bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGARUH EKSISTENSI MUATAN LOKAL BAHASA MADURA TERHADAP MINAT BELAJAR SISWA KELAS IV DI UPTD SDN BANYUBUNIH 2 GALIS BANGKALAN
Authors: Aminullah
Published: January 2025

Abstract
The background to this research is students' interest in local Madurese language subjects which in fact are part of their own regional culture but are still of little interest. So the researcher used several problem formulations, including: What is the existence of local content in regional languages ​​for grade IV at UPTD SDN Banyubunih 2? on the learning interest of class IV students at UPTD SDN Banyubunih 2?. The researcher used a quantitative type of research. From the data produced in the discussion and research, the researcher obtained very good conclusions. It can be seen from the results of the questionnaire/questionnaire as well as the results of observers that they received a high score, in the range of scores of 80-100 nominal percentages and positive answers. This means that there is an influence of the existence of local Madurese language content on the learning interest of class IV students at UPTD SDN Banyubunih 2.
Keywords: Existence, Local Content of the Madurese Language, and Interest in Learning
Abstrak
Latar belakang penelitian ini adalah ketertarikan siswa kepada mata pelajaran mauatan lokal bahasa Madura yang notabene adalah kebudayaan daerah sendiri tapi masih kurang diminati. Maka peneliti menggunakan beberapa rumusan masalah antara lain: Bagaimana Eksistensi Muatan Lokal Bahasa Daerah kelas IV di UPTD SDN Banyubunih 2?, Bagaimana Minat belajar siswa kelas IV terhadap Muatan Lokal Bahasa Madura di UPTD SDN Banyubunih 2?, Apakah ada pengaruh Eksistensi Muatan Lokal Bahasa Madura terhadap minat belajar siswa kelas IV di UPTD SDN Banyubunih 2?. Peneliti menggunakan jenis penelitian kuantitati.f  Dari data yang dihasilkan dalam pembahasan dan penelitian tersebut, peneliti memperoleh kesimpulan yang sangat baik. Terlihat dari hasil angket/kusioner serta hasil observer yang mendapat bobot nilai tinggi, yang dikisaran skor 80-100 nominal presentase maupun jawaban positif. artinya ada Pengaruh Eksistensi Muatan Lokal Bahasa Madura Terhadap Minat Belajar Siswa Kelas IV Di UPTD SDN Banyubunih 2.
Kata Kunci: Eksistensi, Muatan Lokal Bahasa Madura, dan Minat Belajar
IMPLEMENTASI PROGRAM PENGUATAN PENDIDIKAN KARAKTER MELALUI PEMBIASAAN DI SDI AL-ANWAR ASSHOLAH DURIN TIMUR KONANG
Authors: Amiruddin Ibnu , Syahid Ishaq
Published: January 2025

Pelaksanaan program penguatan pendidikan karakter melalui pembiasaan sangat membantu dalam meningkatakan Akhlaq siswa. Dengan adanya program penguatan pendidikan karakter melalui pembiasaan serta dukungan dan peranan dari berbagai pihak, baik dari pihak sekolah, orang tua dianggap sebagai penyebab utama timbulnya para siswa dalam memahami, dan mengamalkan ajaran agama islam, Dalam hal ini program penguatan pendidikan karakter melalui pembiasaan bertujuan untuk membantu dan meningkatkan Akhlak siswa baik di ligkungan sekolah, keluarga, maupun di lingkungan masyarakat. Berdasarkan hal tersebut maka ada dua permasalahan yang menjadi kajian pokok dalam penelitian ini. Yang pertama yaitu bagaimana proses implementasi program penguatan pendidikan karakter melalui pembiasaan di SDI Al-Anwar Assholah Durin timur Konang Bangkalan. Kedua apa saja faktor yang menjadi pendukung dan penghambat dalam implementasi program penguatan pendidikan karakter melalui pembiasaan di SDI Al-Anwar Assholah Durin timur Konang Bangkalan. Penelitian ini menggunakan pendekatan kualitatif dengan jenis deskriptif. Sumber data yang di peroleh  melalui wawancara, observasi, dokumentasi. Informannya adalah kepala sekolah, waka kurikulum, wali kelas 1, wali kelas 5, wali kelas 6. Sedangkan pengecekan keabsahan data dilakukan melalui keikutsertaan, pengamatan, triangulasi atau mencari pembanding data dengan cara yang berbeda, dengan menggali informansi tertentu melalui berbagai metode dan sumber perolehan data. Hasil penelitian menunjukkan bahwa: pertama pelaksanaan implementasi program penguatan pendidikan karakter yang bernuansa religius ini ada berbagai macam kegiatan diantaranya sholat duha berjamaah, pembacaan istighotsah setiap hari jum’at, pembacaan do’a bersama sebelum dan sesudah jam pelajaran di mulai, setelah itu di lanjutkan pembacaan pancasila, dan surat-surat pendek sebelum jam pelajaran di mulai. Yang ke dua faktor pendukung ialah dukungan penuh dan peranan penting  dari semua pihak sekolah, guru dan orang tua. Yang ke tiga faktor penghamabat, beberapa siswa kurang  disiplin dalam kegiatan sholat Dhuha berjamaah, pembiasaan yang dilakukan di sekolah masih kurang di terapkan di rumah (belum istiqomah).
Inovasi Penggunaan Media Pembelajaran; Film Animasi Diva sebagai Stimulan Pengembangan Kemampuan Menghafal Huruf Hijaiyah pada Anak
Authors: Purnamasari Nia Indah , Azizah A’yun Nuris
Published: January 2025

