bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Resolution and Noise impact on Multi Band Hyperspectral Image Processing
Authors: Vaibhav J. Babrekar
Published: January 2025

Hyperspectral imaging has become a vital tool for analysing various applications covering environmental monitoring and agricultural sectors. However the effectiveness of hyperspectral image processing can be significantly affected by spatial resolution and noise; pertaining to inaccurate classification outcomes. This study investigates the impact of these two factors (spatial resolution and noise) on multi-band hyperspectral image processing. This study specifically employs the ResNet architecture, a deep learning model known for its effectiveness in image classification tasks. The study highlights the effects of spatial resolution on the performance of ResNet by showcasing that lower spatial resolutions can result in a loss of critical spectral and spatial details. This loss leads to challenges in feature extraction and classification accuracy of trained learning models. Through a series of experiments, we demonstrate how variations in spatial resolution impact the ability of ResNet to accurately classify hyperspectral data. Secondly the effect of Noise on ResNet model reveals that while ResNet exhibits robustness against certain noise types, its performance deteriorates under high-noise conditions when specifically spatial resolution increases. To address this issue, we propose a pre-processing techniques aimed at noise reduction, assessing their effectiveness in enhancing the classification accuracy of ResNet. Overall, our study highlights the intricate relationship between spatial resolution, noise, and machine learning performance in hyperspectral image processing.
Advancing Automatic Math Word Problem Solvers: The UMLC Dataset and Comprehensive Model Evaluation
Authors: Michael Kurisappan
Published: January 2025

Recent years have seen a rise in the number of generative AI technologies, there has been a significant improvement in solving math word problems. This presents its own unique set of challenges because of answer accuracy while solving more complex real-world logical questions. Despite its popularity, existing benchmark datasets are suffering from limitations due to lack of a unified, standardized, fair, and comprehensive dataset to train the AI models to solve complex math reasoning. To overcome the shortcomings of previous studies outlined above, we propose the Unified Mathematical Language Challenge (UMLC), an innovative and comprehensive dataset exactly crafted to ease the solving of simple to complex Math Word Problems (MWPs). UMLC differentiates itself with its extensive range of diverse MWP combinations, spanning various mathematical concepts and problem complexities. Throughout the development, the maximum possible effort was made to cover a diverse array of scenarios, making it an in-depth resource for testing, and improving MWP solvers. Researchers are invited to explore the complexities of UMLC dataset, which serves as an invaluable asset for the advancement of Math Word Problem solving in both practical applications and academic research. The most significant observation of this study is that by using a united, consistent, and reasonable dataset with varying complexity of math word problems, may result in a significant drop in performance of majority of the well-known models.
An Advanced E-Learning Recommendation System Utilizing Enhanced Wild Horse Optimization and Ensemble Classification
Authors: D. Poornima
Published: January 2025

The growth of e-learning will result in the revolution in education. Here, a huge variety of materials and courses from any location in the globe can be accessed and it is facilitated by this revolution. The recommendations received by the learners is difficult to ensure that are both and engaging by using a lot of material. Data sparsity is the one of the issue that several current recommendation systems (RS) may face. This data sparsity occurs when limited data on interaction that will support for accurate predictions and over specialization when the recommendations are highly specialized. To deliver high-quality, personalized learning recommendations, advanced e-learning RS was introduced in the study, as it integrates the Enhanced Wild Horse Optimization (EWHO) with an ensemble classification technique. Fuzzy-C-Means Clustering (FCM) was initially employed by the system for the purpose of pre-processing, as it facilitates in detecting data dense regions and managing noisy data. To optimize system parameters as well as obtaining the best Fitness Values (FV), the EWHO is applied for enhancing the recommendation accuracy. Hybrid model of collaborative filtering (CF) and content-based (CB) approaches was integrated by RS. By using Jaccard Similarity Algorithm (JSA), user preferences based on item attributes and item similarities was analyzed by CBF. Then, Improved Genetic Algorithm (IGA) was employed, user preferences were predicted by CF, as it takes advantages of user interactions. An ensemble classification approach (ECA) is applied, which combines K-Nearest Neighbour (KNN), Kernel-based Support Vector Machine (SVM), and an Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS) for the further enhancement of RS. Based on the needs of the individual learner, a personalized recommendation was facilitated by this integrated methodology. Robustness and accuracy was also ensured by this integrated methodology.
Research on Small Target Detection Algorithm of DETR Network Based on Improved SWIN Transformer
Authors: Fengchang Xu
Published: January 2025

