bluuebunny/crossref_metadata_2025 huggingface dataset:
YOLO V8 Network Lightweight Method Based on Pruning and Quantization
Authors: Cheng Yiyang
Published: December 2024

With the development of deep learning, convolutional neural networks have been widely used as core algorithms in fields such as computer vision, natural language processing, and speech processing, surpassing the performance of traditional algorithms. However, its complex structure and massive computational load make many algorithms dependent on GPU implementation, which limits its application on mobile devices with limited resources and high real-time requirements. This article explores in detail the lightweighting process of the YOLO v8 model, aiming to optimize its performance in resource constrained environments by combining BN layer scale factor pruning and int8 quantization techniques under the TensorRT framework. Firstly, using the scale factor in the BN layer as an evaluation criterion, redundant channels in the network are identified, effectively reducing the number of model parameters and computational requirements; Then deploy the model in NVIDIA's TensorRT framework for int8 quantization. This process compresses the floating-point operations of the model into low bit int8 operations, significantly reducing computational complexity and memory usage; And validated through COCO dataset experiments during the process. The results show that YOLO v8, after pruning and quantization, can achieve a compression ratio of 64.2% with a loss detection accuracy of about 4%. This study provides a comprehensive solution for the efficient deployment of deep learning models, especially in application scenarios that require balancing performance and resource efficiency, which has important practical significance. Future work will focus on further optimizing pruning and quantization strategies to adapt to a wider range of network architectures and application requirements.
A Study of Production Decision Making Problems Based on Sample Testing and Cost Optimisation Models
Authors: Zhou Xin
Published: December 2024

In this paper, we provide an in-depth discussion on the optimization of sampling inspection and production decisions in the manufacturing process. In order to strike a balance between cost and accuracy, we propose a method based on two-stage sampling inspection. The method accomplishes the cost inspection with a better inspection cost the method effectively reduces the inspection cost while ensuring the inspection quality. Further, we propose a dynamic model-based production decision scheme. The scheme is able to minimize the production cost by continuously updating the estimate of the defective rate during multi-stage inspection and assembly sessions. Finally, we provide a detailed analysis and discussion of the production decision scheme combining sampling inspection and dynamic modeling using confidence intervals and Monte Carlo simulation methods.
Study on the Influence of Gaussian Window Shaping Parameters on Amplitude Accuracy
Authors: Li Chengwei , Han Zhenwei
Published: December 2024

To address the issue of accuracy in extracting the amplitude of electronic pulses outputted by photon-counting imaging detectors based on microchannel plates, this paper combines deconvolution technology with Gaussian window filtering to achieve Gaussian shaping of the pulses. Simulations were conducted using MATLAB to model the exponentially decaying signals outputted by a charge-sensitive amplifier. By adjusting the pulse width (L) and Gaussian shape parameter (α), the amplitude-frequency response of the shaping system was observed. The amplitude-frequency response patterns of the Gaussian window were obtained.
Research on the Relationship between Obesity and Epigenetics and its Application
Authors: Yang Zhou
Published: December 2024

Obesity is a complex and persistent disease that has been considered an epidemic with serious implications for public health and the global economy, given the rapid increase in its prevalence across all age groups. Pathogenesis of obesity involves environmental and genetic factors. Environmental factors are essential in modifying the epigenome, which in turn affects gene expression and contributes to obesity. This article provides a concise overview of the current knowledge about the connection between epigenetics and obesity, with a specific emphasis on DNA methylation, histone modifications, and non-coding RNAs. The study also investigates two primary mechanisms of epigenetic transgenerational inheritance, which refers to the phenomenon where environmental damages encountered by one generation can impact the susceptibility to obesity in future generations. The article aims to highlight the importance of epigenetics in obesity development and to propose potential interventions for its prevention and treatment.
Brain-Computer Interface Application in Recognition and Regulation of Emotion
Authors: Liu Yichen
Published: December 2024

This paper investigates the application of Brain-Computer Interface (BCI) technology in the realms of emotion recognition and regulation. BCI technology facilitates direct communication between the brain and external devices, offering significant promise for improving human-environment interactions, particularly with regard to the identification and modulation of emotional states. By analyzing brain signals, such as electroencephalography, this study classifies emotions based on widely recognized models, including Ekmans model and the Russell circumplex model. To enhance the precision of emotion classification, machine learning algorithms, such as support vector machines and neural networks, are utilized. Moreover, this study explores BCIs potential in emotion regulation, focusing on neurofeedback and brain stimulation methods like transcranial direct current stimulation, which have shown therapeutic potential, particularly for disorders related to emotional dysregulation. Additionally, the paper delves into the integration of BCI with virtual reality to create immersive environments conducive to emotional therapy. Despite its considerable potential, BCI technology faces obstacles such as low data transmission rates and the complexities associated with user training. Nonetheless, the integration of BCI technology within Industry 4.0 frameworks holds promising opportunities for optimizing human-machine interactions and improving workplace safety.
Benchmarking Mutation Type of SARS-CoV-2's Different Variants and Their Relationship with Infectiousness
Authors: Zhang Jingzhe
Published: December 2024