Usia pra-sekolah menjadi waktu yang efektif untuk mengembangkan potensi anak, terutama dalam hal kemampuan bahasa Arab sebagai bahasa al-Qur'an. Pembelajaran huruf-huruf hijaiyah pada anak pra-sekolah menjadi fokus utama, dengan menggunakan media dan metode yang sesuai untuk menciptakan lingkungan belajar yang menarik dan bermakna. Penelitian ini mengidentifikasi rumusan masalah seputar persiapan, penggunaan, dan dampak penggunaan media pembelajaran film animasi Diva dalam mengembangkan kemampuan menghafal huruf hijaiyah pada anak. Melalui metode kualitatif deskriptif, penelitian ini dilakukan di Kelompok B KB-RA Ukhuwah Al-Ikhlash Kureksari Waru Sidoarjo, menggunakan teknik pengumpulan data observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukkan bahwa penggunaan media film animasi Diva efektif dalam pembelajaran menghafal huruf hijaiyah pada anak KB-RA Ukhuwah Al-Ikhlash. Dengan media ini, perhatian anak terfokus pada huruf hijaiyah, dan sebanyak 89% anak masuk dalam kategori baik dalam menghafal dan melafalkan huruf hijaiyah dengan cepat dan tepat. Penelitian ini menyimpulkan bahwa pendekatan pembelajaran yang menyenangkan, seperti menggunakan media film, dapat meningkatkan minat dan hasil belajar anak karena anak cenderung lebih suka dengan metode yang menarik dan beragam.
Upaya Meningkatkan Kreativitas Anak melalui Metode Bermain Plastisin pada Siswa TK Lab STAI YPBWI Surabaya
Authors: Istikhakimi Istikhakimi
Published: January 2025

Pendidikan merupakan proses pemberdayaan yang diharapkan mampu memberdayakan peserta didik menjadi manusia yang cerdas, manusia berilmu dan berpengetahuan serta terdidik. Pemberdayaan siswa dilakukan melalui proses belajar, proses pelatihan, proses memperoleh pengalaman atau melalui kegiatan lainnya. Melalui proses belajar siswa diharapkan memperoleh pengalaman memecahkan masalah dan mampu mengembangkan potensi serta kreativitas berpikirnya dengan metode belajar mandiri. Penelitian ini bertujuan : (1) Untuk mengetahui bagaimanakah Upaya meningkatkan kreatifitas anak Melalui metode bermain plstisin Pada Anak Usia Dini di TK Lab STAI YPBWI Surabaya. (2) Untuk mengetahui faktor pendukung dan faktor penghambat dalam meningkatkan kreatifitas anak Melalui metode bermain plstisin Pada Anak Usia Dini di TK Lab STAI YPBWI Surabaya. (3) Untuk mengetahui adanya pengaruh peningkatan kreatifitas anak Melalui metode bermain plstisin Pada Anak Usia Dini di TK Lab STAI YPBWI Surabaya terhadap kegiatan sehari-hari anak usia dini. Penelitian ini merupakan penelitian deskriptif kualitatif. Data diambil melalui wawancara, pengamatan dan angket untuk orang tua siswa. Hasil penelitian ini menunjukkan bahwa kreatifitas anak usia dini dapat ditingkatkkan melalui bermain Plastisin. Kegiatan belajar menggunakan metode bermain plastisin dapat menjadikan anak lebih bergerak dan mengolah grekan tangan dan jari sehingga mampu menyampaikan pikiranya menjadi sebuah karya dan prodak dai pikiran dan kreatifitas anak oleh karena itu bermain plastisin dapat meningkatkan kreatifitas anak. Bermain Plastisin menjadi salah satu kegiatan latihan dalam meningkatkan kemampuan motorik dan dapat meningkatkan Kreatifitas anak. Melalui kegiatan berimain plastisin anak bisa mengekspresikan imajinasinya, dan tidak terasa bahwa anak sedang belajar dalam mengolah dan meningkatkan kemampuan motorik halusnya
Building Scalable Batch Processing Systems for Financial Transactions Using Mainframes
Authors: R Depa Keshava
Published: January 2025

Using mainframes to develop highly scalable batch-processing systems for financial transactions is mandatory for banking solutions with a high transaction rate. In this article, the author focuses on the role of Mainframes in making financial systems effective, scalable, and robust. One of the technologies widely used in financial institutions is batch processing, which helps institutions to complete large volumes of data processing at low costs, bearing high costs on both hardware and human resources to complete the transactions. The article is more of a technical nature, exploring the principles of building and deploying large and extendible batch processing environments with an emphasis on using COBOL for program development, JCL for scheduling, and DB2 for integration. In this regard, the principal approaches include job prioritization, load balancing, and error recovery measures, all discussed to improve performance and reduce downtime. The result of deploying such systems consists of minimizing the time it takes to complete the various transactions and adapting to the expanding needs at the corporate level. Mainframe technology has user-friendly functions and capabilities that are suitable for today’s financial institutions because these institutions require systems that provide services to customers effectively and safely with extensive scalability. Therefore, it is essential to point out from this article that Mainframes remain critical infrastructure in the move to digital financial services.
RFID Technology in Manufacturing and Supply Chain: Improving Productivity and Reducing Costs
Authors: Kacheru Goutham
Published: January 2025

