bluuebunny/crossref_metadata_2025 huggingface dataset:
PELATIHAN MENGAJAR DENGAN METODE ROLE PLAY BERBASIS PSIKODRAMA KEPADA TENAGA PENDIDIK SEKOLAH DASAR SWASTA PELOPOR DURI
Authors: Sutawan Komang , Widiyanto Widiyanto , Susanto Susanto , Priono Priono , Sumarwan Edi , Purwandoko Edi
Published: August 2024

Tujuan utama daripada kegiatan pengabdian masyarakat ini adalah untuk meningkatkan kemampuan mengajar dengan Metode Role Playing Berbasis Psikodrama Kepada Tenaga Pendidik Sekolah Dasar Swasta Pelopor Duri. Pemberian pelatihan secara virtual mengenai cara menjadi pendidik yang menggunakan psikodrama yang berlandaskan pada kegiatan eksplorasi tindakan. Metode yang digunakan dalam kegiatan pengabdian kepada masyarakat ini yakni melalui pendampingan menulis naskah drama dan praktik atau mendemonstrasikan metode role play. Hasilnya, tenaga pendidik mampu membuat naskah drama dan mampu menerapkan Metode Role Play Berbasis Psikodrama dalam kegiatan belajar mengajar, metode role play menjadi salah satu alternatif metode mengajar.
Pengenalan Meditasi Pelangi untuk Anak-Anak Sekolah Minggu di Vihara Pubba Mangala Arama Pekanbaru
Authors: Angela Shelfia , Surya Julia
Published: August 2024

Kegiatan  pengabdian  kepada  masyarakat  ini  bertujuan  untuk memperkenalkan meditasi pelangi ke anak-anak Sekolah Minggu yang berjumlah sebanyak 12 siswa di Vihara Pubba Mangala Arama Pekanbaru. Meditasi pelangi membuat anak-anak dapat bernapas sambil membayangkan berbagai warna pada tubuhnya. Meditasi ini berdurasi sekitar 15-30 menit. Mengenalkan meditasi kepada siswa merupakan hal yang sangat positif. Guru membimbing anak belajar fokus atau menenangkan pikiran, melatih kesadaran dan perhatian pada ketujuh warna pelangi. Meditasi pelangi berdampak pada perkembangan cinta kasih dan kasih sayang anak-anak kepada semua orang. Hasil kegiatan pengabdian menunjukkan bahwa anak-anak memahami pentingnya rasa cinta dan kasih sayang dan kepedulian terhadap diri sendiri, keluarga, guru, teman, orang yang dikenal maupun yang tidak dikenal. Meditasi pelangi sangat menarik dan bermanfaat bagi anak-anak. Anak-anak lebih terbimbing dan tidak bosan untuk melakukan meditasi.
Social Motives Towards Illegal Immigration from the Perspective of Graduate Students from Jordanian Universities
Authors: Banisaeed Rihab Ahmad
Published: August 2024

The study aimed to identify the social motives towards illegal immigration from the point of view of graduate students from Jordanian universities. This study relied on the descriptive approach, where the questionnaire was used as a tool for the study, which was applied to (748) graduates of various Jordanian universities during the summer semester of the academic year (2023/2024). The results of the study concluded that the social motives for illegal immigration were moderate, with an arithmetic mean of (3.27) and a standard deviation of (0.63).
The Influence of the Nitaqat Program and the Saudi Nationality Decree on the Intention to Stay of Indonesian Professional Workers in Saudi Arabia Mediated by Remuneration and Moderated by the Expatriate Residency System in Saudi Arabia
Authors: Fadil Dilal Adlin , Sudibjo Niko , Meilani Yohana
Published: August 2024

This study examines the influence of the Nitaqat Program and the Saudi Nationality Decree on the decision of Indonesian professional workers to remain working (Intention to Stay) in Saudi Arabia, by considering the mediating role of remuneration and moderation of the expatriate residency system. This study will be conducted using quantitative methods. The results of this study found that the Nitaqat Program has a significant negative effect on Intention to Stay (path coefficient: -0.256, t-statistic: 3.72, p-value: 0.000). In contrast, the Saudi Nationality's Decree has a significant positive effect (path coefficient: 0.354, t-statistic: 4.072, p-value: 0.000). Nitaqat Program has no significant effect on remuneration (path coefficient: -0.033, t-statistic: 0.52, p-value: 0.603), while Saudi Nationality's Decree has a significant positive effect (path coefficient: 0.659, t-statistic: 12.823, p-value: 0.000). Competitive remuneration has a significant positive effect on Intention to Stay (path coefficient: 0.198, t-statistic: 2.456, p-value: 0.014). Remuneration does not mediate the effect of the Nitaqat Program on Intention to Stay (path coefficient mediation: -0.006, t-statistic: 0.492, p-value: 0.623), but mediates some of the effects of Saudi Nationality's Decree (path coefficient mediation: 0.130, t-statistic: 2.441, p-value: 0.015). The expatriate residency system has a significant moderating effect on the relationship between remuneration and Intention to Stay (moderation path coefficient: -0.148, t-statistic: 2.056, p-value: 0.040).
Seeing Employee Performance Through the Influence of Leadership Behavior and Work Environment with Mediation of Public Service Motivation in the Public Service Regional Apparatus Organization in the City of Kendari
Authors: Umar Nahwa , . Samdin , . Nasrul , . Sinarwaty
Published: August 2024

