bluuebunny/crossref_metadata_2025 huggingface dataset:
The potential of intercultural foreign language education for the development of existential competence in senior schoolchildren
Authors: Burmistrova E. K.
Published: June 2024

The anthropocentrism of today’s educational paradigm prioritizes foreign language learners’ ideological self-determination. The latter actualizes the need to define the concept and the components of existential competence in foreign language education. A literature review in existential analysis helps to identify the meaningful models potentially suitable for developing existential competence through an examination of authentic cultural facts. The research paper defines the concept of existential competence, justifies the potential of intercultural foreign language education as a factor in existential competence development, and suggests a short lesson scenario as an educational technology for developing existential competence in senior school students.
The perspective of an effectual application of the ethnomethodological approach to teaching the Russian language in the polyethnic educational space of the Republic of Ghana
Authors: Tenkorang R.
Published: June 2024

This article is aimed at justifying the viability of the polyethnic Ghanaian audience for the implementation of the ethnomethodological approach to teaching Russian as a foreign language. Applying the analytical research method in reviewing literature, dedicated to the study of the ethnomethodological approach to teaching Russian as a foreign language, and literature, that describes the peculiarities of the Ghanaian contingency of students, thus the subject of this research work, the author recommends how the Russian language teacher can contribute to the effective implementation of an ethnomethodological approach to teaching the language in Ghana, regardless of the difficulties presented by the polyethnic Ghanaian landscape.
AN INVISIBLE TERROR OUTSIDE: THE ANXIETY OF UNCERTAINTY, PANIC AND ISOLATION IN RUMAAN ALAM’S LEAVE THE WORLD BEHIND
Authors: Bezrukov Andrii , Bohovyk Oksana , Budilova Oleksandra
Published: June 2024

Today humanity is going through a period of great upheavals and
rapid changes in every sphere of life, including the environment, that inevitably
lead to general destabilisation and disruptions. In the latest novel by Rumaan
Alam, apocalyptic Leave the World Behind (2020), a crisis appears to reshape
our closest bonds and forge new ones. This story of an invisible terror deals with
cataclysmic but mysterious events that shut down the communication networks
we over-rely on, and sees an almost overwhelming sense of uncertainty, panic
and increasing anxiety. Isolated in the remote holiday house with the Vermont
stone kitchen tops and night-lit swimming pool, a couple of New Yorkers and
their teenage children are looking forward to taking a rest from the routines of
city life when catastrophe strikes. In addition to the major theme of the threat of
human extinction, Leave the World Behind explores the relationship between
class and race and the complexities of parenthood and solitude during an
unspecified disaster. Those issues are included in the context of the global
problem of anthropogenic impacts on the environment. At the same time, Alam
demonstrates how habituation to the ongoing crises in the modern world,
including social-ecological transformations, affects the understanding of a
severe situation people are facing and ways to prevent it: they have increasing
tolerance for the absurd. The suspenseful, provocative and prescient book, Leave
the World Behind, captures the generalised panic of 2020, the year of a global
outbreak of coronavirus. As a kind of end-of-the-world fiction, the novel is full
of moments of exquisite recognition and reappraising of our attitudes the article
discusses.
Pemanfaatan Alat Peraga untuk Melatih Pemahaman Peserta Didik terhadap Operasi Aljabar
Authors: Sarumaha Yenny Anggreini , Kurniasih Ayu
Published: June 2024

Kenyataan di lapangan menunjukkan bahwa pemahaman peserta didik mengenai aljabar cukup rendah. Tujuan penelitian ini untuk mengetahui bagaimana manfaat alat peraga papan aljabar dalam menyelesaikan operasi hitung aljabar bagi siswa. Subyek penelitian terdiri dari lima peserta didik kelas VII salah satu SMP di DIY. Pengumpulan data melalui lembar observasi, lembar wawancara, lembar kerja peserta didik, dan tes akhir. Penelitian ini adalah penelitian kualitatif dengan menggunakan metode analisis deskriptif. Diperoleh hasil bahwa alat peraga papan aljabar merupakan salah satu alat yang bisa digunakan untuk meningkatkan pemahaman peserta didik terhadap operasi hitung aljabar. Alat peraga ini juga sebagian besar dijadikan sebagai alat kalkulasi untuk memudahkan peserta didik dalam menyelesaikan soal operasi hitung aljabar.
The reality in the field shows that students' understanding of algebra is quite low. The purpose of this research is to find out how the algebra board teaching aids are useful in completing algebraic arithmetic operations for students. The research subjects consisted of five class VII students from a junior high school in DIY. Data collection through observation sheets, interview sheets, student worksheets, and final tests. This research is qualitative research using the descriptive analysis method. The result is that the algebra board teaching aid is a tool that can be used to increase students' understanding of algebraic arithmetic operations. Most of these teaching aids are also used as calculation tools to make it easier for students to solve algebraic arithmetic operations.
Hubungan Pemahaman Konsep Matematis dengan Kemandirian Belajar Siswa pada Kelas VIII SMP
Authors: Yani Vanda Putri , Haryono Yulia , Lovia Lita
Published: June 2024