A small target object refers to a target with a very small bounding box size. The definition methods include: (1) Relative definition: the ratio of the width of the bounding box to the width and height of the original image is less than 10%, or the ratio of the area of the bounding box to the total area of the original image is less than 3%; (2) Absolute definition: the size of the bounding box is less than 32×32 pixels. Small target detection has important application value in remote sensing image, medical image, industrial quality inspection and automatic driving. Although the detection of large targets has achieved remarkable results, the detection of small targets still faces challenges such as low image resolution, small size, strong background interference and insufficient samples, resulting in low detection accuracy and slow speed. In view of the fact that the detection accuracy of DETR small target detection algorithm of Swin Transformer on Tiny Person and Wider Face data sets still needs to be improved. In order to solve the problem of low detection rate, this paper puts forward an improved DETR small target detection algorithm model of Swin Transformer, and adopts the following optimization strategies: Firstly, BiFPN (Bidirectional Feature Pyramid) is introduced to enrich multi-scale features, so that its small target features can flow more fully between shallow and deep layers, thus improving the detection accuracy; Secondly, by dynamically adjusting the window size and increasing the attention weight of small target area, the feature loss caused by local self-attention mechanism is compensated; Furthermore, the Hungarian matching algorithm of DETR is improved, and Simota (Optimal Transport Assignment) strategy is introduced to match the prediction frame and the small target more efficiently, so as to improve the recall rate and detection accuracy of the small target and reduce the detection speed. Finally, multi-scale training and data enhancement strategies (random cropping, scaling, small target random resampling, etc.) are used to increase the number of small target samples, and progressive learning rate decline and data disturbance are used to enhance the perceptual ability of the model. The small target detection rate mAP of the improved algorithm model on Tiny Person and Wider Face datasets reaches 50.8% and 70.3% respectively. The results show that the improved DETR algorithm of Swin Transformer has excellent generalization ability, detection accuracy and robustness in different data sets and scenarios.
Machine Learning for Fault Identification in Power Transformers
Authors: Vaishali Niranjane
Published: January 2025

Power transformers play a critical role in the efficient and reliable distribution of electrical energy. Timely detection and diagnosis of faults in transformers are crucial for preventing costly downtime, ensuring safety, and maintaining the integrity of power systems. Traditional methods for fault identification in transformers often rely on manual inspection and periodic testing, which can be time-consuming, labour-intensive, and prone to human error. Machine learning (ML) techniques offer promising solutions for automating fault detection and diagnosis processes in power transformers. In recent years, machine learning (ML) techniques have emerged as promising tools for automating fault detection and diagnosis processes in power transformers. ML algorithms can analyze large volumes of data collected from transformer sensors to identify patterns indicative of various faults, including winding faults, insulation degradation, and overheating. By leveraging ML, utilities and operators can move towards predictive and proactive maintenance strategies, minimizing the risk of catastrophic failures and optimizing asset performance. This paper presents a comprehensive review of recent advancements in applying ML algorithms for fault identification in power transformers. It explores various ML techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning, highlighting their strengths and limitations in transformer fault detection. The paper discusses data availability, model interpretability, and generalization to address these challenges and unlock the full potential of ML in enhancing the reliability and efficiency of power systems.
An Experimental Investigation of the Durability of RC Beams Constructed with Conventional Concrete and the Less Expensive Bubble Deck Method of Field Construction
Authors: Sanjay Bhadke
Published: January 2025

For the construction of houses and other structures, beams are a necessary tectonic component. This greatly reduces the structural dead load by effectively removing the non-structural concrete from the beam's core. When spheres of various sizes and shapes are used in place of the core, the result is a bubble beam. The benefits of factory-made materials and components, controlled conditions, on- and off-site finishing, Hollow Bubble Deck technology, and controlled settings are numerous. There are numerous advantages, such as reduced total expenses, enhanced structural effectiveness, reduced material usage, expedited construction, and environmental friendliness. High-density polyethylene balls are used in place of concrete for the midsection of the beam
A CNN based Discrimination between Natural and Computer Generated Images
Authors: Gangu Rama Naidu
Published: January 2025

The objective of this research is to enhance classification accuracy by employing a systematic pre- and post-processing pipeline, which includes a Convolutional Neural Network (CNN) approach for distinguishing between computer-generated and natural images. In order to improve the consistency of CNN inputs, the methodology implements preprocessing procedures, including image scaling and normalization. The initial phase entails the extraction of a variety of patterns by utilizing feature selection within CNN layers. Transfer learning employs pre-trained CNN architectures, specifically ResNet, to effectively represent features. ResNet is the optimal choice for classification tasks due to its ability to comprehend complex visual characteristics. In order to improve classification decisions and minimize false positives, a post-processing phase implements thresholding on prediction scores. The experimental results suggest that the proposed strategy is effective and can be implemented in situations that necessitate a high level of precision in distinguishing between authentic and synthetic visual content.
Optimizing Spectrum Management Using Advanced Spectrum Sensing Techniques
Authors: Abdul Hameed Ansari
Published: January 2025