The ongoing COVID-19 pandemic, fueled by the highly transmissible and mutable SARS-CoV-2 virus, has disrupted global health, economies, and daily life since late 2019. The virus's capacity to mutate into novel variants poses significant challenges to prevention and control efforts, altering its transmissibility, virulence, and potential for immune evasion. Monitoring the circulation of SARS-CoV-2 variants is crucial as they may differ in transmissibility, disease severity, and impact on healthcare systems. Furthermore, mutant strains can compromise vaccine effectiveness, necessitating continuous surveillance to inform public health interventions and ensure the efficacy of preventive measures. This study underscores the importance of tracking variant dissemination to anticipate and mitigate the evolving threat posed by SARS-CoV-2.
From Physiology to Psychology: A Multidimensional Analysis of Recovery Strategies for Young Athletes
Authors: Han Zeyu
Published: December 2024

Young athletes are often at risk of physical fatigue and injury during intense competition and training, making post-competition rehabilitation particularly important. Effective rehabilitation not only helps them regain strength and reduce the likelihood of injury, but also improves overall athletic performance. Science-based post-competition rehabilitation can help young-athletes maintain good physical condition, extend their athletic careers, and enhance their academic and athletic performance. This paper analyzes the effectiveness of post-competition recovery strategies in improving the athletic performance of young athletes, focusing on physiological, physical, and psychological recovery methods. It emphasizes the importance of proper nutrition and rest for physiological recovery, highlighting their role in muscle repair and energy replenishment. Physical recovery strategies such as stretching, massage, and low-intensity activities were examined to understand the benefits of these strategies in relieving muscle tension and improving flexibility. The study also explored mental recovery techniques, including relaxation exercises and psychological support, to understand their impact on stress management and mental resilience. This research highlights the significance of employing a multifaceted approach to recovery techniques in order to enhance overall performance. By adopting efficient recovery protocols, athletes are able to attain superior recovery outcomes, boost their competitive advantage, and potentially prolong their athletic careers. The manuscript advocates for additional investigations to fine-tune these strategies and assess their long-term advantages for sustained athletic success.
Research Progress on the Intervention Effects of Different Exercise Forms on Risk Factors for Obesity-Related Cardiovascular Diseases
Authors: Wang Yuchen
Published: December 2024

Obesity has become a major global health issue and is an independent risk factor for several cardiovascular risks. Exercise, as a therapeutic method, is commonly used to improve cardiovascular health in obese populations. This paper reviews and analyzes the effects of five different exercise forms on risk factors for obesity-related cardiovascular diseases: Moderate-Intensity Continuous Training (MICT), High-Intensity Interval Training (HIIT), Sprint Interval Training (SIT), aerobic combined with resistance training, and Blood Flow Restriction Training (BFR). The aim is to explore the similarities and differences in health benefits among these exercise forms for individuals with obesity. The review finds that MICT is notably effective in reducing cardiovascular disease risk factors and promoting cardiovascular health in obese individuals. In contrast, high-intensity interval exercises such as HIIT and SIT show even more significant improvements in cardiovascular health. This difference may be attributed to varying biological effects caused by different intensity levels and intervals, though the specific mechanisms require further clarification. Since increased skeletal muscle can enhance basal metabolic rate and positively impact the cardiovascular system, resistance training is often recommended in combination with aerobic training for better results. Additionally, BFR can achieve similar training effects under lower exercise loads, providing a safer training option for obese individuals. Future research should focus on the mechanisms by which different exercise forms affect specific cardiovascular disease risk factors and how to optimize exercise programs based on individual differences to enhance personalization and scientific validity of interventions.
Evaluating Retinal Transplantation Success in Degenerated RCS Nude Rats Using Electroretinography
Authors: Wu Xia
Published: December 2024

Our research aims to develop a therapy for incurable retinal diseases, such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD), using the retinal degenerate Royal College of Surgeons (RCS) nude rat model. The approach involves transplanting human embryonic stem cell (hESC)-derived retinal organoid sheets. Photoreceptors in RCS nude rats deteriorate over time, leading to impaired vision, similar to RP and AMD. By transplanting hESC-derived retinal organoid sheets, the degenerated host photoreceptors may be replaced or rescued, potentially halting further degeneration and even enhancing visual performance. Electroretinography (ERG), a noninvasive method, is used to measure visual function. Our current project utilizes ERG to monitor visual responses in RCS nude rats and test whether transplantation of hESC-derived sheets leads to improved light response compared to age-matched control (AMC) non-surgery and sham groups. The left eyes of RCS nude rats were exclusively treated with retinal sheet transplants, allowing for internal comparison. ERG data collected every two months over six months showed a significant decline in visual function with age in both low-light scotopic and bright-light photopic tests for AMC and sham groups. Notably, transplanted RCS nude rats exhibited enhanced responses in the treated left eyes. Together with findings from superior colliculus (SC) recordings and Optokinetic (OKN) assessments, the results suggest that hESC-derived retinal organoid sheets improve visual function in RCS nude rats.
Financial Technology and New Quality Productive Forces of Enterprises
Authors: Zhang Yingxi
Published: December 2024

The field of financial technology (Fintech) is rapidly advancing within the global economy, offering essential support to businesses undergoing digital transformation, enabling data-driven decision-making, and providing access to innovative financial services. New quality productivity, crucial for achieving high-quality development, represents a more integrated and modern form of productivity in the digital age, driving ongoing progress and advancement for businesses. This study utilizes data from Chinese A-share listed companies (2011-2022) to investigate the influence of Fintech on enterprise productivity and its underlying mechanisms. The empirical findings reveal that regional levels of Fintech significantly enhance new quality productivity through technological innovation and optimized allocation of production factors. It is observed that the business environment plays a critical role in moderating the impact of Fintech on new quality productivity. Furthermore, the study highlights Fintech's substantial impact on state-owned enterprises, large-scale firms, and non-high-tech sectors. These findings offer valuable insights for businesses facing transformation challenges. Governments can further empower businesses to maximize Fintech's role in achieving new quality productivity by enhancing the business environment and promoting industry cooperation.
Analyses for Signal Detection Algorithms and Corresponding VLSI Architectures Applied for Massive MIMO Systems
Authors: Wang Congshuo
Published: December 2024