Public service bureaucracy is still attached to the negative stigma of being slow and inefficient because it is not performance-oriented. This study examined aspects of employee performance in the public service sector. This research aims to determine and analyze the role of leadership behavior and the work environment in influencing employee performance and to examine the role of public service motivation as an intermediary for improving performance. This research was conducted at the Kendari City Government by focusing on public service agencies and taking employees as research respondents. In this study, respondents were recruited from three public service agencies with high community service intensity. A total of 130 employees were selected as respondents. Data collected by the survey were processed using partial least squares analysis. Based on the results of the analysis, we found that the work environment plays an important role in increasing the public service motivation and service performance provided by employees. Leadership behavior was also found to have a significant influence on public servant motivation and resulting performance. On the other hand, public service motivation apart from being able to act as a good mediator for the work environment, also has a strong influence on the performance they produce. The findings of this research have an impact on the performance improvements that can be achieved through the aspects discussed.
Strategies to Improve the Competitiveness of Local Agricultural Products Through Product Certification and Standardization
Authors: Ningsih Gumoyo Mumpuni , Rasyid Harun , Ningsih Natali , Pujotomo Darminto , Suseno Gijanto Purbo
Published: August 2024

In today's ever-evolving era, competition in the agricultural sector is getting tougher. Local agricultural products face great challenges in competing with imported products that are often considered to have better quality and wider market access. To overcome this challenge, there needs to be a serious effort to improve the competitiveness of local agricultural products. This study aims to analyze strategies to improve the competitiveness of local agricultural products through product certification and standardization. The method used is descriptive qualitative method with data collection techniques in the form of literature study. Data analysis was carried out in three steps, namely filtering data, presenting data, and making conclusions. The results showed that certification and standardization can improve the competitiveness of local agricultural products by convincing consumers about product quality. Some strategies that can be carried out by the local agricultural sector include implementing the Indonesian National Standard (SNI), halal certification, obtaining Organic Product Certification, meeting international standards, and promoting the certification to consumers. Based on the findings, the benefits of certification and standardization are very positive as an alternative to improve the competitiveness of local agricultural products.
The Role of Vocational Education Towards a Golden Indonesia 2045: Policies and Implementation?
Authors: Muharam Riki Satia , Afrilia Ufa Anita
Published: August 2024

The main factor for the success of development in a country is the availability of quality human resources (HR). Various developed countries in the world show how the strength of human resources has proven to be the driving force of the nation's economy. In 2015, it was recorded that Indonesia had 55 million skilled workers (ADB, 2015). This is a challenge for the education and training sector. This challenge is tried to be answered by the education sector by presenting an educational concept where practical elements in the learning process are carried out more than theoretical elements. Education that prioritizes the application of science rather than the academic side is what is then known as vocational education. Vocational education plays a crucial role in preparing the young generation who are competent and ready to face future challenges. This article discusses the Role of Vocational Education towards a Golden Indonesia 2045.
Strategic Communciation of Regional Information Commission in Communciating Implementation of Public Information Disclosure: Case Study in West Java Province, Central Java, West Kalimantan, and West Nusa Tenggara
Authors: Faisal Ijang , Sumartias Suwandi , Venus Anter , Sjoraida Diah Fatma
Published: August 2024

In globalization era, open government and good governance contribute greatly to nation development, especially to foster public participation. In Indonesia, implementation of open government and good governance guaranteed by Laws Number 14 of 2008 about Public Information Disclosure (UU KIP) which also mandates establishment of an independent and independent institution to implement law, there are Information Commission, both central and regional levels. Every years, Central Information Commission organize monitoring & evaluation of compliance level of Public Bodies with UU KIP, including provincial government. The highest compliance rating is in Informative Provincial Government category. This achievement is inseparable from role of Regional Information Commission in communicating UU KIP to stakeholders, so that when Provincial Government Informative, Regional Information Commission will also considered successful. How is strategic communication which is coducted by Regional Information Commission in communicating information disclosure to be focus of this research with use case study qualitative method on four Provincial Commissions whose Provincial Governments in the last three years (2018-2020) were categorized as informative, there are Information Commission of West Java, Central Java, West Kalimantan, and West Nusa Tenggara. The results of the research show: The factors behind the Regional Information Commission's strategic communication are the low level of awareness and understanding of the public and public bodies in the implementation of UU KIP; Optimizing the human resources of the Information Commission internally, externally and collaboratively; The clarity and consistency of the Regional Information Commission's messages focus on content that is relevant to UU KIP.
Language and Culture as Social Capital in Local Leadership
Authors: Hoerudin Cecep Wahyu
Published: August 2024