The management of spectrum through the advanced techniques within cognitive radio networks (CRN) is vital for detecting vacant spectrum in networks. With the spectrum dearth increasingly encountered in wireless communication systems, the utilization of dynamic spectrum access represents an important strategy for resource management efficiency. Spectrum sensing plays an important role in finding free frequency bands, thus making it possible to use secondary users without affecting excessively the primary user's operations. The paper includes different advanced techniques, such as Noise-Resilient energy detection (NRED), higher order eigenvalue-based detection (HEVD), and Cyclic Prefix Generalized Likelihood Ratio Test (CGLRT), to optimize their Spectrum Sensing efficiency. The proposed model tests specified hypotheses regarding the presence of a signal providing a robust framework for weak signal detection. Optimizing these techniques improves the correct identification of free frequency bands so that can be opportunistically accessed to meet the network efficiency. The results show efficient performance in terms of probabilities of detection and signal-to-noise ratio where the results of each method are compared. This offers new insights into using vacant spectrum and hence increasing network efficiency.
Mathematical Modeling and Statistical Analysis of Novel Hybrid Deep Learning Models for Efficient Demarcation and Classification of Brain Tumors
Authors: Mukesh Chand
Published: January 2025

Recent advancements in medical imaging and artificial intelligence have significantly enhanced the diagnostic capabilities for brain tumors, yet challenges persist in achieving precise tumor demarcation and classification. This research paper introduces a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) to improve the accuracy and efficiency of brain tumor identification and categorization from magnetic resonance imaging (MRI) data. The proposed model leverages the spatial feature extraction capabilities of CNNs and the sequential data processing strength of RNNs, aiming to address the complexity and variability of tumor appearances and locations.Mathematical modeling of the hybrid architecture is presented, detailing the layer configurations, activation functions, and the integration mechanism between CNN and RNN layers. The model is trained and validated using a substantial dataset of MRI images, which includes a diverse range of tumor types and stages, sourced from publicly available medical databases. Statistical analysis is employed to evaluate the model's performance, focusing on metrics such as accuracy, precision, recall, and F1-score, against existing deep learning models. Results indicate that the hybrid model significantly outperforms traditional single-architecture models in both demarcation precision and classification accuracy. The integration of CNN with RNN not only enhances the detection of intricate tumor boundaries but also improves the classification of tumor types based on their morphological and intensity features. This research contributes to the fields of medical imaging and artificial intelligence by providing a robust mathematical model and empirical evidence of the efficacy of hybrid deep learning systems in medical diagnostics, particularly in the challenging area of brain tumor analysis.
Privacy-Preserving Cryptographic Protocols Balancing Data Security and User Privacy in Modern Networks
Authors: Yashika Gaidhani
Published: January 2025

In this age of always-on connection, it is very important to keep data safe while also protecting user privacy. In today's networks, where data travels through many pathways, such as cloud services and IoT devices, cryptographic algorithms are very important for keeping private data safe. But it's still exceptionally difficult to create beyond any doubt that information is secure without putting people's protection at chance. This conversation goes into detail almost privacy-preserving security strategies, looking at their significance, issues, and other ways to solve them. The objective of privacy-preserving cryptographic strategies is to create beyond any doubt that private information is kept secure whereas still permitting secure contact and computation. To keep data secure from individuals who shouldn't have get to to it, these frameworks utilize diverse sorts of cryptography, like encryption, hashing, and secure multi-party computation (SMPC). Information spills and illicit observing are less likely to happen with these methods because they cover up information at diverse steps of exchange and handling. Indeed in spite of the fact that they may well be useful, privacy-preserving cryptographic strategies have a number of issues. Finding a great blend between client security and information security is one of the most issues. Extreme security measures may offer assistance keep information secure, but they frequently include collecting information in ways that are as well intrusive and abuse people's security. On the other hand, putting as well much accentuation on protection might make security weaker, taking off information open to being abused. Finding a cautious adjust between these competing objectives is key to making cryptographic frameworks that work well. A few potential methods that permit secure information taking care of whereas ensuring security are homomorphic encryption, differential protection, and zero-knowledge proofs. Improvements in hardware-accelerated cryptography and distributed computing tools also make it possible to speed up secure processes and make them more scalable.
The Next-Generation Multi-Band Microstrip Antenna for 5G, Wi-Fi, and Wi-Max
Authors: Sachin S. Khade
Published: January 2025

This article explores the development of a Trishul-shaped antenna designed for wireless applications within the 1.7 to 2.69 GHz frequency range. The antenna features a robust multiband response, accommodating critical frequencies for various wireless technologies, including PCS, 5G, Wi-Fi, and Wi-MAX. It meets specific performance standards, such as a reflection coefficient below -10 dB. The antenna effectively supports 5G (n3 and n41), Wi-Fi, Wi-MAX, and PCS bands. The study examines the effects of DGS and split ring resonators, achieving a peak gain of 3.1 dBi and maximum radiation efficiency of approximately 84%. Even with a modest patch size, the measured and simulated results are strongly correlated.
Dynamic Feature-Based Detection of Malware in Non-Executable Files Using a 1D Convolutional Neural Network
Authors: Amit Kumar Singh
Published: January 2025