Abstract. In the field of 5G communications, the utilization of Massive MIMO has significantly improved the spectral efficiency. However, signal detection involves large-scale matrix inversion, which is computationally intensive. Traditional techniques for linear detection, such as Zero Forcing and Minimum Mean Square Error, struggle to effectively reduce computational complexity. Therefore, this paper introduces three methods to mitigate the complexity in signal detection: the Alternating Direction Method of Multipliers for conditions where the base stations quantities are nearly equivalent to the users quantities, the Neumann series expansion method that replaces matrix inversion, and iterative methods that approximate the true value through iterative updates. Future work should focus on optimizing the iterative expressions and incorporating tools such as deep learning to further reduce computational complexity and accurately retrieve the signal values.
Deep Learning-Based Network Intrusion Detection Systems
Authors: Wang Yaxuan
Published: December 2024

With the rapid growth of the internet, the security threats to computer networks have escalated significantly, making the reduction and prevention of cybercrime a top priority in the digital age. Traditional Network Intrusion Detection Systems (NIDS) struggle with limitations in detection accuracy and real-time performance as attackers employ increasingly sophisticated techniques. In recent years, deep learning has emerged as a prominent solution in the NIDS field due to its powerful capabilities in feature extraction and classification. This paper reviews the application of deep learning in NIDS, with a focus on Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and their hybrid models. The paper discusses the strengths of these models in capturing spatial and temporal features and examines their performance on key datasets such as KDD Cup 99 and UNSW-NB15. Additionally, the paper addresses challenges related to computational complexity, real-time performance, and model interpretability, while suggesting future research directions, including model optimization, lightweight architectures, and improved interpretability. Finally, the potential of Automated Machine Learning (AutoML) in advancing NIDS design and enhancing response capabilities is explored. This study offers valuable insights for further research and development in NIDS.
A Brief Analysis of the Rise of Artificial Intelligence in Internet Applications
Authors: Li Muhan
Published: December 2024

The rise of artificial intelligence (AI) in internet applications has garnered widespread attention, not only due to the rapid development of AI technology and its extensive applications online but also because of the growing demand from users for more efficient, intelligent, and personalized services. With the continuous maturation of big data, deep learning, and natural language processing technologies, AI has gradually found widespread use in areas such as search engines, recommendation systems, automated customer service, and content generation. The application of AI technology has significantly enhanced the efficiency of internet services and improved user experience, driving the internet toward a more intelligent and personalized future. This study reviews the current state and trends of AI applications in search engines, recommendation systems, automated customer service, and content generation through a literature review. The findings indicate that AI technology has markedly improved the efficiency of internet services and user experience. The significance of this study lies in summarizing the achievements of current AI applications, providing references for future research and practice, and promoting the development of a more intelligent and personalized internet.
New Approaches of Traffic Flow Prediction
Authors: Qiao Zhikun
Published: December 2024

Recently, transportation has become a part of people's workday lives, with the fierce development of information, technology, and living standards. Nevertheless, it causes a great deal of inevitable trouble, such as congestion. As artificial intelligence (AI) is becoming increasingly popular, people have begun looking for solutions from AI daily. To bring convenience to relevant scientific research, the author has reviewed some novel AI models for traffic flow forecasting designed in recent years, based on a brief introduction to both machine learning and deep learning. According to the newly proposed AI models, the author has also drawn some expectations of traffic flow prediction that have the potential to be widely applied, the expectation combines mathematical functions, statistical models, and AI algorithms, making the traffic flow forecasting and adjustment more scientific and precise. The integration of advanced AI models into traffic flow prediction holds significant promise for enhancing urban mobility and reducing congestion in our daily lives.
Comprehensive Analysis of Taxi Service Distribution, Demand Hot spots, and Air Pollution Impact: A GIS and Big Data Approach
Authors: Liu Kehan
Published: December 2024

As urban transportation demand increases, optimizing taxi services is critical for improving traffic flow and reducing environmental impact. This study investigates the spatial distribution of taxi services, demand hotspots, and their relation to air pollution using Geographic Information Systems (GIS), big data, and land use analysis. After collecting and processing taxi GPS data, we identified high-demand areas such as airports and commercial centers. We then overlaid this data with air pollution monitoring information to analyze potential correlations. The analysis revealed significant overlaps between regions of frequent taxi operation and elevated pollution levels. Using spatio-temporal models, we predicted demand patterns across different periods and explored the connection between land use (commercial, residential, and industrial zones) and traffic congestion. The results show that taxi services contribute significantly to air quality deterioration in densely populated and commercially active areas. These findings suggest that optimized taxi dispatch and fleet redistribution strategies could not only enhance operational efficiency but also alleviate air pollution in urban centers. This study provides actionable insights for policymakers and taxi companies to develop data-driven approaches to urban transportation management, reducing air pollution while improving service efficiency.
Impact of Adaptive Traffic Signals on Traffic Congestion in Midtown Manhattan: A Propensity Score Matching Approach
Authors: Zhao Zongyang
Published: December 2024

