bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Magnetometer-Equipped Drone for Mineral Exploration in Mining Operations
Authors: Perikleous Dimitris , Margariti Katerina , Velanas Pantelis , Blazquez Cristina Saez , Garcia Pedro Carrasco , Gonzalez-Aguilera Diego
Published: December 2024

This study investigates the geological composition and material distribution within the Lavrion repository located in Greece through an aerial magnetometry survey using a novel aerial drone, CERBERUS, coupled with advanced data processing techniques. The deployment of drone-based magnetometry provided a high-resolution, non-invasive approach to capturing magnetic field data over complex and potentially hazardous terrain (soils highly contaminated), facilitating the rapid and precise mapping of the study area. As a final result, a 3D magnetic susceptibility model was developed, representing a detailed view of the magnetic susceptibility variations within the repository. This model enabled the comprehensive visualization of high-susceptibility zones associated with ferromagnetic materials and low-susceptibility zones correlating with diamagnetic materials like lead, arsenic, cadmium, and zinc. The combined methodologies underscore the effectiveness of drone-based aerial magnetometry in geophysical studies, highlighting its potential for mining exploration and waste management. This study demonstrates that the integration of drone technology with magnetic data processing offers a powerful tool for analysing subsurface structures in a safe, efficient, and non-invasive manner.
A Fully Controllable UAV Using Curriculum Learning and Goal-Conditioned Reinforcement Learning: From Straight Forward to Round Trip Missions
Authors: Kim Hyeonmin , Choi Jongkwan , Do Hyungrok , Lee Gyeong Taek
Published: December 2024

The focus of unmanned aerial vehicle (UAV) path planning includes challenging tasks such as obstacle avoidance and efficient target reaching in complex environments. Building upon these fundamental challenges, an additional need exists for agents that can handle diverse missions like round-trip navigation without requiring retraining for each specific task. In our study, we present a path planning method using reinforcement learning (RL) for a fully controllable UAV agent. We combine goal-conditioned RL and curriculum learning to enable agents to progressively master increasingly complex missions, from single-target reaching to round-trip navigation. Our experimental results demonstrate that the trained agent successfully completed 95% of simple target-reaching tasks and 70% of complex round-trip missions. The agent maintained stable performance even with multiple subgoals, achieving over 75% success rate in three-subgoal missions, indicating strong potential for practical applications in UAV path planning.
Strengthening Data Literacy in K-12 Education: A Scoping Review
Authors: Witte Verena , Schwering Angela , Frischemeier Daniel
Published: December 2024

Competent data handling is crucial for active and informed participation in modern society. To equip students for this challenge, data literacy must be strengthened throughout their K-12 education. This scoping review, conducted following PRISMA-ScR guidelines, aims to provide an overview of methods and approaches for enhancing specific sub-skills of data literacy and to identify research gaps. Analysis of 30 relevant papers reveals that, although various definitions and models of data literacy exist, most emphasize data analysis skills. This area is extensively covered in practical approaches, three times more than methods for planning and conducting independent data collection. This disparity highlights an imbalance in data literacy development and underscores the need to address under-represented sub-skills, in particular the actual data collection process. The review suggests a focus on project-based learning with real-world data and current issues as an effective method to balance out this disparity. Future research should explore and develop comprehensive approaches to teaching all aspects of data literacy, particularly those currently underemphasized.
Commentary: Personal Transformations and the Possibilities of Incremental Progress in Mathematics Teacher Professional Development
Authors: Arbaugh Fran
Published: December 2024

In this commentary, I challenge the field to take seriously the idea of incremental professional development (PD) for teachers of mathematics. I begin by briefly describing the history of PD in the United States since the publication of NCTM’s Curriculum and Evaluation Standards, what we have learned about effective PD through research, and then pose set of dilemmas we face in having a wide impact on the teaching of mathematics in the United States. Through reflecting on my story of incremental and transformative change as a high school mathematics teacher and 28 years as a mathematics teacher educator and scholar, I argue that the concept of incremental PD is worthy of exploration.
Integration of Generative Artificial Intelligence in Higher Education: Best Practices
Authors: Cordero Jorge , Torres-Zambrano Jonathan , Cordero-Castillo Alison
Published: December 2024