With the exponential growth of data, the threat posed by malware has been on the rise. Particularly, nonexecutable documents like PDF files can be highly dangerous due to the difficulty in detecting them and the lack of awareness among users regarding such malicious attacks. In this research article, we present a one-dimensional convolutional neural network (1D-CNN) specifically designed for detecting malware in PDF files. To accomplish this, we gather a dataset comprising both malicious and benign PDF files (Evasive-PDFMal2022). We extensively analyze the structure of the input data and explain how our proposed network is tailored to leverage the inherent characteristics of the data. Our network is designed to capture meaningful patterns among spatial clues obtained from the data, enabling it to predict whether a given byte sequence contains malicious actions. Through experimental evaluations, we demonstrate that our proposed network surpasses several state-of-the-art machine learning models and other networks with different configurations, affirming its superior performance in malware detection.
Topological Methods in Machine Learning and Data Analysis: A Mathematical Perspective
Authors: Tejaswini Pradhan
Published: January 2025

Topology, as a part of mathematics that studies the properties of space that are invariant under continuous transformations, has come into focus of the learning community as one of the effective approaches to addressing the difficulties connected with the analysis of the learning matter. This paper examines key concepts of topological approaches – persistent homology and the mapper algorithm to discover the theoretical background of topological methods, their goals and purposes, and their use in analyzing the patterns and structures of the data, improving the machine learning algorithms, and searching for valuable patterns in large datasets. This work shows how topology can be effectively used to solve important problems of the modern data science, including data in high dimensions, model interpretability and detecting small changes and outliers.
Application of the Generalized Intuitionistic Fuzzy Rayleigh (GIFRD) Lifetime Distribution to Agricultural Data
Authors: S. Sujatha
Published: January 2025

A number of writers have conducted research on reliability analysis, which is an important topic in engineering. Classical distributions rely on precise parameters to ensure their reliability. The parameters of distributions are typically believed to be accurate real values of number system. But in the actual world, it's not always possible to measure and record data precisely. Generalised intuitionistic fuzzy (GIF) reliability is included in the current study's expansion of the fuzzy reliability concept. Under the assumption that this lifespan parameter is a generalised intuitionistic fuzzy number, we investigate the dependability characteristic of systems using the Rayleigh lifetime distribution. The cut sets for all the survival functions of the generalised intuitionistic fuzzy hazard function and fuzzy mean time to failure are analysed when these systems obey the generalised intuitionistic fuzzy Rayleigh lifetime distribution (GIFRD). This method uses a band with upper and lower boundaries to depict the danger and dependability curves for each unique cut set. Additionally, reliability evaluations of parallel and series systems are carried out. The current work investigated the PDF, CDF, Hazard Rate Function, and Survival Function of the GIFRD using intuitionistic fuzzy values for real-time data. Kerala's agricultural production in 2018 was the main topic of this study. Therefore, the comparison showed that it is possible to estimate fuzziness values more precisely than real-time data.
Enhancing Gender Detection in Speech using Generative Adversarial Networks and Autoencoder Architecture
Authors: Mandar Diwakar
Published: January 2025

Gender detection in speech analysis plays an important role in voice-based applications, human-computer interaction, and biometric systems. The present study used Generative Adversarial Networks (GANs) to use Mel-Frequency Cepstral Coefficients as input features in gender detection. Using a database of labeled audio recordings, the authors extracted 13 MFCCs per sample to effectively capture the spectral and temporal characteristics of speech. These features were averaged across time frames and standardized to ensure consistency during the training and testing phases. The resulting 13-dimensional feature vectors were processed using a GAN architecture, with the discriminator trained to classify gender based on the input features.
Experimental validation on a dataset of audio samples demonstrated the system's efficiency, achieving an average accuracy of 94.5% in gender classification tasks. The model processes a 10-second audio sample (approximately 160,000 data points) in 0.02 seconds on a standard CPU, showing the computational efficiency and scalability of the model. A classification threshold of 0.5 was used, where predictions above 0.5 indicated female speakers and below 0.5 indicated male speakers.
This pipeline does highlight the prospects of using GANs and MFCC features to present effective and reliable gender detection systems. The approach provides a scalable, reproducible framework that could easily be used for deployment in different applications in the real world, like virtual assistants or speech-based analytics.
Topology Meets Data: Persistent Homology for Machine Learning Applications
Authors: Ch Achi Reddy
Published: January 2025

The use of topological ideas—in particular, persistent homology—to machine learning is covered in this abstract, with a focus on how they might be used to analyse intricate data structures. By analysing topological characteristics including connectedness, voids, and loops, persistent homology offers a multi-scale description of data structures that captures both local and global properties. Machine learning practitioners can improve feature selection, increase model resilience, and find hidden patterns in high-dimensional datasets by utilizing these principles. In addition to helping with the interpretation of complex data, this collaboration between topology and machine learning advances domains including image recognition, genetic analysis, and sensor data interpretation. We examine the fundamental ideas behind persistent homology, its computational methods, and its effects on machine learning results, demonstrating its revolutionary potential in promoting a better comprehension of data-driven problems. By using this multidisciplinary method, we show that topology provides insightful viewpoints on data analysis.
Enhancing Grid Stability through Reactive Power Compensation in Vehicle-to-Grid Enabled Electric Vehicles
Authors: Chirag Arora
Published: January 2025