Kemampuan pemahaman konsep matematis merupakan kemampunan dasar yang harus dikuasai siswa. Penelitian ini menggunakan pendekatan korelasional yang bertujuan untuk melihat hubungan pemahaman konsep matematis dengan kemandirian belajar siswa pada kelas VIII di salah satu SMP di Lubuk Basung. Teknik pengambilan sampel menggunakan simple random sampling. Instrumen yang digunakan berupa tes pemahaman konsep matematis dan angket kemandirian belajar siswa. Teknik analisis data yang digunakan adalah analisis regresi linier sederhana. Berdasarkan hasil penelitian, dapat disimpulkan bahwa terdapat hubungan kemandirian belajar siswa dengan kemampuan pemahaman konsep matematis dengan arah hubungan positif sebesar 0,845, artinya semakin baik kemandirian belajar maka akan baik juga kemampuan pemahaman konsep matematis.
The ability to understand mathematical concepts is a basic ability that must be mastered by students. This study uses a correlational approach that aims to see the relationship between understanding mathematical concepts and student learning independence in class VIII one of the SMP in Lubuk Basung. The sampling technique uses simple random sampling. The instruments used were tests of understanding mathematical concepts and questionnaires for student learning independence. The data analysis technique used is simple linear regression analysis. Based on the results of the study, it can be concluded that there is a relationship between student learning independence and the ability to understand mathematical concepts with a positive relationship direction of 0.845, meaning that the better the independent learning, the better the ability to understand mathematical concepts.
Pembelajaran Team Quiz Berbantuan Quizizz Terhadap Motivasi dan Hasil Belajar Siswa
Authors: Salamah Dharis , Maryono
Published: June 2024

Rendahnya motivasi belajar matematika siswa menyebabkan hasil belajar matematika tidak maksimal. Diperlukan metode pembelajaran yang dinilai efektif untuk memberikan pengaruh positif terhadap motivasi dan hasil belajar matematika siswa, misalnya pembelajaran team quiz berbantuan aplikasi quizizz. Penelitian ini bertujuan mengetahui pengaruh pembelajaran team quiz berbantuan aplikasi quizizz terhadap motivasi belajar dan hasil belajar siswa. Penelitian quasi experiment ini berdesain Pretest-Posttest control group. Populasi penelitian yaitu seluruh siswa kelas VII salah satu MTs Negeri di Tulungagung, dengan sampel masing-masing 30 siswa kelas VII F sebagai kelas eksperimen dan kelas VII G sebagai kelas kontrol. Metode pengumpulan data dilakukan dengan tes dan kuesioner. Data dianalisis dengan uji-t dan uji manova. Hasil penelitian yaitu terdapat pengaruh metode pembelajaran team quiz berbantuan aplikasi quizizz terhadap motivasi dan hasil belajar matematika siswa. Metode pembelajaran team quiz berbantuan aplikasi quizizz dapat menjadi alternatif pilihan efektif untuk meningkatkan motivasi dan hasil belajar matematika siswa.
The low motivation to learn mathematics causes students' learning outcomes to be not optimal. A learning method is needed that is considered effective to have a positive influence on students' motivation and learning outcomes in mathematics, for example, team quiz learning with Quizizz application assistance. This study aims to determine the effect of team quiz learning with Quizizz application assistance on learning motivation and student learning outcomes. This quasi-experimental study has a pretest-posttest control group design. The research population was all students of class VII at one of the state MTs in Tulungagung, with a sample of 30 students from class VII F as the experimental class and class VII G as the control class. Methods of data collection are done by tests and questionnaires. Data were analyzed by t-test and manova test. The results of the study show the influence of team quiz learning with Quizizz application assistance on students' motivation and learning outcomes in mathematics. The team quiz learning with Quizizz application assistance can be an effective choice to increase student motivation and learning outcomes in mathematics.
Kemampuan Representasi Matematis Siswa Berbantuan Aplikasi Wingeom pada Masa Pembelajaran Tatap Muka Terbatas
Authors: Hidayat Fuad , Lestari Puji
Published: June 2024

