mechanism, and its integration with other optimization algorithms. Furthermore, the paper discusses the applications of the Grey Wolf Optimization algorithm in various domains.
Early diagnosis of Parkinson disorder based on spiral drawing image modalities using hybrid transfer learning architecture
Authors: Anisha C.D. , Arulanand N.
Published: January 2024

The Spiral Drawing Test (SDT) has become a prominent clinical marker for the early diagnosis of Parkinson’s Disorder (PD) by capturing tremor symptoms. The integration of AI algorithms into a PD diagnosis system has proven to be a breakthrough objective assessment that aids professionals in decision-making. However, there is a need for improvisation of the workflow architectures of AI models to optimize the diagnosis system by reducing the misdiagnosis rate. The proposed system presents PD prediction using a Spiral Drawing Test (SDT) image modality integrated with an Artificial Intelligence (AI) algorithm. The proposed study presents three hybrid workflow architectures formed by integrating three core layers: a data augmentation layer, Transfer Layer (TL)-based feature extraction layer, and Deep Learning (DL)-based classification layer. The results were analyzed by conducting 18 experiments based on the hyperparameter values and workflow architectures. The highest accuracy obtained by the proposed study is 98% for Hybrid Workflow Architecture II.
Denoising low SNR electromagnetic conducted emissions using an improved DnCNN based mode
Authors: You Xingye , Mao Jian , Liu Jingming , Huang Kai
Published: January 2024

Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data.
Visual communication design of web-based learning resources in the digital era
Authors: Yuan Songlin
Published: January 2024

Since the dawn of the digital web era, web-based learning resources have become more and more significant in the field of education. To a certain extent, the visual communication design of these resources influences how well students learn. In view of this, the study proposes a deep learning-based approach to visual communication design. Convolutional neural networks are introduced to automatically construct the visual communication interface, a recommendation algorithm is used to develop the system’s recommendation function, and machine translation is used to translate the language description text. The study method’s efficacy was evaluated. According to the experimental results, the research method’s runtime in a color environment was only about 37.7 seconds at 4k resolution; in a non-color environment, the method’s F1 value was 0.87 at a recommended list length of 35, which was higher than that of other methods; and when it came to the interface solutions in real terms, the research method produced 526 at 30 buttons. The aforementioned findings demonstrate that the suggested approach can successfully increase the visual communication’s design speed and performance in online learning materials and offer a suitable answer to the needs of real-world applications.
Two-sided matching decision method and application based on subject differences in interval intuitionistic fuzzy environment
Authors: Qiao Jian-min , Li Wo-yuan , Liu Lin
Published: January 2024

In this paper, a two-sided matching decision model based on interval-valued intuitionistic fuzzy environment is proposed to maximize the demand of individual differences. Firstly, the attribute feature table and weight matrix of both agents are constructed, and a formula for calculating the comprehensive advantage in the interval-valued intuitionistic fuzzy environment is given, namely the interval-valued intuitionistic fuzzy comprehensive advantage aggregation operator (IIFCAAO); Secondly, the interval-valued intuitionistic fuzzy decision matrix of both agents is calculated by using the comprehensive advantage aggregation operator, and the interval-valued intuitionistic fuzzy decision matrix is transformed into a score function matrix by using a score function formula; Thirdly, the two-sided matching model is established to maximize the score; Finally, the scientificity and practicability of the model are verified by an example of college students changing their majors.
Applying machine learning techniques in the form of ensemble and hybrid models to appraise hardness properties of high-performance concrete
Authors: Xu Yue , Afzal Mansour
Published: January 2024

Accurately estimating concrete mechanical parameters using artificial intelligence-based methods can save time and energy. Existing nonlinear relationships between concrete components have entered uncertainty in the estimation of hardness properties of the slump and compressive strength as one of the most important parameters in concrete design. Employing regular approaches to use AI models individually in estimating dependent variables has been adopted in many studies. Therefore, the current study has aimed to develop predictive models in two categories of ensemble and hybrid frameworks to predict the hardness properties of high-performance concrete (HPC). In this regard, models based on Support Vector Regression, Decision Tree, and AdaBoost Machine learning were coupled with a metaheuristic optimization algorithm Chaos game optimizer (CGO). Linking three predictive models as well as tuning their internal settings via optimization algorithm could generate various types of hybrid and ensemble models. By assessing the results of the proposed models for compressive strength, the performance of ADA-CGO hybrid models was calculated higher than the ensemble model of SVR-ADA-DT, with 1.22% and 166% percent difference in terms of R2 and RMSE, respectively. Also, for predicting Slump, other hybrid models appeared with weaker performance than the ensemble model, with an average difference of 40.66% in terms of the MAE index. Generally, using advanced types of individual models, including ensemble and hybrid, indicated boosted performance accompanied by low-cost modeling processes.
TR-YOLO: A pig detection network based on YOLO V5n by combining self attention mechanism and large convolutional kernel
Authors: Pu Shihua , Liu Zuohua
Published: January 2024