This article examines Vehicle-to-Grid (V2G) technology, which enables two-way or bidirectional battery chargers (BBCs) to be used to distribute power between electric vehicles (EVs) and the grid. In addition to supporting reactive power compensation, V2G focuses on active and reactive power exchange for load levelling. Vehicles with V2G capabilities help with voltage and frequency management, reactive power support, active power regulation, load balancing, and current harmonic filtering. Reactive power correction may be achieved using V2G without needing active power exchange, which preserves EV battery charge levels and minimizes charging cycles. For an EV to work as V2G from G2V, the battery must be refilled from the grid and discharged. The main focus of this work is the reactive power management of electric cars with V2G capability while discharging. The general goal of the suggested control technique is to maintain the voltage value at the point of CC as close to the nominal value as possible by using mathematical derivations using corrected voltage phase angles. The study also looks at how a V2G application can enable EVs to maintain grid stability with reactive power without using up the batteries inside the car. However, traditional techniques need power to flow simultaneously, which charges or discharges the battery. The device helps to mitigate grid stability issues by controlling the flow of reactive current at the PCC. Since there would not be any expensive infrastructure installation and EV owners might be compensated for providing this service, it is then demonstrated that this benefit is economically feasible. V2G-based reactive power adjustment has a negligible effect on battery life, according to MATLAB simulations, making it a viable option for contemporary power networks. Reactive power compensation is demonstrated through simulations using the MATLAB Simulink and SimPower tools. Reactive current saturation keeps power within limits that have been established. The suggested strategy also maximizes profits for EV owners.
Power Edge Domination Number of Certain Graphs in its Corona Product
Authors: M.Rekha
Published: January 2025

For a graph G(V,E) with size n any edge f∈E, a set S^'⊆E is said to be power edge dominating set of graphs G if each edge e∈E-S^' is dominated in S^' by the following rules if : (i) an edge f in G is in power edge dominating set (in short PEDS), then it dominates itself and dominates all the adjacent edges of f, (ii) an observed edge g in G has m >1 adjacent edges and if m-1 of these edges are observed earlier, then the remaining non-observed edge is also observed by g∈G. The minimum cardinality of a power edge domination number of G denoted by γ_ped^' (G). In this paper we introduce a new notion called power edge domination number and discuss the power edge domination number of corona product of certain graphs.
Más allá de los límites disciplinarios y metodologías tradicionales de aprendizaje
Authors: Tacuma Pineda Sandra , Contreras Godoy Daniella , Tacuma Pineda Freddy
Published: January 2025

El presente artículo contiene el análisis interpretativo y crítico de la secuencia de procesos creativos, científicos, artísticos y culturales lideradas por el colectivo Arte + Ciencia = Consciencia (ArtCC), desarrollados en la región de Coquimbo, Chile. Implementado desde 2020, el proyecto ArtCC integra los conocimientos del arte y la ciencia para promover un aprendizaje social y transformador. Así, el problema que abarca esta investigación comprende la necesidad de transformar las relaciones de los individuos con su entorno, fomentando el pensamiento crítico, la responsabilidad social y la apreciación cultural, específicamente en comunidades, territorios y contextos marcados por serios desafíos sociales y ambientales. Este conjunto de problemáticas es abordado mediante la implementación de metodologías educativas innovadoras y activas que rompen los silos disciplinarios y promueven entornos de aprendizaje colaborativos, inspirados en la pedagogía crítica y la educación para la transformación social. La metodología para el aprendizaje propuesta por ArtCC es interdisciplinaria y experiencial, utiliza el juego, la creatividad y la colaboración como herramientas fundamentales para el aprendizaje. Este enfoque educativo y de transformación sociocultural ha generado un impacto significativo en la región de Coquimbo, evidenciando resultados positivos en diversas dimensiones, desde la creación de programas educativos innovadores hasta el fortalecimiento del tejido social y comunitario. El proyecto ArtCC se consolida como una experiencia educativa inspiradora con un impacto transformador, abriendo nuevas rutas hacia una educación más significativa, relevante y comprometida con los desafíos del siglo XXI.
Penggambaran Hubungan Ibu dan Anak dalam Iklan Lifebuoy Versi Akar Kuat di Bulan Suci
Authors: Nugroho Surya Andrie , Agustrijanto
Published: January 2025