Media pembelajaran yang digunakan belum sesuai dengan kebutuhan siswa khususnya pada materi Geometri Ruang sehingga kurang efektif untuk mengoptimalkan kemampuan representasi matematis siswa. Penelitian ini bertujuan untuk mengetahui kemampuan representasi matematis siswa kelas XII MIPA di salah satu MAN di Ciamis Tahun Pelajaran 2021/2022. Metode dalam penelitian ini yaitu penelitian deskriptif dengan pendekatan kualitatif. Instrumen penelitian berupa instrumen tes. Teknik analisis data terdiri dari reduksi data, penyajian data, dan penyimpulan. Hasil penelitian menunjukan persentase 12,50% siswa masuk kategori tinggi, 70,83% siswa pada kategori sedang, dan 16,67% siswa pada kategori rendah. Disimpulkan bahwa kemampuan representasi matematis siswa kelas XII MIPA di salah satu MAN di Darussalam Ciamis pada materi Geometri Ruang berada pada kategori sedang.
The learning media used are not following the needs of students, especially in the material of Spatial Geometry so it is less effective in optimizing students' mathematical representation abilities. This study aims to determine the mathematical representation abilities of class XII MIPA at one of MAN in Ciamis students in the 2021/2022 academic year. The method in this research is descriptive research with a qualitative approach. The research instrument is a test instrument. Data analysis techniques consist of data reduction, data presentation, and inference. The results showed that the percentage of 12.50% of students were in the high category, 70.83% of students were in the medium category, and 16.67% of students were in the low category. It was concluded that the mathematical representation ability of class XII MIPA students at one of MAN in Ciamis in Spatial Geometry material was in the medium category.
Sava Transformation Chronicle (A)
Authors: Filipović Nenad
Published: June 2024

This case study is part of a sequel of cases (from A to D) prepared to be used together in a course on Strategic Management (more in particular on topics related to Mergers and Acquisitions, Business Portfolio Management and Corporate Transformation). Due to the article length restrictions, parts B – D, as well as the case Teaching Note, will be published in a separate article. Please refer to the Teaching Note for more precise suggestions related to classroom use.
Sava Transformation Chronicle (D)
Authors: Filipović Nenad
Published: June 2024

This case study is part of a sequel of cases (from A to D) prepared to be used together in a course on Strategic Management (more in particular on topics related to Mergers and Acquisitions, Business Portfolio Management and Corporate Transformation). Due to the article length restrictions, parts B – D, as well as the case Teaching Note, will be published in a separate article. Please refer to the Teaching Note for more precise suggestions related to classroom use.
Subjection of Religious Institutes to the Holy See
Authors: Grzegorczyk Mateusz
Published: June 2024

The ecclesiastical legislator grants religious institutes just autonomy so that they can carry out their mission, but this autonomy is not unlimited. This article explains the essence of this autonomy of religious institutes – both of pontifical right and diocesan institutes – while indicating and discussing their dependence on the Holy See. This interdependence is realised in such aspects as the approval of religious authorities, changes to constitutions and statutes, issues concerning the management of goods or broadly interpreted formation. An analysis of this research problem must factor in ecclesiological, juridical and teleological aspects of the subjection of religious institutes to the Holy See.
Causal Association Between Type 2 Diabetes Mellitus and Alzheimer’s Disease: A Two-Sample Mendelian Randomization Study
Authors: Li Cong , Qian Haifeng , Feng Lina , Li Mingquan
Published: June 2024

Background:

There is now increasing evidence that type 2 diabetes mellitus (T2DM) is associated with Alzheimer’s disease (AD). However, it is unclear whether the two are causally related.

Objective:

To reveal the causal association between T2DM and AD, we performed a bidirectional Mendelian randomization (MR) analysis.

Methods:

Genetic instrumental variables were systematically screened, and inverse-variance weighting, MR—Egger regression, weighted median, simple mode, and weighted mode were applied to assess the pathogenic associations between the two diseases, and sensitivity analyses were used to further validate the robustness of the results.

Results:

The results of forward MR analysis with T2DM as the exposure were [OR = 0.998, 95% CI (0.975∼1.021), p = 0.857], and the results of reverse MR analysis with AD as the exposure were [OR = 0.966, 95% CI (0.934∼0.999), p = 0.043]. The results showed no significant association between T2DM and AD at the gene level ( p < 0.025). Sensitivity analyses were consistent with the results of the main analysis, confirming the robustness of the study.