Globalization has affected many aspects of life, including local languages and cultures, which are often threatened by the dominance of global culture. In this context, local languages and cultures are key in strengthening local leadership and maintaining community identity. This challenge requires a deep understanding of how cultural elements influence leadership and how strategies can be implemented to preserve it. This study aims to explore the influence of language and culture on local leadership, as well as the challenges and opportunities in preserving it in the era of globalization. A qualitative approach was used to collect data from various sources such as previous studies, which were then analyzed to identify relevant themes and patterns. The results showed that social ties built through language and culture strengthen the relationship between leaders and communities, increasing trust and support. Leader strategies that combine innovation with traditional values can create sustainable development without sacrificing cultural heritage. Education and media play a crucial role in promoting and preserving local languages and cultures. This study recommends a holistic approach that integrates technology with tradition to ensure cultural preservation amidst globalization. The use of social capital based on local culture can also strengthen community participation and empowerment in the leadership process.
Revolutionizing Arabic Learning: Education Management Program Students' Perceptions of Utilizing Zoom Premium Amid the Evolution of Hybrid Learning
Authors: . Raswan , Qodri Muhammad , Mudhofir Ihwan , Sastradiharja EE. Junaedi , Susiawati Wati
Published: August 2024

Adapting and innovating is important for lecturers and students in pre- and post-pandemic conditions. Hybrid learning is emerging as a major alternative, with efforts to ensure its success equals or surpasses traditional methods. This study uses Zoom Premium to examine the process and outcomes of learning Arabic in the Education Management Study Program, Faculty of Tabiya and Teacher Training (FITK) at the State Islamic University of Indonesia. With a qualitative approach, data was collected through surveys and written interviews, involving 110 students who responded via Google Forms. The findings show that student motivation is quite high, but the effectiveness of online learning is still inferior to traditional methods, while student learning outcomes are very good. The main problem lies in the less-than-optimal signal conditions. Recommendations include leveraging student motivation with challenging assignments, increasing Zoom Premium innovation, and limiting in-person online sessions for a better learning experience.
Social Responsibility of the Engineer grounded in the Critical Theory of Technology
Authors: Salazar Johny Antonio Álvarez , Palmeth Luis Humberto Martínez , Marín Juan Gonzalo Ardila
Published: August 2024

Technological advancement brings with it ethical challenges and unforeseen consequences, making it essential for engineers to evaluate the human and social impacts of their creations. This research sought to answer how the Critical Theory of Technology (CTT) can guide engineers towards a more inclusive and socially responsible technological design, questioning for whom it is designed and the resulting power distributions. This study managed to determine the contribution that CTT makes to Social Responsibility (SR) in engineering. Using qualitative analysis and Atlas.ti software, 28 documents were reviewed, applying inductive coding for CTT and deductive for SR. Key elements were identified and correlated. The research highlights which elements of CTT have greater or lesser relevance in SR during technological design. Key subcategories such as critical thinking skills and instrumentalization in CTT, and SIS competencies, morality, and innovation in SR were identified. CTT emphasizes the importance of individual criticism, social interest, and ethics, while SR focuses on self-management and moral awareness. Both categories contribute to responsible technological design and positive social impact. Elements of CTT such as social interest and the democratization of technology are vital for SR, promoting engineering solutions with a human and ethical focus. This research highlights how CTT can improve engineering practices through more inclusive and sustainable technology.
Microbiological analysis of wound content in patients with type 2 diabetes mellitus with diabetic foot syndrome
Authors: Volch Iryna , Mykhailyshyn Halyna , Pokotylo Olena , Hetman Uliana , Bukata Volodymyr
Published: August 2024