Under the highly valued environment of intelligent breeding, rapid and accurate detection of pigs in the breeding process can scientifically monitor the health of pigs and improve the welfare level of pigs. At present, the methods of live pig detection cannot complete the detection task in real time and accurately, so a pig detection model named TR-YOLO is proposed. Using cameras to collect data at the pig breeding site in Rongchang District, Chongqing City, LabelImg software is used to mark the position of pigs in the image, and data augmentation methods are used to expand the data samples, thus constructing a pig dataset. The lightweight YOLOv5n is selected as the baseline detection model. In order to complete the pig detection task more accurately, a C3DW module constructed by depth wise separable convolution with large convolution kernels is used to replace the C3 module in YOLOv5n, which enhances the receptive field of the whole detection model; a C3TR module constructed by Transformer structure is used to extract more refined global feature information. Contrast with the baseline model YOLOv5n, the new detection model does not increase additional computational load, and improves the accuracy of detection by 1.6 percentage points. Compared with other lightweight detection models, the new detection model has corresponding advantages in terms of parameter quantity, computational load, detection accuracy and so on. It can detect pigs in feeding more accurately while satisfying the real-time performance of target detection, providing an effective method for live monitoring and analysis of pigs at the production site.
Optimized multiple-attribute group decision-making in uncertainty employing TODIM and EDAS technique and application to product styling design quality evaluation
Authors: Shi Lin
Published: January 2024

With the improvement of the public’s aesthetic level, product appearance has become an important influencing factor for consumers to make purchasing decisions. Product styling design is based on this market demand, combining the aesthetic and functional aspects of the product to create a personalized product appearance, in order to better attract consumers, improve the competitiveness and added value of the product. Usually, product styling design involves multiple elements such as product form, color, proportion, etc. The quality evaluation of product styling design is a MAGDM problems. Recently, the TODIM and EDAS technique has been employed to manage MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the quality evaluation of product styling design. In this paper, the interval-valued Pythagorean fuzzy TODIM-EDAS (IVPF-TODIM-EDAS) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for quality evaluation of product styling design is employed to validate the proposed technique. The main contribution of this paper is managed: (1) the TODIM and EDAS technique was extended to IVPFSs; (2) Entropy technique is employed to manage the weight values under IVPFSs. (3) the IVPF-TODIM-EDAS technique is founded to manage the MAGDM under IVPFSs; (4) Algorithm analysis for quality evaluation of product styling design and comparison analysis are constructed based on one numerical example to verify the feasibility and effectiveness of the IVPF-TODIM-EDAS technique.
Parkinson’s Disease Is Predominantly an Environmental Disease
Authors: Dorsey E. Ray , Bloem Bastiaan R.
Published: January 2024

Parkinson’s disease is the world’s fastest growing brain disorder, and exposure to environmental toxicants is the principal reason. In this paper, we consider alternative, but unsatisfactory, explanations for its rise, including improved diagnostic skills, aging populations, and genetic causes. We then detail three environmental toxicants that are likely among the main causes of Parkinson’s disease— certain pesticides, the solvent trichloroethylene, and air pollution. All three environmental toxicants are ubiquitous, many affect mitochondrial functioning, and all can access humans via various routes, including inhalation and ingestion. We reach the hopeful conclusion that most of Parkinson’s disease is thus preventable and that we can help to create a world where Parkinson’s disease is increasingly rare.
Brain injury, medical progress, and the disability paradox: Towards an Americans with Abilities Act
Authors: Fins Joseph J. , Shulman Kaiulani S. , Wright Megan S. , Shapiro Zachary E.
Published: January 2024

It is helpful to think about the needs of patients with moderate to severe brain injury through the lens of disability law. However, there are limitations to current disability law that contribute to ongoing segregation and marginalization of individuals with severe brain injury. Indeed, one of the paradoxes of American jurisprudence is that more clear constitutional protections accrue to those who have definitively immutable conditions. Thus, as neuroscience brings new therapies to those with brain injury, they may become less protected by the constitutional elements of disability law because their conditions have changed and become mutable. This is the clinical progress that brain injury professionals all seek to achieve, but ironically these advances could potentially degrade the legal protections of patients who benefit from emerging treatments. In this paper, we will critically examine this paradox at the interface of medicine and the law and suggest that the Americans with Disabilities Act (ADA) could be nicely complemented by legislation we have named the Americans with Abilities Act (AWAA). Instead of focusing on disabilities that need protection, the AWAA seeks to sustain and foster newfound abilities made possible by the fruits of medicine and neuroscience.
Digital Twins, Digital Triplets, and eXplainable AI, in Precision Health
Authors: Talukder Asoke K. , Sarbadhikari Suptendra Nath , Selg Erwin , Haas Roland E.
Published: January 2024