Conclusions:

T2DM and AD may not be genetically causally associated.
Urban Train Timetable Optimization Based on Multi-Objective Optimization
Authors: Huang Guangjun , Ming Tiancheng , Wang Wutong , Yan Lixia , Kumari Saru
Published: June 2024

The urban rail transit industry in modern society is growing at an alarming rate, and in the rail transit of the network is the schedule is the basis of the train operation. Current research focuses on designing and optimizing train schedules to better meet passenger demands. In this paper, a multi-objective programming model is established on the basis of objective functions that are coupled and aimed at minimizing operating costs and passenger travel efficiency in order to optimize the train timetable, while considering meeting the demand of passenger flow. One objective function is kept in the original problem according to the characteristics of the model, and other objective functions are transformed into constraint conditions by adding restricted domains, thus turning them into single-objective programming models. Genetic algorithm is used to obtain results and train operation is simulated through the dynamic programming algorithm to carry out dynamic search. The CSMA/CD (Carrier Sense Multiple Access/Collision Detection) protocol is introduced to optimize the constraint conditions. The waiting time is transformed into the minimum tracking time interval by sending the carrier monitoring code. As such, the departure time data of large and small routes are calculated dynamically, and equal interval parallel operation diagrams are drawn. The calculation results indicate that the multi-objective optimization model improved by genetic algorithm can effectively solve practical cases, and its train timetable can highly match the spatiotemporal distribution of passenger flow demand and obtain satisfactory feasible solutions within a reasonable time.
Multi-Directional Traffic Flow Fusion Analysis and Modelling for Urban Road Networks Serving Intelligent Transportation Systems
Authors: Jiang Haiyan , Jin Ziming
Published: June 2024

This paper investigates the fusion analysis of complex multi-directional traffic flows in urban areas based on macroscopic fundamental diagrams with the objective of serving urban traffic management subsystems in Intelligent Transportation Systems (ITS) at rush hour or under special circumstances. Therefore, travel efficiency and regional vehicle accumulation are used as basic parameters to model the macroscopic fundamental diagram based on city-regional road network and traffic flow data. According to the directional influx traffic flow, the city area division is defined, thus a multi-directional traffic flow fusion analysis model can be established. The multi-directional traffic flow fusion analysis model is used to accurately describe the changing characteristics of multi-directional traffic flow and the complex integration between them under the influence of directional incoming traffic in urban road network areas. The analytical model also reveals that in addition to the imposition of boundary control management to limit the influx of traffic, the internal flow in the peripheral area also has a non-negligible impact on the overall traffic system operation.
Logistics Distribution Path Optimization and Application Based on Swarm Intelligence Optimization Algorithm
Authors: Miao Huachang
Published: June 2024

With the developing economy and consumption level, the market demand for agricultural products is getting bigger and bigger, and the requirements for the level of agricultural logistics and distribution are also getting higher and higher, which also brings great opportunities and challenges to China’s agricultural logistics and distribution. Swarm intelligence optimization algorithm includes ant colony genetic algorithm, so this paper is based on ant colony genetic algorithm for logistics distribution path optimization for application research. Agricultural products are perishable, and in the process of logistics distribution, the distributor has to meet the customer’s demand, but also to achieve the minimization of product distribution time and cost. This paper firstly constructs the distribution model through the basic constraints and objective functions of the model; then designs the vehicle path dynamic optimization algorithm and sets the initial population size so as to achieve the optimization of the distribution path; finally, by comparing and analysing the ant colony genetic algorithm and the improved ant colony genetic algorithm, the improved ant colony genetic algorithm is used to analyse different distribution modes.
Enhancing Urban Mobility: A Comprehensive Evaluation of Python’s Impact in Optimizing Intelligent Transportation Systems
Authors: Xiao Kai
Published: June 2024

This article aims to evaluate the application effect of Python in intelligent transportation system (ITS) data analysis. With the development of informatization and digitalization, ITS has become the core of urban traffic management. The article first introduces the concept of ITS, technical framework and principles of Python data analysis, and then analyzes in detail the application of Python in data collection, processing, traffic mode prediction and traffic safety analysis. The research adopted a case analysis method and selected the intelligent transportation system of a certain city for empirical research. Through tools such as Python’s Pandas library and SciPy library, the collected traffic data is processed and analyzed, and models such as linear regression and time series prediction are used to predict and optimize traffic flow and safety. The results show that data analysis using Python effectively improves traffic circulation, reduces accident rates, and optimizes the traffic signal system.
Traffic Flow Prediction Based on Decomposed Spatio-Temporal Fusion Graph Convolutional Network
Authors: Jiang Ting , Guo Min , Yang Liu , Ma Zheng , Liu Heng
Published: June 2024