RFID is derived as radio frequency identification. Highly stated technology is used in RFID by manufacturers to increase the rate of return on RFID. RFID has an application centered on the unreliability of tag standards. Many different applications in the Industry elaborated, such as force task management, tracking the item, identifying the parts in a closed loop, and customers aren't required to synchronize RFID technology, different parts of sellers and providers. This gives end users a much bigger range of chances for RFID adoption, which provides users with the option of many confirmed products that are saved by grown-up international standards (Poirier, C. C., (2006). The components used in the RFID tags are bar codes, minicomputers, wireless LANs, industrial controls to manage materials, and handling systems. An RFID solution strategy from a business process standpoint identifies points in the process where data are classified, collected, communicated, and acted upon (Poirier, C. C., (2006)).  Enterprises have to look further than the tag to see the value of RFID. This research paper describes how companies are using or using internal RFID systems to address different concerns such as maintenance, diminished costs and security, changes, and enhanced proficiency.
The Role of AI-Powered Telemedicine Software in Healthcare During the COVID-19 Pandemic
Authors: Kacheru Goutham
Published: January 2025

This overwhelming demand puts a lot of pressure on already strained healthcare services and contrives to bring out the inefficiencies that travel deep within them, calling for new solutions. At the same time, strict lockdowns, with mandatory social distancing significantly decreased the opportunities of more conventional, face-to face medical consultations thus many patients remained unserved or underserved. To these challenges, telemedicine emerged quickly as a preferable option to practice healthcare away from the traditional centralized healthcare facilities. Telemedicine lets the patients talk to the doctors, get a diagnosis, or even get prescribed medication all from the safety of their own home. Moreover, incorporation of artificial intelligence (AI) in telemedicine greatly enhanced this solution to an enhanced level of technological advancement.
Defining the Contours of Knowledge/Science: A Study of H. G. Wells’s The Invisible Man in Terms of Metaphysics of Science Fiction
Authors: Maheshwar Singh  , Nisha Gupta
Published: January 2025

Derived from the Greek meta ta physika ("after the things of nature"); referring to an idea, doctrine, or posited reality outside of human sense perception. In modern philosophical terminology, metaphysics refers to the studies of what cannot be reached through objective studies of material reality. Areas of metaphysical studies include ontology, cosmology, and often, epistemology (pbs.org). The metaphysics of science fiction explores the fundamental nature of reality as presented in sci-fi narratives. It examines concepts like existence, time, identity, and the nature of consciousness within imaginative frameworks. Metaphysics in science touches the concepts of time machine, parallel universes, artificial intelligence, aliens and invisibility. It works on how different laws of physics shape our existence. The metaphysics of science fiction in H.G. Wells' The Invisible Man explores profound themes related to identity, existence, and the nature of perception. The novel delves into different problems related to identity, when Griffin becomes invisible he becomes immoral raising questions of ethics. The novel challenges the relationship between what is perceived and what exists. Griffin's invisibility forces characters and readers alike to consider how much of reality depends on sensory experience. Griffin's invisibility leads to profound isolation, prompting existential questions about human connection. His detachment from society highlights the need for social interaction in defining one’s existence and reality. His detachment will be compared to the detachment of Marlow’s Doctor Faustus, who was in his mad journey of gaining supernatural powers, and Ralph Ellison’s Invisible Man who was an Afros-American deprived of his societal interaction because he is black. Griffin's experiments and subsequent actions lead to unintended consequences, challenging notions of free will and determinism. He decides his fate by his actions. Griffin's use of invisibility illustrates the corrupting influence of power. The research will help to find these sub topics under umbrella title to explore ethical implications because of the advancements in science and changing nature of reality because of advancements.
Single-photon Source Based on Self-Assembled Quantum Dots and Its Applications
Authors: Wang Wenjing
Published: January 2025

Over the past few decades, extensive exploration has been conducted on quantum dot light sources. Researchers have continuously improved the synthesis techniques of quantum dots, developing various materials and structures to enhance their optical performance and stability. This has also led to the expansion of application fields for quantum dot light sources. This paper provides a concise overview of the fundamental background knowledge on self-assembled quantum dot (QD) single-photon sources. Additionally, it explores the techniques for evaluating the standards of single-photon sources through two-photon suppression and the second-order correlation function. It reviews common self-assembled quantum dot growth methods, focusing on molecular beam epitaxy (MBE) technology and the introduction of Distributed Bragg Reflectors (DBR).  Finally, this article introduces the research history of enhancing single-photon performance by modifying the quantum dot system structure. The primary measure involves enhancing single-photon emission using microcavity structures. By leveraging the tight coupling between the photons radiated by atoms, quantum dots, and other structures with the microcavity field modes, significant improvements in single-photon characteristics are achieved. Researchers have already obtained single-photon emission streams with well-regulated time intervals and time-controllable quantum dot single-photon sources. Additionally, they have been able to fabricate high-quality quantum dot light sources that exhibit high efficiency, indistinguishability, and single-photon purity.
Research on CMOS Devices Based on Wide Bandgap Semiconductor Materials
Authors: Ding Yuehang
Published: January 2025