An important component of the comprehensive conservative treatment of diabetic ulcers is antibacterial therapy, as without timely and correct treatment, patients may develop toxic shock syndrome, leading to multiple organ failure. The aim of the study was to examine the species variety of the wound content in diabetic foot ulcers in patients with type 2 diabetes and to determine the susceptibility of the isolated microorganisms to antimicrobial drugs. Given the results of the bacteriological examination of the wound content, the most frequently isolated microorganisms were: Staphylococcus aureus, Staphylococcus haemolyticus, Klebsiella spp., Pseudomonas aeruginosa, Corynebacterium spp., Escherichia coli, Proteus spp., and fungi of the genus Candida spp. The study not only investigated the microbial variety in diabetic foot syndrome but also established the effectiveness of applying antibacterial agents to specific isolated pathogens. The most effective antibiotics were tigecycline and vancomycin, with the susceptibility of the specified microorganisms ranging from 89.6% to 100.0%, respectively. The susceptibility of S. aureus and S. haemolyticus to amikacin was 75.9% and 62.1%, respectively. P. aeruginosa was susceptible to only 3 out of the 12 antibiotics included in the study, namely: amikacin, vancomycin, and ciprofloxacin. Klebsiella spp. showed moderate susceptibility (45.5%) only to doxycycline. Corynebacterium spp. was resistant to most of the studied antimicrobial agents, and only to amikacin, tigecycline, vancomycin, and chloramphenicol, the susceptibility level was within 50.0-100.0%. E. coli demonstrated high susceptibility (100.0%) to such antimicrobial drugs as ciprofloxacin, ceftazidime, chloramphenicol, amikacin, azithromycin, tigecycline, and ertapenem. Proteus spp. was susceptible (100.0%) to chloramphenicol, amikacin, azithromycin, tigecycline, and ertapenem. The identification of the microbial diversity of the wound contents of a diabetic ulcer and a broad antibiotic profile will allow the optimization of antibiotic therapy in accordance with the treatment protocol for this pathology and prevent the development of antibiotic resistance
JULIUS BLEICHMANN’S PIANO MUSIC IN ST. PETERSBURG SALONS OF THE SILVER AGE: SOCIO-CULTURAL DISCOURSE
Authors: Богомолова Е.Д.
Published: August 2024

В статье рассматривается специфика функционирования программ из произведений фортепианной музыки, исполненных на эстраде и в любительских салонах Санкт-Петербурга на рубеже XIX–XX веков. Даётся краткая характеристика сочинений забытого петербургского композитора Юлия Ивановича Блейхмана в контексте отечественной культуры этого периода. На основе изученных источников предпринимается попытка реконструкции публичного фортепианного репертуара и знаковых для слушателя эпохи Серебряного века музыкальных персон.

The article deals with the particular features of the function of piano music programmes performed on stage and in amateur salons of St. Petersburg at the turn of the 19th and 20th centuries. A brief description of the piano works of the forgotten St. Petersburg composer Julius Bleichmann is given in the context of Russian culture of that period. An attempt to reconstruct the public piano repertoire and significant musical figures for the Silver Age listener on the basis of the examined sources is made.
Moje pierwsze spotkania z Bogdanem Warszawa, 1962-1967*
Authors:   Laboratoire de PhysiqueThéorique de la Matiére Condensée (LPTMC), Sorbonne-Universités - CNRS UMR 7600, Paris, France, Kerner Richard
Published: August 2024

Author’s encounters and friendship with Bogdan Mielnik (1936-2019) are narrated, evoking the atmosphere of Warsaw University in the years 1964-1967. Mielnik’s colorful and rich personality along with his outstanding contribution and influence on mathematical physics in Poland and Mexico are also discussed.
BogdanMielnik jakiego znałem
Authors:   Departamento de Física, Centro de Investigación y de Estudios Avanzados, Meksyk, Kielanowski Piotr
Published: August 2024

This text introduces Bogdan Mielnik, a longtime professor at the Institute ofTheoretical Physics at the University ofWarsaw and the Center for Research and Advanced Studies in Mexico City.
Exploring the Impact of Hyperparameters on the Generation Quality of CycleGAN
Authors: Tong Zhaoxiang
Published: August 2024

Cycle-Consistent Generative Adversarial Networks (CycleGAN) has demonstrated remarkable proficiency in its ability to undertake image transference between differing domains without the need for paired examples, overcoming a major limitation of traditional image-to-image translation methods. These advantages make it a valuable addition to the toolbox of computer vision researchers and practitioners. However, there persists ample potential for further advancements. This article explores the effectiveness of refining the loss function and training parameters of CycleGAN, aiming to explore how these adjustments affect the outcomes. Specifically, this work plans to improve the loss weight allocation, balancing the translation from different domains. Additionally, the author also explores the number of training epochs and learning rate, halting the process at specific intervals to observe the impact of gradually decreasing the learning rate, as opposed to reducing it to zero. To assess the enhanced performance of CycleGAN, the author will employ both direct human visual perception and cycle consistency loss as evaluation metrics.
A Comparative Analysis Between GAN and Diffusion Models in Image Generation
Authors: Peng Yingying
Published: August 2024

In the field of artificial intelligence, image-generation techniques have been a hotspot for research. Two generative models that have garnered a lot of attention are diffusion models and generative adversarial networks (GANs). This review paper aims to compare and analyze GAN and Diffusion Models in the field of picture generation, as well as to give a thorough discussion of their features, applications, benefits, and drawbacks. Firstly, the work related to the working principle of GAN and diffusion models are introduced, and then their applications and results in image generation are reviewed. By comparing the existing research results, the author find that GAN performs well in generating realistic images but suffers from problems such as pattern collapse and unstable training, while the diffusion model has better stability and controllability. Combining the advantages of the two methods, this paper explores the possible fusion methods and looks forward to the future development direction in the field of image generation. These research results provide important references and insights to further enhance the level and application scope of image generation technology.
A Study of Momentum in Tennis Based on Multiscale Momentum-Success Test Model and Swings Prediction Model
Authors: Lin Yiyi , Xiang Yuhang , Huang Yuhan
Published: August 2024