With increasing urbanization, traffic issues have become a significant challenge for cities worldwide. This study employs Propensity Score Matching (PSM) to analyze the impact of Adaptive Traffic Signal (ATS) systems on traffic congestion in Manhattan, using data from the "Midtown in Motion" initiative. Data from New York City's Automated Traffic Volume Recording System (ATVRS) was used to assess changes in traffic flow before and after ATS implementation. Findings indicate that ATS effectively reduces peak-hour congestion and enhances flow, demonstrating its positive effect in easing urban traffic stress.
Adaptive Game Mechanics: Leveraging Multi-Armed Bandits for Dynamic Difficulty Adjustment
Authors: Shen Tongle
Published: December 2024

Dynamic Difficulty Adjustment (DDA) is a crucial task in video game design to select the appropriate difficulty level to maintain player engagement. Recent studies have highlighted the importance of developing adaptive systems that balance challenge and enjoyment. This paper explores the application of Multi-Armed Bandit algorithms to DDA, providing a computationally efficient and explainable solution to real-time difficulty adjustment. A numerical game engine was built with a damage calculator and a linear attribute generator for players and monsters. The study tested four Multi-Armed Bandits (MAB) algorithms: epsilon-greedy, Upper Confidence Bound, Asymptotically Optimal Upper Confidence Bound, and Thompson Sampling within the DDA framework. Results showed that all MAB algorithms successfully identified the optimal difficulty within 100 trials, and on average, players won their battles in more than 150 rounds. These results demonstrate the strong performance of MAB algorithms in dynamic difficulty adjustment and indicate the potential for implementing them in more complex gaming environments. The findings suggest that MAB could significantly enhance the adaptability and efficiency of DDA systems in future game development.
Analysis of Different Methods in Pedestrian Re-identification
Authors: Li Kun
Published: December 2024

This study focuses on the field of pedestrian re-identification (ReID), aiming to enhance the accuracy and efficiency of individual recognition through advanced deep learning models. The article first introduces two deep learning models: Omni-Scale Feature Learning for Person Re-Identification (OSNet) and Multi-Scale Interaction Network (MSINet). OSNet employs Depthwise Separable Convolutions (DSC) and Omni-Scale Residual Blocks to improve the ability to learn features across different scales. MSINet, on the other hand, utilizes neural architecture search technology to design a lightweight network architecture, enhancing feature discrimination and flexible utilization through Twins Contrastive Mechanism (TCM) and Multi-Scale Interaction (MSI). Additionally, a Spatial Alignment Module (SAM) is proposed to enhance the consistency of images under different viewpoints or conditions. The experimental section selects two widely used pedestrian re-identification datasets, Market1501 and MSMT17, for evaluation, and the results show that MSINet outperforms existing methods in terms of accuracy and stability. The article concludes by summarizing the advantages of OSNet and MSINet in multi-scale feature learning and points out their application limitations.
Artificial Intelligence in Software Testing for Emerging Fields: A Review of Technical Applications and Developments
Authors: Ding Yuepeng
Published: December 2024

The rapid evolution of the Internet industry has transformed software development in emerging fields, making it markedly different from traditional approaches. Consequently, software testing has become increasingly complex. Artificial Intelligence (AI) has introduced innovative technologies and methodologies to software testing, attracting growing research interest in recent years. This study employs a systematic literature review to gather and analyze relevant research on AI applications in software testing over the past four years. Through careful screening, synthesis, and organization of literature, the study provides a detailed analysis of research progress in this field. The review examines 30 carefully selected papers, primarily focusing on the technical advancements and applications of AI in software testing within emerging domains such as cloud technology, autonomous driving, and DevOps. It explores various AI applications in software testing, including test case generation, GUI testing, and defect prediction. As a comprehensive review, this paper summarizes recent progress in AI technology applications for software testing, with particular emphasis on emerging trends. Based on these findings, the study proposes potential research directions and topics, offering valuable insights and laying a foundation for future work in this rapidly evolving field.
Research on Minority Character Recognition - Taking Small Seal Font as an Example
Authors: Zeng Chuyan
Published: December 2024

Text recognition is one of the important fields of computer vision and is widely used in automated office, assisted reading and other fields. With the continuous development of deep learning, the visual language text recognition method generated by the combination of optical character recognition and natural language processing involves the technology of recognizing text information from images or videos, which greatly improves the machine's understanding ability and interaction efficiency. However, for some niche fonts, such as Urdu, Xixia, and Xiaozhuan, these characters have complex structures, diverse strokes, and sloppy writing, making them a challenging field in text recognition. Taking Small Seal Scripts, also called Xiaozhuan, as an example, this paper summarizes the advantages and disadvantages of the current Xiaozhuan text recognition algorithm, and proposes algorithm improvement suggestions for the segmentation of Xiaozhuan characters on seals. Xiaozhuan fonts are quite different from modern Chinese characters, mainly reflected in the stroke structure, writing style, and the correlation between characters. It has smoother lines and many glyphs are unique. This complexity makes it difficult to directly apply traditional Optical Character Recognition (OCR) technology. Therefore, many studies have combined image preprocessing, character segmentation, and deep learning models for automatic recognition. Future research should focus more on the generalization and lightweight of the model so that it can run on devices with limited computing resources.
Optimal Timing for Safe Bivalving of Fiberglass Casts Is Before the Exothermic Peak
Authors: Byrd Jay J. , Leonard Annemarie K. , Samson Kaeli K. , Larson Jill E. , Shaw Jordan , Halanski Matthew A.
Published: December 2024

Introduction:

Cast saw injury is a notable source of medicolegal risk. Previous work with plaster casts demonstrated that cast saw injury was minimized by waiting 12 minutes before removal. In this study, we evaluate the safety parameters of fiberglass casting materials.