Considerable financial resources have been allocated towards the design and development of wide and ultra-wide bandgap semiconductor circuits due to their use in power and radio frequency electronics. This is due to the fact that these circuits possess the capability to accomplish these tasks. Due to their minimal loss rates and capacity to rapidly switch high currents and voltages, these devices exhibit great versatility. The presence of parasitic effects in silicon-based CMOS circuits, which are necessary for the integration of intricate control logic, often hinder the performance of end systems that use these devices. This is a difficulty since these circuits are crucial for the execution of intricate control logic. A viable technique that has recently evolved involves directly incorporating CMOS circuitry into a wide bandgap substrate, which is suitable for a range of mature materials. This approach has developed a functional technology. The objective of this study is to examine the present condition of CMOS technology in wide bandgap materials such as diamond, SiC, and GaN, with the aim of analysing its current situation. The findings of this study article provide useful insights that might be used in the advancement of energy-efficient electronic gadgets with enhanced performance capabilities.
Speech Synthesis and Personalization under Unimodal and Multimodal Conditions
Authors: Gao Wanlin
Published: January 2025

Recently, there have been notable advancements in TTS technology, with researchers optimizing the efficiency, quality, and flexibility of speech generation through various models. This paper systematically explores end-to-end TTS models based on waveform generation, including Parallel WaveGAN, NaturalSpeech, and Multi-Band MelGAN, each of which has unique features in enhancing real-time generation capabilities and sound quality. Additionally, the paper discusses the development of speech separation and synthesis technologies, highlighting the applications of models like CONTENTVEC in pitch adjustment and speaker information disentanglement. In terms of multimodal technology, speech-to-gesture generation has also seen important breakthroughs, utilizing multimodal information to generate natural gestures. The paper provides a detailed summary of the main datasets used in related research, such as LibriTTS, LJSpeech, and VCTK, aiming to offer reference and guidance for future research in speech generation. Although these technologies have achieved significant advancements in efficiency and multifunctionality, the associated models remain complex and require substantial computational resources, limiting their widespread application in practical scenarios.
Research on Financial Loan Default Prediction Based on Multi-Model Ensemble and Custom Thresholds
Authors: Chen Jialun
Published: January 2025

Loan defaults pose significant threats to financial institutions' financial stability and reputation. Although existing risk assessment models have addressed this issue to some extent, they exhibit significant limitations when dealing with large-scale, high-dimensional data. Therefore, developing an advanced model that can predict loan defaults with higher accuracy is crucial. This paper aims to optimize loan default prediction by combining innovative algorithms and models to enhance the risk management capabilities of financial institutions and reduce economic losses. This study proposes a loan default prediction model based on the LendingClub dataset. The model integrates multiple machine learning algorithms, including Logistic Regression, Random Forest, Gradient Boosting, LightGBM, and CatBoost, as well as ensemble learning methods, aiming to improve the prediction accuracy and stability of the model. Through a comprehensive analysis of the model's precision, recall, and custom evaluation metrics, this paper establishes an optimized comprehensive model, improving recall from 60% to 80% and precision from 28% to 29%. By optimizing thresholds, the model significantly enhances the identification of bad loans while balancing precision and recall, providing an effective solution for loan default prediction.
Advancing Earthquake Prediction in China: Machine Learning Approaches for Risk Assessment and Magnitude Forecasting
Authors: Pan Yaping
Published: January 2025

Earthquakes pose severe disasters and losses due to their sudden and destructive nature, and despite extensive research, predicting earthquakes remains a significant challenge. Recent advancements in earthquake observation and geophysical methods have highlighted the potential of machine learning technologies in improving prediction accuracy. This study aims to explore earthquake distribution in China through machine learning methods, specifically logistic regression and random forest, to identify high-risk areas and predict earthquake magnitudes. The research identifies five high-risk earthquake zones in China and demonstrates that the random forest model excels in predicting earthquake magnitudes within these zones, outperforming support vector machines and backpropagation models. Notably, the study reveals that the b-value is a crucial factor in earthquake magnitude prediction and should be emphasized in future research. This study not only provides new perspectives and methodologies for earthquake prediction but also offers a scientific basis for earthquake warning and disaster prevention. Future work will focus on incorporating additional seismological and precursor indicators to enhance prediction accuracy and contribute to a deeper understanding and more practical solutions in earthquake forecasting.
A Review of the Research on the Combination of Artificial Intelligence and Instrument Recognition
Authors: Yao Cenyue
Published: January 2025