Generative artificial intelligence (GenAI) is transforming various sectors, including education. This study investigates the integration of GenAI in higher education, focusing on its potential to enhance teaching and learning. Through a series of workshops and courses delivered to university professors, it examines opportunities such as improved resource creation and challenges like ethical AI usage, proposing best practices for the sustainable implementation of GenAI in the classroom. The main objective is to analyze how the use of GenAI tools such as ChatGPT, Gemini, and Claude can improve teachers’ professional skills and the overall educational experience while ensuring ethical and responsible use. The methodology comprised a literature review and practical experimentation with university professors. Data collection involved observations, surveys, discussion forums, cooperative activities, and exercises focused on evaluating AI-generated educational resources and analyzing forum insights to identify best practices. The results highlight several opportunities around the use of GenAI in education, including improving writing, creating educational resources, supporting lesson planning, and increasing teacher productivity. In addition, significant challenges were identified, such as the ethical use of AI and strategies for detecting AI-generated text. For instance, workshops demonstrated a 30% increase in teacher confidence with GenAI tools like ChatGPT, highlighting the effectiveness of these technologies in professional development. To address these challenges, best practices for the responsible integration of GenAI in education are presented, focusing primarily on ongoing training, the establishment of institutional policies, the encouragement of responsible use, and the ongoing evaluation of impact in the educational setting. Best practices include clear ethical guidelines, prompt development techniques, and continuous professional training to ensure teachers can effectively and responsibly integrate GenAI tools into their instructional practices. These practices for the effective use of GenAI tools in education aim to maximize benefits while mitigating risks. These include the development of effective prompts for various activities and guidance on the ethical use of AI to ensure a balanced and responsible approach to the integration of GenAI in higher education.
The Influence of Student–Instructor Communication Methods on Student Engagement and Motivation in Higher Education Online Courses During and After the COVID-19 Pandemic
Authors: Deep Promethi Das , Chen Yixin , Ghosh Nitu , Rahaman Md. Shiblur
Published: December 2024

Effective communication between students and instructors is vital for student success. Traditionally, this communication has taken place in person within classroom settings. However, with technological advancements, online classes have become more common. The COVID-19 pandemic accelerated this shift, significantly disrupting traditional communication methods between students and instructors and transforming the educational landscape, particularly in higher education. We conducted a qualitative narrative literature review of the technological tools that emerged during the pandemic and explored how today’s instructors can effectively use them to enhance student engagement and motivation in online classrooms. The review utilized the SANRA (scale for the assessment of narrative reviews) guidelines to ensure the quality of the studies used. Twenty-two articles published within the last ten years were chosen based on their relevance to higher education and student–instructor communication. The articles were analyzed for effective and ineffective educational communication tools (e.g., Zoom or Google Classroom) utilized during the pandemic, focusing on what worked and what could be improved. The findings revealed that live video sessions were more effective than pre-recorded videos, voice-only sessions, or email/text communications in fostering student engagement and motivation.
Technology Leadership for Pandemic STEMgagement in Computer Science: A PK12 Case Study
Authors: Rodgers Devery J.
Published: December 2024

In this post-pandemic hybrid world of PK12 education, the onus is still on education leaders to close achievement gaps through equitable means. There are current socioeconomic, racial, gender, and geographical disparities that limit students’ full access to computer science education (CS). This case study reports how one urban PK12 school district in the United States is addressing the “leaky pipeline” with sustainable solutions for CS education with minoritized students. Using an online engagement framework, an ethnographic lens was used with document review, to conduct a content analysis of projects, programs, and services set up through the central office for nearly 20,000 students at the primary, middle grades, and secondary levels in computer sciences. Findings acknowledge leadership’s planning for student engagement in STEM+C (science, technology, engineering, math and computer science) for virtual instruction. This study will contribute to the burgeoning knowledge of leadership for CS activities in PK12, and serve as a beacon for learning organizations bolstering CS activities in the future.
The Mediating Role of School Refusal in the Relationship Between Students’ Perceived School Atmosphere and Underachievement
Authors: Sorrenti Luana , Caparello Concettina , Meduri Carmelo Francesco , Filippello Pina
Published: December 2024