In tennis, "momentum" is one of the most important factors affecting the results of the game. First, this paper establishes the Multiscale Momentum-Success Test Model and calculates the proportion of the winning side’s momentum at four different scales (score, game, set and match). The proportions of the four scales are 70.3%,78.1%,84.6%,93.5%. Therefore, it can be proved that momentum plays a role in the game. Also, the fluctuation and success in the game are not random. Afterward, this paper establishes the Support Vector Machine Model (SVM) and the Feed-Forward Neural Network Model (FNN). Three correlations are analyzed for factors such as scoring ratio, serve, consecutive scores (lost points), highlight scores, major lost points, and physical condition. It is concluded that the serve, consecutive scores (lost points), highlight scores, and major lost points will play a role in the occurrence of Swing Points. By the two models, we create the Swings Prediction Model. The prediction accuracy of SVM and FNN Model are 84.85% and 67.4%. Finally, based on the momentum changes, match suggestions can be made for the coaches.
Research on The Application of High-Performance Calculation in Real-Time Estimation Algorithm of Polarization Parameters of Conformal Arrays
Authors: Xu Jinjin
Published: August 2024

In this paper, the application of high-performance computing technology in real-time polarization parameter estimation algorithm of conformal array is discussed, aiming at solving the problems of high computational complexity and poor real-time performance in traditional polarization parameter estimation methods. By introducing parallel computing and distributed computing technology, a new real-time polarization parameter estimation algorithm framework is proposed, which can significantly improve the calculation speed and data processing ability and meet the requirements of modern wireless communication systems for high-speed and accurate signal processing. The experimental results show that compared with the traditional algorithm, the time required by the proposed algorithm in this study is obviously reduced when processing the same amount of data, and it also shows significant advantages in accuracy. In addition, the algorithm shows good applicability and robustness in different scenarios, which provides an efficient and reliable solution for real-time estimation of polarization parameters of conformal array antennas.
Visual Information Encoding Modeling From the Perspective of Cell Assemblies
Authors: Wang Tongyu
Published: August 2024

This paper aims to explore the visual information encoding mechanism based on neuronal cell assembly theory, focusing on improving the interpretability and credibility of visual information processing models. By comprehensively applying multidisciplinary theories and methods such as neuroscience, computational neuroscience, and information theory, the effect of neuronal activity in the process of visual information processing is analyzed step by step, and the way visual information is encoded in brain is revealed. It is worth emphasizing that results in this paper is just based on the depth of understanding of neuroscience today. Much unknown remains mysterious so only conjectures and assumptions can be made. It is found that the neurons classify, extract, and reconstruct scenes of complex visual features from the acquired images. And visual modeling idea based on Hebb’s theory about cell assemblies is similar to the real physiological processing process of visual information, and has great potential to solve real-life research problems.
Research on the Metro Ridership Forecasting based on ARIMA Model
Authors: Lyu ShiRu
Published: August 2024

With the increasing coverage of subways in cities, people travel more than just by bus or walking. Nowadays, subway stations have become sites with high population density in the city. Due to the increasing travel demand of residents. Excessive congestion in subway stations will lead to inconvenience and even accidents. The subway passenger flow prediction can avoid many potential problems. Passenger flow prediction can not only optimize the subway scheduling, but also help the operating company to fully prepare for the security work during the rush hours. Data from February 1,2023 to December 31,2023 are from Xi'an Transportation Platform. This research used the ARIMA model for mid-term passenger flow prediction, and the data from February to October were used as the training set. The first order of the data is differential to ensure the stability of the data. The values of p and q in the model were determined using the autocorrelation function, and the most appropriate combinations of p and q were selected by BIC. The ARIMA model was built using (p, d, q), and predicted passenger traffic in November and December. Finally, the feasibility of predicting the medium-term subway passenger flow is determined by comparing the predicted value with the true value.
Recent Progress of Memristor-based Neuromorphic Computing
Authors: Zhou Jun
Published: August 2024

The evolution of memristors and their successful applications have positioned them as formidable candidates for the next generation of computer systems. With the rapid advancement of foundational ar- tificial intelligence applications, there is an increasing demand for computational power, energy efficiency, and stability. Memristors and the Neuromorphic Computing (NMC) systems they underpin hold signifi- can’t potential to break through the von Neumann bottleneck. However, technical challenges remain in the application of NMC to computer systems. In this review, we focus on the performance of various structured memristors within Neuromorphic Computing and across different machine learning algorithms. We pro- vide an overview of the current challenges faced by NMC, including the structural limitations due to sneak paths and the inherent power consumption limitations, and offer a perspective on future developments and opportunities in the field.
Research Process of Path Planning based on RRT Algorithm and Its Improvements
Authors: Wu Wenkang
Published: August 2024