Artificial intelligence has great potential value in the music industry. From the perspective of music recognition based on machine learning, there are numerous possibilities in the industry of music and computer science intersection, music composition and generation; music analysis and preprocessing; music education and interaction, etc. After elaborating on music analysis and preprocessing, a potent about musical instrument recognition can be done with artificial intelligence have discovered. By clarifying previous research combining traditional and modern music features extraction methods like mel-frequency cepstral coefficients (MFCC) and attention mechanism, proposed based on music theory, with deep learning models like convolutional neural network (CNN), artificial neural network (ANN), and Keras for musical instrument recognition. By giving suggestions in this article, musical instrument recognition models’ efficacy can become more precise. Therefore, in the future, researchers can have the possibility to dig into deeper and more branches of the intersection industry, resulting in more unexpected value.
Research on Coverage Width Calculation and Analysis Based on Multibeam Bathymetric Model
Authors: Zhang Junmeng
Published: January 2025

As multibeam bathymetry is widely applied to the detection of complex terrain on the seafloor, how to optimize the coverage width and overlap rate by reasonably setting the measurement parameters has become a key issue in improving the accuracy and efficiency of the measurement. Starting from the actual situation, this study establishes a spatial geometric model based on the seabed topography. It combines the MATLAB programming tool to accurately calculate and analyze the coverage width of multibeam bathymetry and the overlap rate between neighboring strips under different circumstances. For example, when the opening angle of the multibeam transducer is 120 , the slope is 1.5 . The depth of seawater at the center point of the sea area is 70m, the model can calculate that when the distance of the measuring line from the center point is 200m, the coverage width is 224.81m and the overlap rate is 11.1%. On this basis, the influence of the position of the survey ship and the angle of the measuring line on the coverage width is discussed. For example, if the angle of the measuring line direction is 45  and the distance of the survey ship from the center of the sea is 0.3m, the coverage width of the beam is 451.79m. Through the calculation and analysis of the model, the working principle of the multibeam detector is accurately and concretely demonstrated to portray and describe the complex seabed terrain.
Explore the Application of Machine Learning in Image Recognition and Detection
Authors: Xu Junyang
Published: January 2025

With the rapid development of computer technology, the application of image recognition technology and machine learning technology in society has become more and more proven. Through consulting a large number of data and literature, this paper probes into the development status of image recognition technology and its future development trend and shortcomings. This paper concludes that there is a certain advanced technology development at present, and machine learning and image recognition are slowly being applied in many fields. For example, it has applications in the fields of medical pathology examination, unmanned supermarkets, autonomous driving and intelligent monitoring. However, some problems are related to low precision, insufficient intelligence, and privacy, which need to be solved in future development and application. In the future, the application of machine learning in image recognition has great space for development and a large market. This article can help others learn the relevant knowledge and suggest some directions and trends to solve the problem.
Governing equation for a rocket going around the sun and returning to the earth
Authors: Wang Yuchen
Published: January 2025

In the article, the author mainly focuses on how to launch a rocket from the earth to go around the sun and return to the earth. The equation and solution of this motion are obtained by means of Newtonian mechanics, calculus and conic curve. To find the expression of the trajectory of the rocket, the article will obtain Kepler’s Law by using Newton’s Law, after setting a polar coordinate system. Similarly, plenty of mathematical methods are used to obtain the result in the article, hence, the combination of mathematic with physics and the application of pure mathematic is the tenet of the article. The most important mathematical method is solving differential equation, which is widely used in not only mechanics, but even quantum mechanics. After obtaining the solution, the author plugs the values into the analytic expression, in order to better reflect the actual role of the research results.
Advanced Optical Efficiency Analysis of Tower Solar Heliostat Fields based on Monte Carlo Ray Tracing and Analytic Geometry method
Authors: Wang Di , Cao Qingyuan , Liu Yujun , Ma Shan
Published: January 2025

Solar thermal tower power is an innovative technology that uses a field of heliostats to convert solar energy into electricity. The ability to accurately compute the optical efficiency and thermal power output is a crucial aspect of effectively employing this technology, subsequently enhancing power generation efficiency through strategies such as optimizing the distribution of the heliostat field. An innovative approach for calculating the annual mean optical efficiency, annual mean output thermal power will be presented in this paper. To calculate the related parameters, a spatial Cartesian coordinate system will be established in this paper by utilizing conventional analytical geometry techniques. This methodology explicitly provides the trigonometric functions for diverse angles, thereby eliminating inaccuracies linked to approximations, guaranteeing that subsequent algorithms deal with the minimal number of variables necessary to achieve a more precise fitting outcome. Regarding the shadow occlusion efficiency, basic analytical geometry will be adopted to simulate the positional relationships and solar reflection angles of the heliostats. This method enables us to compute an annual mean shadow occlusion efficiency of approximately 0.8412. As for the collector truncation efficiency, this paper has developed a ray tracing model based on Monte Carlo random sampling. This model decomposes the solar light cone into discrete rays, which allows us to use the simulation model to assess whether the rays impact the collector. By integrating the energy carried by rays that meet the requirements, this paper quantifies the energy received by the collector, resulting in an annual mean truncation efficiency of 0.8723.
Network Public Opinion Analysis of the Top 100 Events of 2022 Based on K-Means Clustering
Authors: Fang Zizhou
Published: January 2025