Methods:

Eight-ply plaster and fiberglass casts were applied to a pediatric forearm model at variable dip-water temperatures, and the mean time to reach their exothermic peak was determined. Fiberglass casts were then maintained at the manufacturer's recommended dip-water temperature and removed at intervals of 2 (before exothermic peak), 6 (approximately fiberglass's exothermic peak), or 12 (after exothermic peak) minutes. All casts were removed by a pediatric orthopaedic surgeon blinded to the cast set time. Cast/blade temperature, saw force, blade-to-skin contact, bivalve time, cast spreading force, and cut completeness were assessed individually and as short (<6-minutes) or long (≥6-minutes) set times.

Results:

Fiberglass casts exothermically peaked markedly earlier (5.2 [IQR = 5-5.4] minutes) than plaster (14.8 [IQR = 13.7-15.3] minutes), P < 0.0001, at maximum temperatures, which did not markedly differ. Downward force applied during fiberglass cast removal was markedly lower in the short versus long set time groups [average forces of 8.3 (IQR = 6.4-10.4) versus 12.9 (IQR = 11.1-14.5) Newtons, P < 0.0001, as were maximum forces: 23.2 (IQR = 18.9-26.6) versus 43.8 (IQR = 38.6-48.5) Newtons, P < 0.0001]. Bivalve time and maximum cast spreading force were decreased in short set times with 40.5 (IQR = 39.2-44.7) versus 44.4 (IQR = 40.6-47.3) seconds (P = 0.06) and 15.5 (IQR = 14-18.5) versus 21.5 (IQR = 18-26.5) N (P = 0.07), respectively. Maximum saw blade temperature was markedly lower in the short (99.6°C [IQR = 98.2-105.6°C]) versus long (130.6°C [IQR = 121.9-141°C]) set times (P = 0.04). No notable differences in blade-to-skin touches or touch duration were detected.

Discussion:

Unlike plaster, fiberglass casts cut before exothermically peaking were associated with less downward force, faster bivalve times, and decreased spread force without increased blade temperature or skin contacts. This suggests that fiberglass casts can be bivalved markedly earlier without increased risk of injury.
Penggunaan Teknologi sebagai Alat, Media, dan Praktik Sosial di Sekolah Dian Harapan XYZ: Pandangan Komunitas dan Peran Misi Sekolah
Authors: Sumual Stelly , Idawati Lusiana
Published: December 2024

Era perkembangan teknologi yang pesat ini, mengharuskan sekolah untuk mengambil langkah antisipatif yang cepat untuk mempersiapkan dirinya dalam mengikuti arus perkembangan agar nilai-nilai yang diyakini oleh sekolah tidak bergeser dengan kehadiran teknologi. Penelitian ini bertujuan untuk mengetahui bagaimana sekolah memandang teknologi sehubungan dengan peran teknologi, serta untuk mengetahui bagaimana sekolah menjadikan pernyataan misi sebagai dasar pendekatan terhadap teknologi. Penelitian ini dilakukan di salah satu sekolah swasta Kristen. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi kasus. Hasil penelitian menunjukkan bahwa teknologi dipakai untuk tujuan manajemen pembelajaran yang lebih efektif, serta bermanfaat untuk mengoptimalkan kemampuan siswa. Hasil penelitian dengan jelas menyatakan bahwa regulasi penggunaan teknologi sangatlah krusial bagi komunitas sekolah agar pemanfaatan teknologi dapat terkendali. Hasil penelitian menyarankan bahwa sekolah perlu lebih intensional dalam mengartikulasikan misi sekolah, serta pemimpin sekolah perlu mempertimbangkan dan mengevaluasi pelaksanaan pengembangan profesional sehingga dapat mempersiapkan guru untuk implementasi teknologi dalam pembelajaran dengan lebih efektif.
Prinsip-Prinsip Perencanaan Sumber Daya Manusia dalam Sistem Pendidikan Islam
Authors: Irvan Indra Ari , Sirozi Muhammad
Published: December 2024

Penelitian ini bertujuan untuk mengeksplorasi ketidakpastian terkait suatu permasalahan, dengan tujuan menemukan solusi berdasarkan ilmu pengetahuan yang diperoleh dari inti permasalahan tersebut. Hasil penelitian menunjukkan bahwa pencapaian tujuan dan sasaran pendidikan sangat bergantung pada perencanaan yang efektif, termasuk perencanaan sumber daya manusia (SDM). Beberapa langkah penting dalam merencanakan sumber daya pendidikan mencakup: mengidentifikasi tujuan sekolah, mendefinisikan filosofi, visi, dan misi sekolah, menganalisis data dan informasi awal yang tersedia, memilih solusi alternatif untuk mencapai tujuan, mengidentifikasi sumber daya manusia dan material yang dibutuhkan, menganalisis hambatan potensial, serta merumuskan tahapan dalam proses perencanaan. Metodologi yang digunakan dalam penelitian ini adalah *library research*, dengan pendekatan kajian kritis terhadap pemikiran para ahli, serta literatur seperti buku, jurnal, dan sumber lain yang relevan dengan topik yang diteliti.
Manajemen Pendidikan dan Peserta Didik dalam Perspektif Al-Qur’an
Authors: Yusuf Dodi Muhammad , Akbar Hilda Marwani , Rismalinda Rismalinda , Melinda Reza , Bustamam Risman
Published: December 2024