At present, the global urban traffic system generally faces multiple challenges such as congestion problems and environmental loads. Reasonable traffic planning by predicting traffic flow is conducive to solving such problems. As for traffic flow forecasting, we need to establish a forecasting model first. The big data technology is used to process the previous historical traffic data as input, and the traffic data is found through the prediction model to predict the future traffic. The prediction of future traffic is used as the output of the model. In this paper, a new spatio-temporal fusion traffic prediction model based on input traffic signal decomposition is proposed, which is called Decomposed Spatial-Temporal Graph Convolutional Network (DSTGCN), in order to better capture global spatial correlation and time dependence. It captures the global spatial information through the diffusion graph convolution network in the spatial module, and then captures the time information through the designed time module. The spatial module and the time module are organically connected by the forgetting gate and the update gate to decompose and predict the input information in multi-level and two dimensions, and capture temporal and spatial correlation of traffic. Moreover, the residual learning framework is used to enhance the model simulation ability. Experiments on PEMS04 and PEMS08 data sets indicate that the proposed method is more excellent than ASTGCN and Graph WaveNet.
Exploring the Structural Optimization of T-Steel Structure Nodes and the Application of Smart Sensors in Smart Cities
Authors: Peng Yuhuai , Lei Jinsong
Published: June 2024

The utilization of semi-rigidly connected T-nodes in architectural engineering projects within smart cities presents advantages such as improved fatigue life of the structure, enhanced adaptability to the long-term development needs of the urban environment, and increased flexibility and construction convenience for smart city development. Additionally, this discussion delves into the application and arrangement of smart sensors on T-nodes, along with the implementation of a stress-strain cloud monitoring system for T-nodes. To comprehensively understand the impact of T-node flange plate thickness and bolt preload on the internal force of T-node bolts, a series of tensile tests and finite element simulation analyses are conducted on the T-node. These analyses aim to derive the load-strain curve of the T-node, providing valuable insights into the influence laws associated with these design parameters.
Research on Skeleton-Based Parametric Modeling for the BIM Design of Underground Power Cable Project
Authors: Zhou Liang , Jiang Liu , Yu Duanren , Shi Jian-yong
Published: June 2024

With the continuous progress of building informatization, BIM (Building Information Modeling) is more and more widely used in construction projects, but its application in infrastructure projects still needs to be studied. This study aims at the current problems of building information modeling in municipal engineering and proposes a new method of BIM modeling for infrastructure projects called parametric skeleton modeling (PSM). PSM is based on skeleton system, using parametric diven mechanism and modular assembling mechanism, on API (application programming interface) of Autodesk Revit, which greatly improves efficiency and the precision of modeling. At the same time, the reusability and adaptability of the model are improved .The study developed a set of high-efficient modeling tools and applied it on the BIM design for underground power cable project, providing the reference for the application of the BIM on the design for infrastructure projects. In the future, this method can also be extended to the modeling process of various infrastructure construction projects such as underground pipelines and bridges, which can improve the modeling efficiency, have a wide range of application and promotion value, and can bring positive social and economic benefits.
Courier Path Planning System Based on Automated Guided Vehicles
Authors: Lin Jin , He Weiting , Li Zeqin , Mo Xiangli
Published: June 2024

With the arrival of the digital era, the logistics industry pressure surge, the traditional logistics industry, high cost, low efficiency has been unable to meet the needs of modern logistics. There is an urgent need for intelligent logistics equipment to improve the efficiency of express operation. Automated Guided Vehicles (Automated Guided Vehicles, AGV) as one of the key equipments in the intelligent logistics system, plays an important role in reducing cost and improving efficiency. In this paper, this paper design a sorting system based on AGV path planning, adopting algorithms such as sliding filter, Kalman filter, breadth-first (BFS) search method, etc., and obtaining its own state through encoding motors, gyroscopes, ultrasonic arrays and feeding back to the upper computer ROS system, which obtains images to recognize the express delivery information through the camera, confirms its own position through machine vision and multi-sensor fusion, and makes automatic Navigation trolley AGV path planning to achieve automatic identification of courier information, automatic classification and transportation to the target location, and automatic return to the starting point. Through testing, it is proved that this design can complete the recognition of the target QR code and AGV path planning within the range of 1 meter.
Finite Time Stability Analysis Based on Dwell-Time Dependent Co-Positive Lyapunov Function
Authors: Zhang Zhao , Liu Lijuan
Published: June 2024

Traffic signal control has always been the core problem of urban traffic control, in order to propose a more efficient signal intersection control method, this paper abstracts the signal intersection queue vehicle dissipation and waiting problem into a class of finite-time stability (FTS) problems for switching positive systems containing unstable subsystems. A new dwell-time dependent co-positive Lyapunov function (DTDCLF) is introduced, which can effectively solve the analysis of dwell-time dependent problems and can more effectively use the switching rule to construct the Lyapunov function. Sufficient conditions for the FTS of switched positive systems containing unstable subsystems in discrete-time and continuous-time contexts are obtained by this method, respectively. Lastly, an illustrative numerical example is presented to showcase the efficiency of the method. The method outlined in this paper provides a more efficient solution to the signal timing problem at oversaturated intersections, allowing vehicles at oversaturated intersections to be evacuated more quickly, reducing traffic delays and improving intersection efficiency.
Planning and learning to perceive in partially unknown environments
Authors: Lamanna Leonardo
Published: June 2024