Sosok ibu merupakan sosok yang sangat istimewa dalam keluarga. Hubungan ibu yang baik dengan anaknya dapat membangun perilaku, sifat, dan mental anaknya. Hubungan yang baik ibu dengan anak ditunjukkan dalam iklan Lifebuoy versi “Akar Kuat di Bulan Suci”. Tujuan penelitian ini adalah untuk mengetahui penggambaran hubungan ibu dan anak dalam iklan Lifebuoy versi “Akar Kuat di Bulan Suci”. Penelitian ini menggunakan Teori Konstruksi Realitas Sosial di Media Massa melalui tiga proses sosial, yaitu eksternalisasi, objektivasi, dan internalisasi. Dengan paradigma konstruktivisme, pendekatan kualitatif, Metode Analisis isi dengan teknik analisis semiotika Charles S. Peirce yang menggunakan Sign, Object, Interpretant. Hasil penelitian menemukan bahwa penggambaran hubungan ibu dan anak dalam iklan Lifebuoy versi “Akar Kuat di Bulan Suci” digambarkan sebagai hubungan yang emosional, ditandai dengan gestur-gestur yang ditunjukkan ibu dan anak. Hal ini dilakukan sang ibu kepada anaknya sebagai tanda kasih sayang secara fisik dan emosional.
Tudásátadás az agráriumban – a magyar és a lengyel agrártudás és innovációs rendszer szereplői és működése
Authors: Jakab Ágnes , Dunay Anna
Published: January 2025

Az Agrártudás és Innovációs Rendszer (AKIS) egy olyan rendszer, amely a mezőgazdaságban a tudásátadáson, -megosztáson és -cserén keresztül segíti az agrármodernizációt, elsősorban az innováció és a digitalizáció terjedését. Az agrártanácsadás pedig az AKIS egyik kulcsfontosságú eleme, amely támogatja az információáramlást és a partnerek közötti kapcsolatépítést a hatékonyság és a versenyképesség növelése érdekében. Fentiek megvalósulását segíti az Európai Bizottság Horizont 2020-as i2connect nemzetközi projektje, amelynek célja az agrárszektor interaktív innovációs folyamatait támogató tanácsadói kompetenciák fejlesztése. Ennek keretében képzéseken és külföldi tanulmányutakon (cross-visitek) keresztül arra vállalkoznak a tagországok, hogy megerősítsék és fejlesszék a tanácsadói funkciókat. Fő cél a gazdálkodók fejlesztések iránti igényeinek felismerése és a tanácsadási folyamatba történő beillesztése. Az i2connect projekt országjelentései részletesen bemutatják az egyes országok AKIS-eit, segítve azok összehasonlítását. Fontos az AKIS-szereplők és azok kapcsolatainak megértése, különös tekintettel a Mezőgazdasági Szaktanácsadói Rendszerekre (MSzR), amelyek kulcsszerepet játszanak a tudásmegosztásban, a digitalizációban, végső soron az innováció terjesztésében.
Jelen tanulmány a fentebb említett H2020-as projekt eredményeként létrejött 29 európai AKIS országjelentés közül a magyar és a lengyel országjelentéseket hasonlítja össze. Bemutatja a lényeges hasonlóságokat és különbségeket, fókuszban az agrárinnovációt támogató környezettel és az ezekben az országokban kialakult tanácsadási szolgáltatások jellemzőivel, majd ezekből olyan következtetéseket von le, amelyek a két tagállam számára előremutatóak lehetnek az e területen történő előrelépés, fejlődés, fejlesztés szempontjából.
Eksplorasi kinerja keuangan dan ukuran perusahaan terhadap corporate social responsibility di sektor energi Indonesia
Authors: Venansius Vincent , Wenny Cherrya Dhia
Published: January 2025

The study aims to determine the effect of financial performance and company size on corporate social responsibility in energy sector companies listed on the Indonesia Stock Exchange for 2021-2023. Data collection method by collecting annual financial reports on the IDX website. The number of samples used in this study was 38 from 83 energy companies listed on the Indonesia Stock Exchange; therefore, the number of research data analyzed was 114. The data analysis method used is Multiple Linear Regression. The study's findings indicate that the financial performance variable does not affect corporate social responsibility. At the same time, company size affects corporate social responsibility, and financial performance and company size do not simultaneously affect corporate social responsibility.
Hutang di negara berkembang: Mesin pertumbuhan atau beban ekonomi?
Authors: Zakaria Rinny Apriliany
Published: January 2025

Debt remains a primary source of financing for economic development in developing countries. According to data, there was a sharp economic decline and a significant increase in debt levels among developing countries during the COVID-19 pandemic 2019. This indicates a tendency for governments to rely on debt during the recovery period. Theoretically, debt can have either positive or negative impacts on economic development. This implies that debt can serve as a growth engine or become a developmental burden. Amid this debate, this study aims to identify the impact of debt on economic growth in Southeast Asian developing countries (Indonesia, Philippines, Vietnam, and Cambodia) from 2009 to 2022. Using the Autoregressive Distributed Lag (ARDL) method, we find that debt negatively impacts economic growth in both the short and long term. Additionally, another variable, inflation, also negatively affects economic growth in the long term. Therefore, governments must adopt prudent debt management principles and implement strategic policies to ensure that debt is allocated to finance productive sectors.
Building public trust in cooperatives through digitalization
Authors: Indriastuti Dorothea Ririn , Susanti Rina , Wulandari Rina , Intan Juliana
Published: January 2025