At present, path-planning algorithms in the field of robotics is a problem of research value. Especially in the field of robotics, it has great significance. A good algorithm can improve the efficiency of the robot and ensure the safety of the robot in complex environments. In addition, the path planning algorithm can improve the autonomy and intelligence of the robot, help the robot to realize autonomous navigation and decision-making, and through different algorithms, the robot can also play different roles in different fields. In the past few years, many relatively perfect algorithms have been formed and used. Such as A* algorithm, Q-learning, ant colony algorithm, RRT algorithm, etc. After consulting the relevant literature, the author mainly researches the applications of RRT algorithm and its improved algorithms (RRT*, RRT*-smart, Informed RRT*). There are also some examples of combined algorithms in this article. Therefore, the author will focus on RRT algorithm and its improvements and analyze the common target of these algorithms through examples and comparisons, which is to find a shorter and more accurate path. In addition, the algorithms also need to run faster. In the end, the advantages, and disadvantages of these algorithms in different environments and fields are summarized. In the future research, scientists can further optimize the performance and applicability of these improved algorithms for specific fields or application scenarios. Resulting in better improvements to solve path planning problems, improve efficiency and reduce costs.
Research on the Sex Ratio Variation of Lampetra japonica and Its Reciprocal Altruism Effects Based on the Logistic and Lotka-Volterra Models
Authors: Bao Xingyu , Li Shiya , Chen Zihan , Chen Zihan
Published: August 2024

The lampreys, a parasitic fish native to the Atlantic Ocean, is an invasive non-native species in the Laurentian Great Lakes of North America and an endangered species in most of its native regions in North America and Europe. This paper addresses the study of demographic changes in the population of lampreys in the Great Lakes basin of North America, which is of reference significance to the control of the population of lampreys in the Great Lakes basin of the United States and the protection of its populations in other regions. Since the sex ratio of lampreys changes according to the external environment, its growth rate in the larval stage is fast or slow to determine its final sex, and its larval growth rate is affected by food availability, this paper adopts the logistic model and the Lotka-Volterra model, and simulates the natural growth of the lampreys population in an ideal state by building a mathematical model and the sex ratio changes, and introduced the intra-species competitive role and inter-species competitive role of lampreys population, through the changes in the number of lampreys population, and further analyzed the advantages, disadvantages and such changes of the sex ratio changes on the evolution of the lampreys population itself and the effects of the evolution of the population on the ecosystem and its stability.
Green GDP -- Research on the development of green GDP and ecological environment cost based on regression model
Authors: Chen Zhiyi , Guo Zixin , Wu Ruirui
Published: August 2024

This study explores the concept of green GDP and its relationship with eco-environmental cost, aiming to study the development of green GDP and eco-environmental cost based on regression model. First, green GDP is a new type of GDP after deducting the cost of resource and environment degradation and increasing environmental profit based on traditional GDP. Then, it introduces its accounting method to realize sustainable development by calculating resource and environmental cost and environmental benefit. It is found that the cost of resources and environment has an important impact on the growth of green GDP. Based on the regression model and SPSS statistical software, the complex nonlinear relationship between green GDP and environmental cost, resource depletion cost and environmental deterioration cost is quantitatively analyzed. Through multiple regression, it is concluded that the environmental pollution loss cost has a significant impact on green GDP, and the natural resource depletion cost is also one of the important factors. The results of this study help to better understand the relationship between the development trend of green GDP and the ecological environment cost, and provide theoretical support and policy suggestions for sustainable development.
Deep CNN for EEG in Sleep Staging
Authors: Chen Xiangru
Published: August 2024

There is recently a large amount of deep learning algorithms developed for sleep staging using the signals, especially EEG, recorded by sleep monitoring devices for sleep related patients. Among them, those based on CNN architecture have some advantages over the others. This paper mainly discusses the CNN-based sleep staging algorithms for EEG, and meanwhile considers their upstream monitoring devices and the downstream clinical tasks. The relationship between these three parts of the sleep staging in the clinical context is discussed in this study. According to the overall picture built for the three parts, suggestions are proposed for the scientists and physicians working in these fields. Considering the clinical demands, the CNN-based sleep stage classifiers are expected to perform better on real-time analysis and generalization, while keep improving the precision on two different modality of signals both including EEG. The monitoring devices, correspondingly, should deal with the trade-off between lightweightness and the amount of information recorded. These suggestions should offer an overall view for the scientists to have a clear knowledge about how to work together to contribute to the effective treatment for patients.
A Survey on Referring Image Segmentation
Authors: Wang Honglin
Published: August 2024