For applying symbolic planning, an agent acting in an environment needs to know its symbolic state, and an abstract model of the environment dynamics. However, in the real world, an agent has low-level perceptions of the environment (e.g. its position given by a GPS sensor), rather than symbolic observations representing its current state. Furthermore, in many real-world scenarios, it is not feasible to provide an agent with a complete and correct model of the environment, e.g., when the environment is (partially) unknown a priori. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. In this paper, we provide a general architecture of a planning, learning, and acting agent. Moreover, we propose solutions to the problems of automatically training a neural network to recognize object properties, learning the situations where such properties are better perceivable, and planning to get into such situations. We experimentally show the effectiveness of our approach in simulated and complex environments, and we empirically demonstrate the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.
Transfer contrast learning based on model-level data enhancement for cross-domain recommendation
Authors: Yu Chenyun , Feng Xiwei
Published: June 2024

A cross-domain recommendation system is an intelligent recommendation technology that integrates multiple fields or types of data. It can cross independent information islands, effectively integrate and complement data resources, and improve recommendation performance. This paper proposes a transfer contrast learning method based on model-level data enhancement for cross-domain recommendations. This method first obtains the initial embeddings of the two domains using item-based collaborative filtering, after which it enhances the transformer network with model-level data through contrastive learning to pre-train the source domain data. The pre-trained transformer network parameters are then transferred and fine-tuned before being applied to tasks on the target domain data. The information link from the source domain to the target domain is effectively constructed, and it has been proven to improve the accuracy and effectiveness of the target domain on real datasets.
Exploring the landscape of trustworthy artificial intelligence: Status and challenges
Authors: Mentzas Gregoris , Fikardos Mattheos , Lepenioti Katerina , Apostolou Dimitris
Published: June 2024

Artificial Intelligence (AI) has pervaded everyday life, reshaping the landscape of business, economy, and society through the alteration of interactions and connections among stakeholders and citizens. Nevertheless, the widespread adoption of AI presents significant risks and hurdles, sparking apprehension regarding the trustworthiness of AI systems by humans. Lately, numerous governmental entities have introduced regulations and principles aimed at fostering trustworthy AI systems, while companies, research institutions, and public sector organizations have released their own sets of principles and guidelines for ensuring ethical and trustworthy AI. Additionally, they have developed methods and software toolkits to aid in evaluating and improving the attributes of trustworthiness. The present paper aims to explore this evolution by analysing and supporting the trustworthiness of AI systems. We commence with an examination of the characteristics inherent in trustworthy AI, along with the corresponding principles and standards associated with them. We then examine the methods and tools that are available to designers and developers in their quest to operationalize trusted AI systems. Finally, we outline research challenges towards end-to-end engineering of trustworthy AI by-design.
Design and development of an efficient real-time moving object tracking system in a wireless-controlled automated vehicle
Authors: Chung Yao-Liang , Chung Hung-Yuan , Yang Zheng-Hua , Pichappan Pit
Published: June 2024

This study aimed to apply a microprocessor on a wireless automated vehicle to achieve real-time tracking of moving objects. The targets captured by the camera on the vehicle were first separated from their background through background subtraction. Next, morphological processing was performed to remove unnecessary information. An enhanced seeded region growing method was used to achieve image segmentation by labeling and segmenting the targets effectively, thus enhancing the accuracy and resolving the problem of object concealment. The corresponding red, green, and blue colors of each target were calculated through a color space, which was then converted into an enhanced luminance/chroma blue/chroma red (YUV) color space for color histogram modeling and storage, so as to increase the system’s tracking speed. The enhanced YUV colors also achieved accurate tracking in dark places. After inputting the next image, an enhanced agglomerative hierarchical clustering method was used to agglomerate and connect pixels with the same YUV for tracking. A proportional-integral-derivative controller controlled the motors on the camera lens and the vehicle so that the target could be tracked properly in real-time. The experimental results revealed that our proposed tracking method performed better than conventional tracking methods.
Difference in regional logistics development level in China based on entropy weight TOPSIS and Dagum models
Authors: Wang Pei
Published: June 2024