The Digital Economy is the implementation of computers and communication technology through the Internet to facilitate business processes. The digital economy allows many activities that previously had to be done offline to be converted online through the help of information technology. The economic sector in Indonesia that is affected by digitalization is cooperatives. Cooperatives are business entities known as pro-people and have legal entities in Indonesia. This explanatory research study aims to test and explain the causal relationship between research variables through hypothesis testing. The approach taken in this study is a quantitative research approach. This research focuses on discussing the factors that improve the welfare of cooperative members, namely digitalization and trust. In this study, the researcher used 214 respondents who were members of savings and loan cooperatives in the Greater area of Surakarta. This study uses structural equation modeling (SEM) to test the hypothesis. The results of this study show that digitalization and trust can affect the welfare of cooperative members. In addition, trust can mediate the relationship between digitalization and the welfare of cooperative members.
Enhancing research productivity through bibliometric analysis: A community service training for academics
Authors: Purwanto Edi
Published: January 2025

Academics often face challenges in effectively navigating and analyzing the vast and growing body of scientific literature, which limits their ability to identify collaboration opportunities and emerging research trends. This study highlights developing and implementing a community service program to enhance academics' research productivity through bibliometric analysis training. Conducted online on September 6, 2024, the program gathered 34 lecturers and researchers from diverse fields. The training comprehensively introduced bibliometric concepts and practical applications, utilizing tools such as VOSviewer and Scopus Analyze. Participants gained skills in processing bibliographic data, mapping collaboration networks, and identifying emerging research trends. The initiative improved participants' understanding of bibliometric techniques and empowered them to enhance research quality and productivity. Positive feedback from participants underscores the program's success and potential to drive broader academic impact.
Rezension von: Holtz, Sabine u.a. (Hrsg.), Von der Monarchie zur Republik
Authors: Wettengel Michael , Holtz Sabine
Published: January 2025

Sabine Holtz/Gerald Maier (Hg.): Von der Monarchie zur Republik: Beiträge zur Demokratiegeschichte des deutschen Südwestens 1918-1923 (Veröffentlichungen der Kommission für geschichtliche Landeskunde in Baden-Württemberg, Reihe B: Forschungen 224). Stuttgart: W. Kohlhammer Verlag 2019; XII + 198 S., 27 farbige Abb., 24,00 EUR
Discussion and Thinking About the Development of Computer Technology
Authors: Li Qin
Published: January 2025

With the continuous popularization and rapid development of computers in China, they have become an indispensable tool in people's work and life, bringing unprecedented convenience and efficiency. Especially since the beginning of the new century, China's computer industry has shown vigorous vitality, not only profoundly changing people's ways of obtaining and processing information, but also greatly promoting the comprehensive progress of the social economy, indicating that computer technology is steadily moving towards a wider and more challenging new development path. In view of this, this article deeply analyzes the development process of computers since their birth, focusing on the new characteristics of intelligence, networking, and popularization exhibited by current computer technology. Based on this, effective strategies to promote the sustainable and healthy development of computer technology are proposed, aiming to provide useful references and inspirations for future technological innovation and applications.
Research Progress of Physiotherapy in the Prevention and Treatment of Myopia
Authors: Dong Yanrui , Pan Furong , Pan Wang , Tong JingAn
Published: January 2025

With the development of science and technology and the change of life style, myopia has become a global public health problem, especially in young people. This paper summarizes the research progress of physiotherapy in the prevention and treatment of myopia at home and abroad, analyzes the action mechanism, clinical efficacy and existing problems of each therapy, and emphasizes the importance of the combination of traditional Chinese medicine and western medicine in the prevention and treatment of myopia. The aim is to provide effective reference for the prevention and control of myopia.
Analysis of the Disease Status and Traditional Chinese Medicine Constitution of Elderly People in Care in Guangyuan City Region
Authors: An Luoyao , Xiang Hai , Zhang Yang , Zuo Zemin , Li Jia , Yang Nianjun , Chen Zeyun
Published: January 2025

A questionnaire survey was conducted on 500 elderly people aged 60 and above who were placed in the pension institutions in the area of Guangyuan City, to analyze the characteristics of disease types and distribution of traditional Chinese medicine constitution among the elderly, and to provide reference for the prevention and treatment of diseases in the elderly with traditional Chinese medicine. Method: The count data was analyzed using chi-square criterion, and the correlation study was analyzed using Single factor Logistic regression analysis. P<0.05 indicates statistical significance. Result: The diseases that elderly people suffer from are mainly concentrated in cerebrovascular, cardiovascular, respiratory, endocrine and metabolic diseases. Traditional Chinese medicine constitution types are mainly concentrated in qi deficiency constitution, yang deficiency constitution, and yin deficiency constitution. The proportion of disease types and traditional Chinese medicine constitution types varies depending on age and gender. There are statistically significant differences in the distribution of gender in cerebrovascular diseases, respiratory diseases, kidney diseases, urinary system diseases, and skeletal muscle diseases. Age has statistical significance in the distribution differences of cardiovascular, cerebrovascular, respiratory, kidney diseases, hematological diseases, endocrine metabolism, and urinary system diseases. There is a statistically significant difference in the distribution of age in the areas of calmness, qi deficiency, yang deficiency, and blood stasis. Conclusion: Male is a risk factor for cerebrovascular diseases, respiratory diseases, kidney diseases, and urinary system diseases. Damp heat constitution is a risk factor for neurological diseases. Qi deficiency constitution is a risk factor for respiratory system diseases. Phlegm dampness constitution is a risk factor for endocrine and metabolic diseases.
Machine Learning in Perioperative Management: Applications and Progress
Authors: Zhou Ling , Peng Lihua
Published: January 2025