With the popularity of artificial intelligence models and the increasing expectation of artificial intelligence applications in many fields, reference image segmentation (RIS) has attracted much attention from researchers. RIS, as one of the most basic and challenging visual language cross-modal tasks in the intersection of computer vision and natural language processing, aims to segment an instance from an image corresponding to a given natural language representation. This paper aims to provide an overview as comprehensive as possible, covering the mainstream benchmark datasets and their statistic information, common evaluation metrics, a few crucial and representative works in RIS, and the performance evaluation of each proposed method. Included RIS methods are elaborated with their core model structure and procedure in performing RIS, and are categorized into 5 classes in this paper based on how multimodal information is processed. At the end of this paper, the author makes a brief expectation of possible future expansions on the research of RIS.
Analysis of Three Techniques in Constrcting OFET
Authors: Wu Perry
Published: August 2024

The Organic Field-Effect Transistor (OFET) boasts several salient features including flexibility, reduced power consumption, and enhanced biocompatibility, positioning it as a pivotal component in the advancement of flexible electronics, wearable technologies, and medical electronic devices. Despite its considerable attributes, the OFET's widespread adoption is hindered by inherent limitations, notably its low carrier mobility. Consequently, there is a concerted effort within the research community to augment OFET performance. This manuscript delineates three predominant methodologies employed in the fabrication of the organic semiconductor layer integral to OFETs: spin-coating, dip-coating, and inkjet printing techniques. For each method, a comprehensive analysis of its underlying principles, procedural intricacies, and performance metrics—such as carrier mobility, cost-efficiency, crystal quality, and applicability scope—is presented. Additionally, empirical instances employing these techniques are meticulously examined to furnish a clearer comprehension of their practical implications. This paper contribution aims to equip future investigators with the knowledge to judiciously select appropriate techniques for their research endeavors, thereby facilitating the evolution of OFET technology.
A Comprehensive Analysis of Path Planning Strategies Employed for Mobile Robots
Authors: Lin Mingjie
Published: August 2024

Path planning is the core of the field of mobile robots. In order to solve the problem of finding the optimal solution, experts have conducted extensive research on it. The document offers a concise overview of rigorously conducted research on prevalent path planning approaches for mobile robots up to this point. There are essentially two main categories of path planning methods for mobile robots: Classical methods and Heuristic methods. I have provided a more detailed classification of these methods: (1) Classical methods, (2) Heuristic Search algorithms, (3) Artificial Intelligence algorithms, (4) Bio-inspired algorithms. Classical methods overly rely on static environments and cannot be applied to practical situations, therefore path planning requires innovative methods. Bio-inspired algorithms are a hot topic in path planning methods, and many studies have innovated on the basis of basic bionic algorithms. Nowadays, Artificial Intelligence algorithms represented by artificial neural networks have gradually become the focus of path planning research due to their high adaptability and robustness. This paper investigates the basic principles, advantages and limitations of each method in the above classification, as well as new algorithms extended based on heuristic methods. In conclusion, this paper offers a succinct overview of the present research status on path planning for mobile robots and explores potential future directions in this domain.
Application of Artificial Intelligence Technology in the Agricultural Field
Authors: Wang Yue
Published: August 2024

With the rapid development of artificial intelligence (AI) technology, its application in agriculture is continuously expanding, bringing new intelligent and precise solutions to agricultural production. The continuous development of artificial intelligence technology has brought great changes and opportunities to the agricultural field.This paper discusses the current status of artificial intelligence technology in agriculture and its significant implications for sustainable agricultural development. It reviews the applications of artificial intelligence technology in agriculture, including pest and disease prediction, soil and crop monitoring, agricultural robots, and decision support systems. It also analyzes the technical challenges faced (such as data processing capability, algorithm accuracy, data security, and privacy protection) and social and ethical challenges (such as changes in the role of farmers, fairness in data usage). Solutions to these challenges are proposed, emphasizing the crucial role of policymakers. Overall, the application of artificial intelligence technology in agriculture holds enormous potential to promote agricultural modernization and sustainable development.
Climate Prediction with Tree Structure Based on Random Forest
Authors: Ding Zhouyu
Published: August 2024

Climate prediction refers to the use of scientific methods and techniques to predict and analyze changes in the natural environment at a certain point in the future. This paper aims to improve climate prediction by analyzing its parameters and impacts using Random Forest (RF) techniques. Specifically, firstly, data frames (DF) are manipulated to explore data features, perform visualization, and perform preprocessing. Second, the paper introduces RF as a base model as a backbone network to accurately estimate climate change. RF is robust to outliers in the data using a tree-based approach. By integrating multiple decision trees and introducing stochasticity (e.g., randomly selecting features) during the training process, RFs are effective in reducing the risk of overfitting and improving the model's generalization ability. Third, mean absolute error loss (MAPE loss) is used to compare the errors that are evaluated on the data. The experimental outcomes demonstrate the efficacy of the proposed model in climate prediction, with the accuracy increased to 94%. Applying the proposed model in climate prediction in this paper provides valuable insights for climate prediction.
Advanced Electronics: The Emergence, Evolution, and Future of Gallium Nitride Technology
Authors: Xiong Weixiang
Published: August 2024