Studies have shown that the school atmosphere perceived by students can play a key role in promoting their well-being and success in school. No study to date has analyzed whether the students’ perceived school atmosphere might contribute to school refusal (SR), which in turn might reduce students’ engagement and promote underachievement. A cross-sectional study was conducted with 528 Italian high school students (Mage = 16.08, SD = 1.38; 50.8% males, 47% females, and 2.3% not declared), with the aim of assessing the role of the mediation of SR (Anxious Anticipation, Difficult Transition, Interpersonal Discomfort, and School Avoidance) in the association between students’ perceived school atmosphere (Student Relations, Student–Teacher Relations, Educational Climate, Sense of Belonging, and Interpersonal Justice) and school engagement and underachievement. Data were collected using validated instruments, including the SChool REfusal EvaluatioN for school refusal, the Multidimensional School Climate Questionnaire for school atmosphere, and the Utrecht Work Engagement Scale for school engagement. To evaluate the association between variables, we performed structural equation modeling with latent variables. Mediation analysis indicated that Difficult Transition fully mediates the association between Sense of Belonging and school engagement (β = 0.20, p ≤ 0.05). This study extends the knowledge of school refusal behavior.
Control and Optimization of Hydrogen Hybrid Electric Vehicles Using GPS-Based Speed Estimation
Authors: Mansouri Nouha , Mnassri Aymen , Nasri Sihem , Ali Majid , Lashab Abderezak , Vasquez Juan C. , Guerrero Josep M.
Published: December 2024

This paper investigates the feasibility of hydrogen-powered hybrid electric vehicles as a solution to transportation-related pollution. It focuses on optimizing energy use to improve efficiency and reduce emissions. The study details the creation and real-time performance assessment of a hydrogen hybrid electric vehicle (HHEV)system using an STM32F407VG board. This system includes a fuel cell (FC) as the main energy source, a battery (Bat) to provide energy during hydrogen supply disruptions and a supercapacitor (SC) to handle power fluctuations. A multi-agent-based artificial intelligence tool is used to model the system components, and an energy management algorithm (EMA) is applied to optimize energy use and support decision-making. Real Global Positioning System (GPS) data are analyzed to estimate energy consumption based on trip and speed parameters. The EMA, developed and implemented in real-time using Matlab/Simulink(2016), identifies the most energy-efficient routes. The results show that the proposed vehicle architecture and management strategy effectively select optimal routes with minimal energy use.
Power Grid Violation Action Recognition via Few-Shot Adaptive Network
Authors: Meng Lingwen , Zhang Lan , Ban Guobang , Luo Shasha , Liu Jiangang
Published: December 2024

To address the performance degradation of violation action recognition models due to changing operational scenes in power grid operations, this paper proposes a Few-shot Adaptive Network (FSA-Net). The method incorporates few-shot learning into the network design by adding a parameter mapping layer to the classification network and developing a task-adaptive module to adjust the network parameters for changing scenes. A task-specific linear classifier is added after the backbone, allowing the adaptive generation of classifier weights based on the changing task scene to enhance the model’s generalizability. Additionally, the model uses a strategy of freezing the backbone network and iteratively updating only certain module parameters during training in order to minimize training costs. This approach addresses the challenge of iteratively updating difficulties in the original model, which are caused by limited image data following scene changes. In this paper, 2000 samples under power grid scenarios are used as the experimental dataset; the average recognition accuracy for violation actions is 81.77% for images after scene changes, which represents a 4.58% improvement when compared to the ResNet-50 classification network. Furthermore, the model’s training efficiency is enhanced by 40%. The experimental results show that the method enhances the performance of the violation action recognition model before and after scene changes and improves the efficiency of the iterative model by updating with a smaller sample size, lower model design cost, and lower training cost.
A Deep-Learning-Driven Aerial Dialing PIN Code Input Authentication System via Personal Hand Features
Authors: Wang Jun , Wang Haojie , Sato Kiminori , Wu Bo
Published: December 2024