Precision health is about preventing, predicting, and treating diseases precisely with the principles of the right care at the right time for the right patient. Precision health is expected to help increase health equity in general. Digital Twins and Digital Triplets can significantly help in meeting the goals of precision health. In this chapter we present digital twins and digital triplets and their roles in realizing precision health. Digital twin is the digital representation of a physical object in the digital space. In the Precision Health context, digital twins are indeed enablers of machine learning and knowledge mining. It is also useful for the in-silico simulation of a person’s phenotype (health states) and genotype (molecular states) to realize evidence-based medicine. Moreover, using probabilistic graph model and neuro-symbolic AI, digital twins will be useful in mitigating physician’s knowledge gaps or decision gaps to achieve value-based care. In contrast to Digital twins, Digital triplet is the semantic intelligence about the object. Digital triplets capture the semantics by placing semantically similar objects close together in the vector embedding space. This semantic intelligence helps cognition and discover hidden and unknown knowledge and their interrelationships to make accurate clinical and medical predictions. We group digital twins in three major categories, namely, Person Phenotype Digital Twin, Person Genotype Digital Twin, and Physicians’ Brain Digital Twin. Person phenotype digital twin relates to all observable properties of a person and a population. Person genotype digital twin helps understand the molecular properties of a person and a population. Physicians brain digital twin is the doctors’ brain with actionable biomedical knowledge in the virtual space.
Using teleconsultation for health aspects of computer-based workers in the new normal situation (post COVID-19) of the world
Authors: Gisour Bita Bagheri , Ghayour Najafabadi Mahboubeh , Zandi Hassan Gharayagh , Shaw Ina
Published: January 2024

BACKGROUND: The use of teleconsultation platforms enabling health professionals to provide advice especially in the new normal post COVID-19 situation has increased in both developing and developed countries. This teleconsultation has also expanded beyond that of post COVID-19 application to other purposes such as in the treatment of musculoskeletal pain. OBJECTIVE: The purpose of this commentary is to focus on presenting a feasible guide related to physical as well as psychological aspects in relation to computer based workers that could be taught to the computer-based workers using teleconsultation platforms. METHODS: A multi-criteria search was performed among available guidelines and approaches related to the physical and psychological aspects used for computer-based workers. RESULTS: A continuation of musculoskeletal pain creates a feedforward loop of pain and distress. Several conventional methods exist to manage musculoskeletal pain, however, the new normal situation following COVID-19 has rendered many of these moot, especially in computer based workers and with the increased reliance on teleconsultation platforms. CONCLUSIONS: During the COVID-19 pandemic, individuals were required to stay home due to varying levels of lockdown protocols and provide their usual services using online platforms both increasing sedentary behaviour in computer based workers and limiting the tools available to health professionals. Following in what is now the worldwide new normal post COVID-19 situation, individuals have adapted and accepted that they receive much of their necessary health advice using teleconsultation platforms as well.
The Effect of Massively Multiplayer Online Role-Playing Games (MMORPG) Vocabulary on Students’ Willingness to Communicate: A Narrative Inquiry
Authors: Salsabil Muhammad , Drajati Nur Arifah , Haryati Sri
Published: January 2024

Learning is not only done in the classroom but also outside the classroom through game media. This study focuses on the effect of vocabulary knowledge from massively multiplayer online role-playing games on students' willingness to communicate. The subjects of this research are three EFL learners who play MMORPG games. This study uses a biographical narrative inquiry method and collects data through semi-structured interviews and artefacts. EFL learners were assigned to share their experiences playing MMORPGs related to vocabulary knowledge and the desire to communicate. The data is analysed using thematic analysis. The findings highlight the positive impact of vocabulary knowledge on participants' self-confidence and pronunciation skills. A solid grasp of game-related vocabulary increases participants' self-confidence, making them more comfortable engaging in conversations and less afraid of making mistakes. It also improves pronunciation skills, as participants better understand word pronunciation through in-game interactions. Through collaboration among teachers, institutions, and parents, using MMORPGs as a learning tool is expected to provide maximum benefits for students in improving their English communication skills.