Gallium nitride (GaN) is a desirable substance for electronics that require high frequencies and high power. The research and development of vertical GaN devices on GaN substrates has made significant progress in recent years, thanks to the availability of high-quality free-standing bulk GaN substrates. Despite some inherent difficulties associated with substrate fabrication and the intricate process of metal doping for sensor applications, the prospects for the future of GaN appear quite encouraging. As a result, various transistors and diodes with excellent characteristics have been reported. This essay explores the trajectory of Gallium Nitride (GaN) and its transformative influence on the field of electronic technology. It starts from the distinctive characteristics of GaN, including its broad bandgap, Wurtzite structure, and remarkable thermal conductivity, within the framework of its historical progression, spanning from its initial synthesis to its significant contribution to energy-efficient lighting and 5G technologies. The progress made in the manufacturing processes of GaN, which have shifted towards more sustainable and efficient methods, is also discussed. It also explores the innovative applications of GaN in high-frequency power electronics and optoelectronics, emphasizing its importance in enhancing device efficiency and facilitating environmental monitoring.
Investigating and Forecasting Momentum Shift Effects on Strategy Development in Sports Competitions
Authors: Sun Wenqian , Hua Heng
Published: August 2024

Momentum shifts in competitive sports have a significant impact on game outcomes and strategy development, and despite their widely recognized importance, there is a relative lack of relevant systematic research. In this regard, this study quantified the relationship between momentum shifts and player success, using the metrics analysis method to analyze their correlation and stochasticity. The relevant metrics mainly comprised Spearman rank correlation analysis, and P-value test calculations, which yielded a strong correlation of 0.84 and a P-value less than 0.05. Furthermore, to predict and visualize possible shifts, our study used the LightGBM algorithm to build a predictive model, where we considered shifts to represent changes in strengths and weaknesses between players i.e. moments of change. Afterward, the researchers did an error test for our model, which had a low mean squared error of 0.0019. Finally, this research performed a features importance analysis to identify the most relevant factors such as GPCI, and SGD, and based on the results four ways were suggested for players to deal with new opponents.
Object Detection Based on Deep Learning
Authors: Yin Hang
Published: August 2024

Object detection technology is a crucial research direction in the field of computer vision. Its primary task is to identify and locate objects within images. This technology finds extensive applications across various domains such as autonomous driving, medical diagnostics, and security monitoring. With the development of machine learning, especially the progress of deep learning in related fields of image processing, the accuracy rate of object detection has achieved better results. However, different algorithms have different characteristics, which increases the difficulty of algorithm selection in actual use. This paper starts with the development of object detection, and summarizes three aspects: data set, metrics and object detection algorithm. This paper mainly introduces the commonly used data sets of object detection (such as COCO, VOC), traditional object detection algorithms and deep learning-based object detection algorithms. In addition, the current challenges encountered in object detection, especially in the detection of small objects, and future research directions are discussed.
Integrated solutions for line planning and seafloor surface fitting in marine surveying
Authors: Yun Ruoxi , Wei Yunlong , Wei Kaiji
Published: August 2024

Marine surveying plays an important role in nautical safety and marine resources development. Among them, survey line planning and seafloor surface fitting are two key problems in marine surveying. In this paper, we propose a comprehensive solution to these problems. Firstly, for line planning, we establish a mathematical model for determining the shortest line in rectangular sea area, and derive the optimal line path through iterative solving. Second, for the seafloor surface fitting problem, we transformed the seafloor surface into a flat slope and established a plane-approximation surface model to calculate the line path. By visualizing the data and calculating the results, we obtained important results such as the shortest line length and the percentage of missed sea area to the total sea area to be measured. The method proposed in this paper provides an effective solution for marine surveying.
Research on Human-computer Interaction and Emotion Recognition based on Convolutional Neural Networks
Authors: Yang Yuhai
Published: August 2024

Amidst the swift advancement of intelligent vehicles, computer vision technology has become an increasingly important core technology in the field of autonomous driving, especially in providing safety guarantees for drivers. Given the common Incidence of traffic accidents resulting from driver fatigue. The use of deep learning technology to prevent driver fatigue behavior has become an indispensable protective application in intelligent cockpit human-computer interaction. This article introduces an algorithm designed to detect drowsy driving, which relies on Convolutional neuronal networks are employed to address the challenge of fatigue during driving. The core of the research lies in constructing a model for detecting fatigue depending on YOLOv5. This model uses UTA-RLDD and YawDD datasets and extracts refined image features from video frames as available input data for the model. It can have a better detection effect on the blinking and yawning characteristics of the driver's eyelids and lips, thereby effectively determining whether they are in a fatigue state. Compared with deep learning fatigue detection algorithms, experiments have shown that its average accuracy for eye closure and yawning reaches 97.3% and 91.7%, respectively, which can effectively detect driver fatigue, prevent drivers from entering a fatigue driving state, and improve driving safety. Future research will continue to optimize and expand the model to adapt to more complex driving scenarios and make greater contributions to the development of intelligent vehicles.