The application of machine learning (ML) technology in perioperative management is increasing, with its importance lying in enhancing surgical safety, improving patient outcomes, reducing healthcare costs, and optimizing anesthetic management. Research progress indicates that ML technology has shown great potential in perioperative risk prediction, real-time monitoring, and rationality assessment of prescriptions, and is gradually changing clinical practice in anesthesiology. We will introduce the perioperative application of ML from the aspects of preoperative assessment, intraoperative management, and postoperative recovery. In addition, we will discuss the progress and challenges of ML in recent years, as well as the future use and research directions of ML.
Research Advancements in the Treatment of Obesity with Chinese Medicine
Authors: Li Yingcong , Wang Defen
Published: January 2025

With the improvement of people’s living standards and changes in lifestyle, obesity has become a global health issue. The incidence and growth rate of obesity in China top those of other countries, thus making it urgent to develop effective methods for preventing and treating obesity. Traditional Chinese Medicine (TCM) has accumulated rich experience in the long-term clinical treatment of obesity, forming advantages such as holistic concept, syndrome differentiation, and minimal side effects, which holds strong potential. In recent years, as research on TCM continues to deepen, the mechanisms and methods of TCM treatment for obesity are gradually being revealed. This article reviews TCM’s understanding of obesity, the theoretical basis behind TCM treatment for obesity, clinical internal and external treatment methods, as well as existing problems and prospects, aiming to provide new ideas and methods for TCM treatment of obesity.
Exploration of the Mechanism of Intimal Hyperplasia in Autogenous Arteriovenous Fistula
Authors: Liang Yuxin , Zhao Yanlong
Published: January 2025

Objective: To explore the mechanism of intimal hyperplasia (IH) in autogenous arteriovenous fistula (AVF) from the perspective of integrated traditional Chinese and western medicine. Methods: This study reviewed the relevant literature, analyzed and summarized the mechanism of intimal hyperplasia of autogenous arteriovenous fistula from the perspective of integrated traditional Chinese and western medicine. Conclusion: Intimal hyperplasia of autogenous arteriovenous fistula involves many aspects. From any Angle, it provides a theoretical basis for clinical research and further guides clinical practice.
Mechanism of Fucoxanthin on Ferroptosis of Human Erythroleukemia Cells Via the TFR1/SLC7A11/GPX4 Signaling Pathway
Authors: Wang Bi , Wang Siyu , Du Haofei , Yang Ziyan , Zhang Xiuqiang , Wang Caisheng , Ding Haomiao
Published: January 2025

Background: Fucoxanthin (Fx) is a natural carotenoid gaining widespread interest for its anticancer effects. However, the mechanism by which Fx treats leukemia remains unclear. Here, we studied the mechanism of Fx-induced ferroptosis in human erythroleukemia (HEL) cells. Methods: HEL cell viability after Fx treatment was assessed using an MTT assay. Mitochondrial membrane potential (MMP) and cell cycle distribution of HEL cells were determined using flow cytometry. Fucoxanthin's effect on reactive oxygen species (ROS), iron and ferrous ions in HEL cells was measured using assay kits. Expression levels of relevant proteins in HEL cells were assessed through western blot analysis. Molecular docking was performed to validate the interactions. Results: Fx significantly inhibited HEL cell viability both dose- and time-dependently. As the Fx concentration increased, the proportion of cells in the G0/G1 phase significantly increased, and MMP and cells in the S phase were notably decreased. Fx markedly promoted ROS, iron and ferrous ion expression levels; reduced GPX4 and SCL7A11 expression levels, and increased p53 and TFR1 expression levels. Molecular docking analysis revealed that the binding energy between Fx and GPX4, SCL7A11, p53 and TFR1 was less than −5 kcal/mol, primarily through hydrogen bonding at the enzyme's active site. Conclusion: Fx-induced ferroptosis of HEL cells may be related to activation of the TFR1/SLC7A11/GPX4 signaling pathway.
The Influence of Guqin Music on Loneliness and Psychological Alienation in Society
Authors: Li Wenqian , Lee Hoonjin
Published: January 2025

As an essential art form in Chinese traditional culture, guqin music, with its unique timbre and profound cultural connotations, plays a positive role in the field of mental health. In modern society, the fast-paced lifestyle and increasing social estrangement have made loneliness and psychological alienation more prevalent. This paper explores the influence of guqin music on social loneliness and psychological alienation through its aesthetic characteristics, psychological mechanisms, and social functions. It also discusses the application prospects of guqin music in social psychological therapy.