The high-quality development of the logistics industry, which is an essential and strategic industry supporting the national economic operation and a fundamental component of modern industrial system construction, is not only a key component of the high-quality development of the national economy but also the main driving force for the high-quality growth of the national economy. As the supporting industry of the national economy, the logistics industry will also face spatial disequilibrium during development. Therefore, to achieve the coordinated development of the logistics industry, the high-quality development and the spatial-temporal unbalanced development status of the logistics industry in each province must be figured out first. This research established a comprehensive evaluation system for the logistics industry development, which included 14 basic indexes based on the provincial-level panel data of 30 provinces in China during 2009–2020. Then, the regional logistics development level score in China was measured using the entropy weight TOPSIS method, and the differences in the regional logistics development level in China and the dynamic evolution law of their distribution were deeply explored through the Dagum Gini coefficient model. The research results revealed that the evaluation index system (14 basic indexes) for the regional logistics industry development level in China was relatively scientific and reasonable; the regional logistics industry development level in China was increasing year by year, showing a steady upward trend, and the imbalance in the eastern, central, and western regions regarding the regional logistics development was shrinking year by year; the average intergroup contribution rate was 36.33%, the intragroup contribution rate was 31.49%, and the contribution rate of intensity of trans variation was 32.19%, proving that the regional differences exerted a most extraordinary influence on the spatial differences in the regional logistics industry development level in China. The research results have important reference value for summarizing the meaning of high-quality logistics industry development, constructing the evaluation index system for logistics industry development, and exploring the reasons for the temporal and spatial differences in logistics industry development in China.
Evaluation of water resources carrying capacity in China based on entropy weight TOPSIS model
Authors: Lan Jiahao , Du Yunchao
Published: June 2024

Water resources carrying capacity refers to the ability of the water resources ecosystem to continuously carry the coordination relationship between human society and economy in the normal development process of a country or a region. Its self-sustaining ability, self-regulation and self-development potential often hinder sustainable development in water shortage areas. Research on water resources’ carrying capacity is a meaningful way to support regional water resources security and realize harmonious development of society, economy, and ecological environment. Correct assessment of water carrying capacity and response to government policies will contribute to improved water use and sustainable economic and social development. This study first sorts out the relevant questionnaires of water resources carrying capacity level evaluation, proposes the evaluation indicators of water resources carrying capacity level, collects and standardizes the required data, and calculates the weight of each evaluation index by entropy weight method. Then, it calculates the comprehensive evaluation value of China’s water resources carrying capacity from 2012 to 2022 in the TOPSIS model. The results show that the total afforestation area, total investment in environmental pollution control, and total industrial wastewater discharge are the third most important factors in improving the carrying capacity of water resources. From 2003 to 2010, China’s water resources carrying capacity improved year by year. From 2011 to 2021, China’s water resources carrying capacity remained stable year by year. The continuous adjustment of China’s industrial structure and strengthening environmental pollution control are inevitable measures to improve the carrying capacity of China’s water resources. This study provides a scientific basis for exploring the changing trend of China’s water resources carrying capacity and formulating reasonable optimal allocation of water resources. It also has great significance for promoting China’s water resources’ carrying capacity and sustainable development of the social economy and ecological environment.
Application of deep learning and statistical methods in predicting Taiwanese stock trends
Authors: Chung Yao-Liang , Wu Zheng-Lin , Pichappan Pit
Published: June 2024

Since its inception, the stock market has been a topic of considerable interest. Its variation and the complexity of integrating technology into the stock market have made it difficult for stock market trends to be fully understood. Various metrics and analytical approaches have been proposed in response to such changes, ranging from purely technical metrics to hardware upgrades. The widespread application of deep learning in the stock market, from basic metrics (opening price, closing price, highest price, lowest price, trading volume) to machine learning in sentiment analysis, further increases the possibility of increasing profits. Some front-end techniques, such as noise reduction through mathematical models, enhance the accuracy of deep learning models. However, few studies have centered on predicting long-term stock price changes. The traditional moving average (MA) cannot rapidly reflect drastic changes on its curve even though it can display trends; therefore, this study proposes an MA-based approach that improves the 200-day MA such that its delayed response to actual prices in real-time can be overcome. This deep learning model training was performed by combining 200-day MA data with two other types of MA data, thereby creating a new approach to metric analysis. The sample consisted of stocks of 13 Taiwanese companies with a high market cap: Taiwan Semiconductor Manufacturing Co., Ltd., MediaTek Inc., Chunghwa Telecom Co., Ltd., Fubon Financial Holding Co., Ltd., Cathay Financial Holding Co., Ltd., Nan Ya Plastics Corp., United Microelectronics Corp., Delta Electronics, Inc., CTBC Financial Holding Co., Ltd., Mega Financial Holding Co., Ltd., Formosa Chemicals & Fibre Corp., Hon Hai Precision Industry Co., Ltd., and Formosa Plastics Corp. Through multiple evaluation metrics, the experimental results revealed that the proposed model performed better in general than the traditional MA model for all stocks.
An improved genetic algorithm for robot path planning
Authors: Yao Zhifeng , Xu Ye
Published: June 2024