To explore the intrinsic patterns and characteristics of the generation and dissemination of online public opinion in a big data environment, this paper collected the top 100 most influential and high-ranking public events from 2022. After quantifying these events with relevant indicators, we employed the classic K-means clustering algorithm in SPSS to perform a cluster analysis on these high-impact 2022 events. This analysis yielded several distinct categories of high-interest online public opinion clusters. We then examined and summarized the unique characteristics of each type, offering a novel perspective for identifying and classifying significant online public opinion events. Additionally, this study provides constructive recommendations to help the public better navigate major online public opinion dynamics.
Transforming Autism Spectrum Disorder Care: The Role of Artificial Intelligence in Diagnosis and Treatment
Authors: Li Mengyu
Published: January 2025

The diagnosis and treatment of autism spectrum disorder (ASD) are highly challenging, demanding extensive preventative and intervention plans. With an emphasis on the use of artificial intelligence (AI) technology, this study aims to look into efficient prevention and therapy approaches for ASD. The study highlights the shortcomings in the present approaches and emphasizes the significance of early diagnosis and intervention. Using a mixed-method approach, the study looks at the effectiveness of different treatment methods and AI-driven diagnostic tools using case studies and a thorough literature review. The methodology includes a detailed analysis of peer-reviewed research papers, clinical trials, and actual AI implementations in ASD care. The results show that AI can improve outcomes for people with ASD by enhancing diagnostic accuracy and customizing therapies. The study additionally investigates at how AI-powered assistive technology, including interactive applications and social robots, can help people with ASD. Additionally, the viability and moral implications of incorporating AI-based solutions into clinical practice are looked at. The paper ends with suggestions for integrating AI into clinical procedures and future lines of study to confront the dynamic nature of ASD therapy. The study intends to give an extensive understanding of how AI may transform ASD care, making it more accurate, tailored, and efficient by addressing these factors.
Across the Spectrum: A Study of Autism in National Survey Data Using Machine Learning
Authors: He David
Published: January 2025

Autism, a neurological disorder and developmental impairment, affects roughly 1 in 36 children in the US. However, relatively few machine learning algorithms, the majority being Logistic Regression models, have been used to predict autism using national data. In this study, other than the Logistic Regression algorithm, four other Machine Learning (ML) algorithms, namely the Random Forest, KNN, Extreme Gradient Boosting, and Support Vector Classifier algorithms, are applied to the NSCH data collected in 2021-2022 (namely, the National -Survey-‘(Data)’ of-Children’s-Health), with a 7:3 training to testing split. Additionally, three resampling methods—'random over sampling, ‘random under sampling’, and ‘SMOTE’—are leveraged to address class imbalance issues. Furthermore, the Shapley Additive ex-Planation (SHAP) values of specific features are visualized and evaluated for their importance. The scalability and potential of these ML algorithms in predicting ASD is explored. Among the children with autism, 77.5% were male, 45.1% had ADHD, 39.0% had allergies, 28.1% had a genetic condition, 29.8% had experienced parents’ divorce, and 22.0% had experienced household hardship. From the SHAP selection, sex, ADHD, genetic conditions, low birth weight, allergies, certain ACEs, and other family factors are identified as important features. The precisions across five ML models are consistent around 95% while the recalls vary from 77% to 92% and F1 Scores range from 84% to 93%. The accuracies are between 0.77 and 0.92. This study demonstrates potential ML models applications in further understanding autism features of children, towards providing early assessment and tailoring data-driven evidence-based interventions.
Very Long Baseline Interferometry Techniques and Their Applications in Radio Astronomy Research
Authors: Zhou Yuyang
Published: January 2025

Radio astronomy plays a crucial role in advancing the understanding of space objects by utilizing radio waves, allowing us to observe phenomena and celestial bodies that are otherwise invisible when using traditional optical telescopes. This technique, called Very Long Baseline Interferometry (VLBI), combines signals from different radio telescopes to give a much clearer picture. This paper talks about how VLBI works, including how it picks up signals, measures time delays, and fine-tunes these signals. It's especially useful for taking pictures of black holes and studying space jets, and it was crucial for the Event Horizon Telescope project. The research shows that VLBI improves how well it can see space and helps us learn about extreme conditions in the universe. Although there are challenges with sensitivity and handling the data, future improvements in technology and expanding into space could help us understand the universe even better. This study gives new ideas and directions for making VLBI technology better.
The Quest for Universal Flu Vaccines
Authors: Duncombe Ryan , Kaur Jasmin
Published: January 2025

Modern flu vaccines have an average efficacy of just 40 percent, and they must be revamped each year. How can we make vaccines that are “universal” — both broadly-protective and highly potent?
Aromatherapy and Essential Oils in Pediatric Care: Applications and Future Directions
Authors: Silva Raquel Maria Galiazzi Diniz Gonçalves , Melo Emília Ferro de
Published: January 2025