Artikel ini bertujuan untuk menganalisis dan menjelaskan penafsiran ayat-ayat Al-Quran yang berkaitan dengan manajemen pendidikan serta peran peserta didik yang tercantum dalam beberapa surat, yaitu QS Al-Baqarah: 31-32, QS Al-Kahfi: 65-66, QS Al-Isra: 24, QS Luqman: 13, QS Al-Ahqaf: 15, QS Al-Maidah: 31, QS An-Nahl: 79, dan QS An-Nur: 41. Penelitian ini menggunakan metode studi literatur, dengan mengkaji tafsir dari berbagai sumber otoritatif untuk menggali nilai-nilai pendidikan yang terkandung dalam ayat-ayat tersebut. Artikel ini memaparkan konsep-konsep manajemen pendidikan dari perspektif Islam, khususnya dalam hal peran pendidik dan peserta didik, serta bagaimana ayat-ayat ini memberikan panduan bagi pengembangan kapasitas pendidik dan pembentukan karakter peserta didik. Hasil penelitian menunjukkan bahwa penafsiran ayat-ayat ini memiliki relevansi yang kuat dalam membentuk kerangka manajemen pendidikan Islam yang holistik, yang menekankan pada pentingnya pembelajaran, hubungan yang harmonis antara pendidik dan peserta didik, serta pengembangan potensi spiritual dan intelektual. Artikel ini juga menyajikan beberapa studi kasus yang menggambarkan penerapan nilai-nilai tersebut dalam konteks pendidikan modern.
Integrasi Kearifan Lokal dalam Perencanaan Pengembangan Kurikulum Pendidikan Islam
Authors: Andini Dinda Rizki , Sirozi Muhammad
Published: December 2024

Penelitian ini membahas pentingnya integrasi kearifan lokal dalam perencanaan pengembangan kurikulum pendidikan Islam, menguatkan identitas budaya, dan memperkuat nilai-nilai moral. Integrasi ini penting dalam menghadapi tantangan globalisasi tanpa kehilangan nilai-nilai tradisional. Desain Penelitian ini menggunakan penelitian kualitatif dengan pendekatan metode studi literatur untuk menelusuri sumber-sumber yang relevan dengan topik ini. Hasilnya menunjukkan bahwa kearifan lokal dapat memperkaya materi ajar dan metode pengajaran, meningkatkan relevansi pembelajaran dengan konteks lokal, dan membantu dalam pengembangan kurikulum yang lebih inklusif dan adaptif. Namun, integrasi kearifan lokal juga dihadapkan pada berbagai hambatan, seperti ketidaktahuan, ketidaksesuaian dengan kebutuhan global, resistensi terhadap perubahan, keterbatasan sumber daya, dominasi budaya asing, dan ketidakpastian terhadap hasil. Untuk mengatasi tantangan ini, diperlukan pendidikan dan sosialisasi, partisipasi masyarakat, pelatihan guru, dan penguatan institusi lokal. Dengan demikian, integrasi kearifan lokal dalam pendidikan Islam dapat memperkuat karakter dan identitas siswa, serta meningkatkan relevansi dan efektivitas pendidikan Islam dalam menghadapi perubahan zaman.
Adaptasi Kurikulum Pendidikan Agama Islam terhadap Kebutuhan Generasi Milenial
Authors: Aldyandra Aldyandra , Sirozi Muhammad
Published: December 2024

Adaptasi kurikulum Pendidikan Agama Islam (PAI) terhadap kebutuhan generasi milenial merupakan upaya penting dalam memastikan relevansi dan efektivitas pendidikan agama Islam di era kontemporer. Penelitian ini bertujuan untuk menguraikan pentingnya adaptasi kurikulum PAI bagi generasi milenial dan mengusulkan beberapa strategi untuk mewujudkannya. Kebutuhan akan adaptasi ini muncul dari perubahan sosial, teknologi, dan nilai-nilai yang dihadapi oleh generasi milenial. Dalam adaptasi tersebut, penting untuk menyesuaikan kurikulum PAI agar lebih sesuai dengan realitas sosial dan kebutuhan generasi milenial. Ini mencakup penambahan materi pembelajaran yang relevan dengan isu-isu kontemporer seperti teknologi, globalisasi, dan tantangan moral modern. Selain itu, penggunaan teknologi dalam pembelajaran juga perlu diperkuat untuk meningkatkan daya tarik dan efektivitas pembelajaran. Kurikulum PAI yang disesuaikan dengan kebutuhan generasi milenial diharapkan dapat membantu membangun pemahaman yang lebih mendalam tentang agama Islam serta memperkuat identitas keislaman mereka dalam menghadapi tantangan zaman yang semakin kompleks. Dengan demikian, adaptasi kurikulum PAI menjadi kunci dalam memastikan bahwa pendidikan agama Islam tetap relevan dan dapat memberikan kontribusi positif dalam pembentukan karakter generasi milenial yang sangat berkualitas.
Inovasi dalam Perencanaan Pengembangan Madrasah di Era Digital
Authors: Sari Mia Permata , Sirozi Muhamad
Published: December 2024