The dialing-type authentication as a common PIN code input system has gained popularity due to the simple and intuitive design. However, this type of system has the security risk of “shoulder surfing attack”, so that attackers can physically view the device screen and keypad to obtain personal information. Therefore, based on the use of “Leap Motion” device and “Media Pipe” solutions, in this paper, we try to propose a new two-factor dialing-type input authentication system powered by aerial hand motions and features without contact. To be specific, based on the design of the aerial dialing system part, as the first authentication part, we constructed a total of two types of hand motion input subsystems using Leap Motion and Media Pipe, separately. The results of FRR (False Rejection Rate) and FAR (False Acceptance Rate) experiments of the two subsystems show that Media Pipe is more comprehensive and superior in terms of applicability, accuracy, and speed. Moreover, as the second authentication part, the user’s hand features (e.g., proportional characteristics associated with fingers and palm) were used for specialized CNN-LSTM model training to ultimately obtain a satisfactory accuracy.
Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults
Authors: Chan Meiling , Yu Ying , Chang Pohan , Chen Tsung-Yi , Wong Hok-Long , Huang Jian-Hua , Zhang Wiping , Chen Shih-Lun
Published: December 2024

With shifts in lifestyle and dietary patterns, obesity has become an increasing health issue among younger demographics, particularly affecting young adults. This trend is strongly associated with a heightened risk of developing chronic diseases, especially cardiovascular conditions. However, conventional health monitoring systems are often limited to basic parameters such as heart rate, pulse pressure (PP), and systolic blood pressure (SBP), which may not provide a comprehensive assessment of cardiac health. This study introduces an intelligent heart health monitoring system that leverages the Internet of Things (IoT) and advanced sensor technologies. By incorporating IoT-based sensors, this system aims to improve the early detection and continuous monitoring of cardiac function in young obese women. The research employed a TERUMO ES-P2000 to measure blood pressure and a PhysioFlow device to assess noninvasive cardiac hemodynamic parameters. Through precise sensor data collection, the study identified key indicators for monitoring cardiovascular health. Machine learning models and big data analysis were utilized to predict cardiac index (CI) values based on the sensor-derived inputs. The findings indicated that young obese women showed significant deviations in blood pressure (SBP and PP) and cardiac hemodynamic metrics (SVI, EDV, and ESV) at an early stage. The implementation of signal processing techniques and IoT sensors enhanced the CI prediction accuracy from 33% (using basic parameters like heart rate, PP, and SBP) to 66%. Moreover, the integration of extra sensor-based parameters, such as Stroke Volume Index (SVI) and Cardiac Output (CO), along with the use of color space transformations, successfully improved the prediction accuracy of the original data by 36.68%, increasing from 53.33% to 90.01%. This represents a significant improvement of 30.01% compared to the existing technology’s accuracy of 60%. These results underscore the importance of utilizing sensor-derived parameters as critical early indicators of cardiac function in young obese women. This research advances smart healthcare through early cardiovascular risk assessment using AI and noninvasive sensors.
VUF-MIWS: A Visible and User-Friendly Watermarking Scheme for Medical Images
Authors: Lin Chia-Chen , Lin Yen-Heng , Chu En-Ting , Tai Wei-Liang , Lin Chun-Jung
Published: December 2024

The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications.
GNSS/IMU/ODO Integrated Navigation Method Based on Adaptive Sliding Window Factor Graph
Authors: Ji Xinchun , Long Chenjun , Ju Liuyin , Zhao Hang , Wei Dongyan
Published: December 2024