Aromatherapy has emerged as a complementary therapeutic modality in pediatric care, leveraging the bioactive properties of essential oils for managing anxiety, pain, and sleep disturb-ances. This mini-review examines the diverse administration routes—topical, inhalational, and oral—and their respective impacts on safety, absorption, and efficacy. Emphasis is placed on non-pharmacological interventions in pediatric dentistry, pain relief, and overall well-being. Current evidence highlights the anxiolytic effects of essential oils such as lavender, jasmine, and Rosa dama-scena in reducing procedural anxiety and enhancing comfort during medical and dental procedures. Despite the growing acceptance of aromatherapy, challenges such as standardization, caregiver ac-ceptance, and limited clinical guidelines persist. This review underscores the need for rigorous re-search and educational initiatives to optimize aromatherapy's integration into pediatric healthcare. Aromatherapy offers a promising, non-invasive approach to enhance pediatric patient outcomes, supporting its incorporation into holistic care models.
Comprehensive evaluation of physical education based on personalized training plan generation algorithm and biomechanics
Authors: Sun Bingjie
Published: January 2025

This work builds on advancements in biomechanics and artificial intelligence to develop personalized training plans, enhancing physical education by optimizing movement performance and reducing injury risks. However, limitations include reliance on accurate biomechanical data, potential algorithmic bias in training plan personalization, and challenges in integrating real-time feedback from wearable devices. The aim is to establish a comprehensive evaluation framework for physical education, leveraging personalized training algorithms and biomechanics to enhance performance and create tailored data-driven exercise plans. We propose the Versatile Hunter-Prey Optimizer-tuned Intelligent CNN (VHO-ICNN) to optimize ICNN parameters through VHO algorithms, thereby improving performance analysis, movement optimization, and injury prevention in athletes. The BFP and BMI datasets contain data for various human features and are utilized for biomechanical analysis and optimizing physical activities in sports and education. To preprocess the data, we employ z-score normalization to standardize joint position data, ensuring uniformity across features. Additionally, the Fourier Transform is applied for feature extraction, allowing us to analyze the frequency components of movements and enhance the model's performance. After evaluation, the results demonstrate an F1-score of 92.37%, accuracy of 93.41%, recall of 96.22%, and precision of 92.95%. The results indicate that the VHO-ICNN significantly improves classification accuracy and reduces injury risk, demonstrating its potential as a powerful tool in physical education. At the cell molecular biomechanics level, cells in tissues like muscles and ligaments are affected by mechanical forces during exercise. These forces can change how molecules in cells work. When we design personalized training, understanding these cell changes can help. If we know how cells react to different forces, we can make better training plans. This can make muscles stronger and less likely to get injured. It also ties in with the data we get from biomechanical analysis and the algorithms we use. So, adding cell molecular biomechanics knowledge makes our approach to physical education and athlete training even better.
Bioinspired adaptive landscape design: Environmental responsiveness strategy based on biomimetic principles—Driven molecular and cellular biomechanics
Authors: Zhang Zhiwei , Wang Xiaoxiao
Published: January 2025

In an era of ecological degradation and urban growth, the significance of delving into the cellular and molecular biology realm becomes evident for bioinspired adaptive landscape design. At the cellular and molecular level, organisms possess intricate mechanisms that govern their responses and adaptations. This study, within the context of landscape architecture, explores how these microscopic biological processes can inspire macroscopic design strategies. This study investigates the use of biomimetic principles in landscape architecture, incorporating bioinspired adaptive techniques such as Integrated Elephant Herding Inspired Swarm Optimization (IEHSO) to optimize design solutions that draw inspiration from natural systems to create resilient and sustainable environments. The efficiency in resource utilization and biodiversity enhancement in nature can be attributed to the precise regulation of molecular pathways and cellular functions. By investigating dynamic interactions in ecosystems, design techniques that mimic nature’s adaptive strategies, such as self-organization, resource efficiency, and biodiversity enhancement, can be uncovered. Concepts like fractal geometry, modular design, and the golden ratio, which are prevalent in natural forms, may have its origin in the growth patterns regulated by cellular and molecular cues. Bioinspired approaches lead to novel solutions for solving concerns such as climate change, habitat loss, and urban heat islands using case studies of contemporary landscape projects, which have been demonstrated. The proposed method is implemented using Python software. The IEHSO model demonstrates precision (91.3%), F1-score (93.85%), recall (93.8%), and accuracy (95.1%) significantly enhancing sustainable environments. The findings show that adaptable landscapes have the potential to reflect the complexity of natural systems while also actively contributing to environmental sustainability. This study aims to highlight the essential role of cellular and molecular biology in creating landscapes that are not only aesthetically pleasing but also ecologically sustainable and robust, thereby advancing the field by bridging the gap between microscopic biological phenomena and macroscopic landscape design.
Optimization of environmental engineering practical teaching system based on cellular mechanics principles and construction of multi-dimensional evaluation model
Authors: Meng Na
Published: January 2025