Penelitian ini bertujuan untuk meningkatkan kualitas Pendidikan dimadrasah dan menjadikan madrasah lebih relevan dengan perkembangan zaman. Inovasi dalam perencanaan pengembangan madrasah di era digital merupakan hal yang mendesak untuk meningkatkan kualitas pendidikan dan relevansi lembaga pendidikan Islam dengan perkembangan zaman. Dalam konteks era digital yang terus berkembang, madrasah perlu mengadopsi strategi inovatif dalam merancang perencanaan pengembangan yang memanfaatkan teknologi informasi dan komunikasi secara efektif. penelitian ini membahas konsep inovatif dalam perencanaan pengembangan madrasah di era digital, termasuk penerapan teknologi dalam proses pembelajaran, pengembangan kurikulum berbasis teknologi, dan integrasi sistem informasi untuk manajemen sekolah yang lebih efisien. Adapun manfaat Dengan memanfaatkan inovasi dalam perencanaan pengembangan, madrasah dapat memperkuat posisinya sebagai lembaga pendidikan yang relevan, modern, dan mampu memberikan pendidikan berkualitas di tengah dinamika era digital yang terus berubah. Kegiatan ini mengkaji kritis pemikiran tokoh-tokoh dan literatur buku, jurnal, dan yang berkaitan lainnya yang memang sesuai dengan materi yang akan disajikan.
Perencanaan Peningkatan Mutu dan Relevansi Pendidikan Agama Islam di Era Revolusi Industri 4.0
Authors: Fitriyanti Riska , Sirozi Muhammad
Published: December 2024

Pendidikan Indonesia di tuntut untuk bisa menyesuaikan dengan keberadaan dengan era kemajuan teknologi, integrasi teknologi. Modernisasi pendidikan Islam berbasis pendidikan karakter menjadi keniscayaan untuk menciptakan pendidikan yang sesuai dengan kebutuhan zaman generasi milenial. Penelitian ini bertujuan mengetahui permasalahan pendidikan Islam yang sedang terjadi dan bagaimana tantangan pendidikan Islam kedepan. Kemudian bagaimana solusi bagi dunia pendidikan Islam agar mampu bersaing bahkan menjadi acuan dalam pelaksanaan pendidikan di Indonesia. Metode penelitian menggunakan studi literatur dengan mengumpulkan data dari dokumentasi menggunakan metode deskriptif-analisis. Transformsi pendidikan Islam diperlukan untuk membawa keluar dari krisis dan permasalahan, penguatan lembaga pendidikan Islam juga perlu mendapatkan perhatian, meliputi penguatan manajeman pendidikan Islam, penguatan kepemimpinan pendidikan Islam, dan reformasi kebijakan pendidikan, dengan dengan tujuan untuk membentuk generasi seutuhnya dan manusia unggul yang berdaya saing yaitu manusia kreatif, inovatif, berkarakter, mandiri, cinta tanah air dan religius untuk menghadapi Era 4.0 di mana manusia dituntut serba cepat, tepat, efektif dan efisien. Oleh sebab itu pentingnya perencanaan peningkatan mutu terhadap pendidikan agama Islam di era saat ini yang terus berkembang.
Analisis Hambatan Guru dalam Penerapan Model Pembelajaran Guided Discovery Learning pada Pembelajaran IPS Kelas V Sekolah Dasar
Authors: Listiawani Zulfia , Aramudin Aramudin
Published: December 2024

Penelitian ini bertujuan untuk mengidentifikasi kendala dalam penerapan model *Guided Discovery Learning* (GDL) dalam pembelajaran Ilmu Pengetahuan Sosial (IPS) pada siswa kelas V SD. Latar belakang penelitian ini adalah kesulitan yang dialami oleh sejumlah guru dalam menerapkan model pembelajaran yang efektif di kelas. Guru diharapkan mampu menyesuaikan kebutuhan dan karakteristik siswa dengan metode pembelajaran yang inovatif, salah satunya adalah model GDL yang mendorong partisipasi aktif siswa melalui eksplorasi terbimbing. Model ini sering digunakan dalam pendidikan IPS untuk mengembangkan pengalaman belajar yang lebih bermakna. Penelitian ini menggunakan metode deskriptif kualitatif dengan tujuan untuk mengkaji permasalahan yang dihadapi oleh guru kelas V SDN 01 Kandis dalam menerapkan model GDL. Proses pengumpulan data dilakukan melalui wawancara, observasi, dan dokumentasi. Hasil penelitian menunjukkan bahwa kendala utama dalam penerapan GDL mencakup keterbatasan waktu pembelajaran, kurangnya sarana dan prasarana yang memadai, serta rendahnya kesiapan guru dalam mengimplementasikan model ini secara efektif. Temuan ini menunjukkan pentingnya upaya peningkatan fasilitas pembelajaran, pelatihan guru, dan pengelolaan waktu yang lebih baik agar model GDL dapat diterapkan dengan optimal di lingkungan pendidikan dasar.
Sociale Constructie: ethiek en verbeelding
Authors: Van Donkersgoed Lizet
Published: December 2024