The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times.
Carbon emissions forecasting based on temporal graph transformer-based attentional neural network
Authors: Wu Xingping , Yuan Qiheng , Zhou Chunlei , Chen Xiang , Xuan Donghai , Song Jinwei
Published: June 2024

In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency “energy-electricity-carbon” integrated dynamic emission factor can be achieved. Through the spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making.
Effects of different intensity aerobic exercise on intestinal flora and gastrointestinal hormones in type 2 diabetic rats
Authors: Wang Xiaomei , Jing Huifeng , Guo Yuyue
Published: June 2024

To examine the impact and mechanisms of the aerobics exercise on gut flora and gastrointestinal hormones in type 2 diabetes rats. Methods Adult male SD rats aged 8 weeks were divided into 5 groups at random (n= 10): a peaceful comparison group (N), a diabetic comparison group (D), a diabetic low-intensity exercise group (LD), a diabetic middle-intensity exercise group (MD), and a diabetes highintensity exercise group. (HD). The rat groups LD, MD, and HD performed aerobic exercise five times per week for a total of 6 weeks and compared general condition, blood glucose, blood lipids, insulin, leptin, gastrin (GAS) and motilin ( MOT), gastrointestinal motility, and intestinal flora. Results The levels of insulin, leptin, total cholesterol, triglycerides, E. coli, LDL-cholesterol, LDL-cholesterol, insulin, and gastric residual rate were significantly different from group N. Conclusion: Type 2 diabetic rats have gut flora imbalance and gastrointestinal hormone changes. Aerobic exercise with different intensity can alleviate gastrointestinal hormone imbalance and intestinal flora imbalance in type 2 diabetes rats, and the impact of moderate intensity exercise is the strongest.
Digital reconstruction of substation equipment and facility layout via LiDAR point cloud registration
Authors: Wang Fei , Fan Zikai , Miao Yun , Ren Jiayi , Luo Yuchao
Published: June 2024

Generating as-built Building information models (BIMs) is promising in power substation construction projects because they can reflect the actual conditions of facilities. However, traditional manual-designed BIMs are different from real-world scenarios due to reality gaps. In this paper, we present a new method of reconstructing the layout of power equipment and facilities in substations using LIDAR point clouds. The proposed method extracts electric equipment and facilities via object segmentation and model retrieval. In particular, we investigate PFH, FPFH and SHOT descriptors for the 3D-SIFT keypoints in the 3D shape retrieval of complex electric equipment and facilities. After the best-match model is retrieved from a model library, the layout of typical electric equipment and facilities is reconstructed by aligning the model to the scene point cloud via point cloud registration. Experimental results validate the effectiveness of the proposed method. The proposed method enhances the efficiency of generating 3D models of power substations.
Intelligent learning methods for improving student performance through virtual reality technology
Authors: Zhao Wei , Qi Jian
Published: June 2024

With the rapid development of information technology, the field of education is undergoing a profound change, in which intelligent hybrid learning and virtual reality technology are increasingly valued. This study proposes an intelligent hybrid learning method based on virtual reality for student performance improvement. This paper reveals the limitations of traditional learning methods in meeting the needs of modern education, and expounds the theoretical basis of intelligent hybrid learning and virtual reality technology. This paper collects and processes a large amount of learning data, based on which a new model of student learning performance prediction is established. The verification results of the model show that the model in this study has excellent performance in predicting students’ learning performance. This paper gives some suggestions for future educational practice and research. In general, this study provides a new learning method for the field of education and has important reference value for education reform and teaching practice.
Analysis on the motivation of financial investment and the influence of financial risk of listed enterprises: A case study of A-share manufacturing industry
Authors: Peng Yunxiang , Tian Guixian
Published: June 2024

With the deepening of enterprise financialization, the trend of “moving away from reality to emptiness” has increased the difficulty of financial management in the manufacturing industry. This paper selects the data of A-share main board listed companies from 2012 to 2021 to study the motivation of financial investment in the manufacturing industry and its impact on financial risk. The research results show that the main motivation of listed companies’ financial investment in the manufacturing industry is “substitution” motivation. With the purpose of maximizing profits, the excessive allocation of monetary assets, especially long-term financial assets, increases financial risks of enterprises. Furthermore, the financial risk caused by the financial investment of state-owned enterprises is greater.