In the realm of emerging engineering education, the practical teaching of environmental engineering majors cries out for reform and optimization, which can be analogized to the regulatory mechanisms within cellular molecular biomechanics. Cells maintain their functionality and adaptability through a complex network of molecular interactions and signaling pathways. Similarly, an effective practical teaching system must have a well-structured and optimized framework. This study aims to explore the reform of the practical teaching system for environmental engineering majors in the context of emerging engineering education. A multi-dimensional evaluation model was constructed based on the Analytic Hierarchy Process (AHP), and the heuristic algorithm was integrated for weight optimization. The results show that the Improved Genetic Particle Swarm Optimization (IG-PSO) exhibits significant advantages in optimizing the weights of various indicators. After optimization, its Consistency Ratio (CR) decreased to 0.07, representing a 53% and 46% improvement over Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively. Additionally, the fitness value of IG-PSO after 800 iterations reached 0.046, significantly outperforming other comparative algorithms. Furthermore, the assessment of teaching effects in dimensions like experimental performance and innovation ability parallels the overall functionality and responsiveness of a cell. The IG-PSO-optimized evaluation system achieved an excellent score of over 90 in the assessment of actual teaching effectiveness across dimensions such as experimental performance and innovation ability. It shows that the teaching system is a healthy, well-regulated cell that can effectively perform its functions and adapt to different educational needs. Through the analogy with cellular molecular biomechanics, we can gain a deeper understanding of the improvement and optimization of the practical teaching system of environmental engineering, which is crucial for the cultivation of skilled professionals in this field.
Application of multi-scale mathematical model in optimization of cellular metabolic network
Authors: Wang Hongtao , Wang Yulei
Published: January 2025

A cellular metabolic network is an intricate system of biochemical routes and reactions that allow a cell to grow, survive, and operate. These networks handle the conversion of nutrients into energy, building blocks for cell structures, and other bioactive compounds required for cellular functions. The metabolic network’s complex physiological function in completing the catalytic conversion could be completely comprehended from a whole-body viewpoint, which considers the underlying interactions between the metabolic conditions of the body as a whole, surrounding tissue, and specific cells. Research presents a multi-scale mathematical model to optimize cellular metabolic networks by integrating cellular-level metabolic processes with whole-body physiological systems. This approach integrates dynamic flux balance analysis with refined genetic algorithms (RGA) to optimize enzyme activities and metabolic fluxes. The liver material of a physiologically based adult pharmacokinetic (PB-PK) classical was used to evaluate the methods using a genome-scale network rebuilding of a humanoid hepatocyte. A systems-level investigation of hyper uricemia treatment, liver metabolism, detoxification pathway simulation, and PB-PK models was conducted using the multi-scale model that was produced. This model offers a framework for metabolic optimization, facilitating an improved understanding of medication discovery and illness treatment approaches.
Medical Staff as Catalysts for Change: A Systematic Review on Improving Clinical Environments for Better Care
Authors: Alali Fatimah mohammed ali , Alammari Naif Abdulrahman Abkar , Alessa Zahra Habib , Aldhaif Noah Ali , Alnahwi Mohammed Hussain , Alfraij Fatimah abdulaziz saleh , ALMOBARAK Narjis ibrheem mansor , ALDawood Balqees Mohammed Ahmed , Alsaleh Aqeala Taher Habeeb , Alshwai Khadeajah Ahmed
Published: January 2025

The clinical environment is a critical determinant of healthcare quality and patient outcomes. Medical staff, encompassing physicians, nurses, and allied health professionals, play a pivotal role in catalyzing changes that improve these environments. This systematic review explores the contributions of medical staff as change agents in clinical settings, focusing on their leadership, communication, and training efforts. Following PRISMA guidelines, the review synthesizes evidence from studies published since 2016, highlighting the impact of these interventions on workflow efficiency, resource management, patient safety, and satisfaction. The findings reveal that proactive engagement of medical staff leads to significant enhancements in operational effectiveness and care delivery. Recommendations are provided to support their roles through targeted training programs, policy adjustments, and collaborative initiatives. This review underscores the importance of empowering medical staff to drive sustainable improvements in clinical environments, ultimately fostering better healthcare outcomes.
THE ROLE OF URBAN AGRICULTURE IN FOOD SECURITY: A STUDY ON ENTREPRENEURIAL STRATEGIES FOR SUSTAINABLE FOOD SYSTEMS
Authors:   Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, Maharashtra, India, Sapkale Harshdeep Chandrakant , Sonawane Madhulika Ajay ,   Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, Maharashtra, India.
Published: January 2025

Urban agriculture, encompassing farming methods within urban and peri-urban areas, has gained significant attention as a viable strategy to address food insecurity, environmental degradation, and urban poverty. This research explores the multifaceted importance of urban agriculture, highlighting its social, environmental, economic, and health benefits. It further examines entrepreneurial opportunities and strategies to foster sustainable food systems through innovative practices such as backyard cultivation, participatory gardens, green roofs, vertical farms, and tech-enabled marketplaces. The study underscores the critical role of urban agriculture in enhancing food security, promoting economic resilience, and fostering environmental sustainability, especially in rapidly urbanizing regions like Asia and Africa. By integrating policy advocacy, public-private partnerships, and climate-adaptive technologies, urban agriculture presents a scalable model for addressing urban challenges while encouraging entrepreneurship. The findings advocate for a holistic approach that combines technical assistance, community engagement, and policy support to harness the full potential of urban agriculture as a cornerstone of sustainable urban development.