De ‘fly on the wall’ positie wordt gezien als een onderzoeksmethode om zonder vooroordelen in een sociale context waardevolle data te verzamelen. Wanneer echter deze ‘traditionele’ focus op het streven naar objectiviteit en neutraliteit in praktijkonderzoek als onethisch wordt ervaren, blijkt Sociale Constructie een alternatief te zijn. Deze sociaalwetenschappelijke visie wordt geconstrueerd door uit te gaan van relationele processen en brengt—zoals we zullen zien—zowel het gezamenlijk mobiliseren van verbeeldingskracht als ook het stellen van ethische vragen naar de voorgrond. Verbeeldingskracht is van vitaal belang voor elke innovatie. Het stellen van ethische vragen is essentieel om ook tot menswaardige nieuwe oplossingen te komen.
Kant formuleerde de ethische noodzaak van menswaardigheid in zijn categorische imperatief (Kant, 1785). Een mogelijke praktische uitwerking daarvan wordt hier voorgesteld. Ook is dit artikel te zien als een verdere uitbreiding en praktische handleiding voor ‘reflection in action’ (Schön, 1984). Door een inclusieve en toekomstgerichte focus kan het denken, werken en onderzoeken vanuit Sociale Constructie actief bijdragen aan het tot stand komen van ethisch verantwoorde innovaties.
Prospects of perennial intermediate wheatgrass (Thinopyrum intermedium) cultivation in Ukraine for grain and feed purposes
Authors: Kurhak V. , Crews T. , Warner R. , Sarunaite L. , Tkachenko M. , Kolomiiets L. , Tkachenko A. , Neimet І.
Published: December 2024

Aim. Generalization of literary sources and familiarization of the scientific community and agro-producers about the perennial culture of various purposes, as well as the need to conduct research on the technological aspects of growing wheatgrass medium or intermediate under the Kernza® trademark in Ukraine. Methods. General scientific analysis and synthesis, analogy and generalization of scientific literary sources and practice. Results. The rich, deep Chernozem soils of Ukraine were formed under the influence of diverse, perennial grassland vegetation. The conversion of grassland ecosystems to annual grains initiated a process of soil degradation that continues to this day. This is because the termination of vegetation required to sow annual crops results in unsustainable levels of soil erosion, loss of soil organic matter, leaching of nutrients, reduced rain infiltration, and a less functional soil microbiome. In recent decades, researchers in the USA and increasingly around the world have begun to develop perennial grain crops that have the potential to reverse the soil degradation associated with annual grain crops. One of the most promising perennials being developed is a dual purpose grain and forage crop derived from intermediate wheatgrass - IWG (Thinopyrum intermedium (Host) Barkworth & D.R. Dewey). The grain from IWG is known under the trade name Kernza®. New research in Ukraine is needed to assess the potential economic and environmental benefits of cultivating Kernza across diverse soil and climatic zones. This paper outlines the environmental and food-related roles of Kernza, including its history of development and dissemination in the USA and worldwide, its contribution to safeguarding environmental quality and food security, specific aspects of intermediate wheatgrass cultivation technology, and the justification for its expansion in Ukraine. Ukraine’s unique combination of climate, soils, and agrarian culture position it to be a potential leader in the regenerative transition to a perennial agriculture. Conclusions. Varieties of Kernza wheatgrass (Thinopyrum intermedium (Host) Barkworth & DR Dewey) are a new promising perennial grain crop that deserves comprehensive study and distribution in the conditions of Ukraine. Kernza is a promising perennial crop that deserves to be grown in Ukraine. Additional research is needed in Ukraine to develop Kernza varieties adapted to the soils and climate of Ukraine, and to document the best agronomic practices for growing Kernza in different regions of the country.
Productivity of winter intermediate forage crops depending on the technological measures of growing in the conditions of the Western Forest-Steppe
Authors: Krasiuk L.M. , Senyk I.I. , Karasevich N.V. , Boltyk N.P.
Published: December 2024

Analysis of the processes of yield formation of winter intermediate forage crops under the influence of pre-sowing seed treatment and the ratio of components in the mixture. To identify the best variants of the experiment for implementation in production in the soil and climatic conditions of the western forest-steppe. Methods. Field, laboratory, mathematical statistics. Results. Research has established that the formation of the winter legume-cereal agrophytocenoses yield was influenced by the studied factors, in particular, the component composition of the agrophytocenosis and the methods of processing the seeds of the legume component, as well as the weather conditions of the growing season. It has been proven that, on average, over the years of research, grass mixtures of winter rye and рannonian peas were better in terms of dry matter yield compared to similar mixtures of winter triticale with рannonian peas. Depending on the ratio of the components and the pre-sowing treatment of the leguminous component seeds, their yield was 5.82–7.10 t/ha and 5.48–6.64 t/ha, respectively. It was stated that the pre-sowing treatment of рannonian peas seeds with a growth stimulator of humic origin contributed to the increase in the productivity of grass mixtures in all variants of the experiment. The increase in dry matter yield was 0.39–0.79 t/ha. Conclusions. On average, over the years of research, the highest productivity in terms of dry matter was marked by the option where a grass mixture of рannonian peas was sown with a sowing rate of 2.5 million/ha of similar seeds and winter rye 75 % of the full sowing rate in its pure form – 7.10 t/ha of dry matter. At the same time, the seeds of the leguminous component were treated with the Lignohumate growth stimulator before sowing. Among the agrocenoses of рannonian peas with winter triticale, the variant with 2.5 million/ha of similar leguminous components, which before sowing was treated with the Lignohumate growth stimulator and winter triticale 25 % of the full rate of sowing in its pure form – 6.64 t/ha of dry matter, proved to be the best .