One of the predominant technologies for multi-source navigation in vehicles involves the fusion of GNSS/IMU/ODO through a factor graph. To address issues such as the asynchronous sampling frequencies between the IMU and ODO, as well as diminished accuracy during GNSS signal loss, we propose a GNSS/IMU/ODO integrated navigation method based on an adaptive sliding window factor graph. The measurements from the ODO are utilized as observation factors to mitigate prediction interpolation errors associated with traditional ODO pre-integration methods. Additionally, online estimation and compensation for both installation angle deviations and scale factors of the ODO further enhance its ability to constrain pose errors during GNSS signal loss. A multi-state marginalization algorithm is proposed and then utilized to adaptively adjust the sliding window size based on the quality of GNSS observations, enhancing pose optimization accuracy in multi-source fusion while prioritizing computational efficiency. Tests conducted in typical urban environments and mountainous regions demonstrate that our proposed method significantly enhances fusion navigation accuracy under complex GNSS conditions. In a complex city environment, our method achieves a 55.3% and 29.8% improvement in position and velocity accuracy and enhancements of 32.0% and 61.6% in pitch and heading angle accuracy, respectively. These results match the precision of long sliding windows, with a 75.8% gain in computational efficiency. In mountainous regions, our method enhances the position accuracy in the three dimensions by factors of 89.5%, 83.7%, and 43.4%, the velocity accuracy in the three dimensions by factors of 65.4%, 32.6%, and 53.1%, and reduces the attitude errors in roll, pitch, and yaw by 70.5%, 60.8%, and 26.0%, respectively, demonstrating strong engineering applicability through an optimal balance of precision and efficiency.
Backdoor Federated Learning by Poisoning Key Parameters
Authors: Song Xuan , Li Huibin , Hu Kailang , Zai Guangjun
Published: December 2024

Federated learning (FL) utilizes distributed data processing to enable collaborative machine learning model development while safeguarding user privacy. However, the decentralized nature of FL, combined with data heterogeneity, substantially expands the attack surface for backdoor threats. Existing FL attack and defense strategies typically target the entire model, neglecting the critical backdoor parameters—a small subset of parameters that govern model vulnerabilities. Focusing on these parameters can replicate the impact of attacking the entire model while greatly reducing the risk of detection by advanced defenses. To address this challenge, we introduce Key Parameter Backdoor Attack in Federated Learning (KPBAFL), an innovative, adaptive, and scalable framework specifically designed to exploit model vulnerabilities by targeting critical backdoor parameters. KPBAFL integrates three core components: key parameter analysis, a beacon feedback mechanism, and adaptive attack strategies. By embedding beacons within the backdoor model, the framework can gather real-time attack feedback and dynamically adjust its strategy accordingly. When these components operate in concert, KPBAFL exhibits exceptional stealthiness, achieving an attack success rate (ASR) exceeding 96.5% while maintaining a benign task accuracy (BTA) of 97.8% across various datasets and models. Extensive experiments demonstrate its effectiveness, even in the presence of advanced defenses such as FLAME, Fldetector, Rflbat, and Deepsight, underscoring its strong generalizability. Although the modular design ensures adaptability, the framework’s performance may significantly degrade if the components are not properly synchronized. Our research provides a critical foundation for understanding and mitigating backdoor vulnerabilities in federated learning systems.
Observer-Based Robust H∞ Control for Stochastic Markov Jump Delay Systems Through Dual Adaptive Sliding Mode Approach
Authors: Deng Jianping , Meng Xin , Jiang Baoping
Published: December 2024

This study presents an approach to enhancing the robustness of H∞ control in Ito^-type stochastic Markov jump systems, addressing uncertainties in parameters, time-varying delays, and nonlinear perturbations. In this study, the nonlinearities are not exactly known, so an adaptive control strategy is employed. Firstly, an adaptive state observer of full dimension is constructed along with the derivation of error dynamics. Subsequently, different from traditional methods, two linear sliding surfaces are designed, respectively, for the state observer system and error dynamics, resulting in two sliding mode dynamics. Secondly, by employing the linear matrix inequality (LMI) method, sufficient conditions are established to ensure mean-square exponential stability of the closed-loop systems, including observer sliding mode dynamics and error sliding mode dynamics, along with an H∞ attenuation performance index γ. Thirdly, adaptive sliding mode controllers are proposed, ensuring the finite-time arrival and maintenance of the established sliding surfaces. Finally, the efficacy of the derived outcomes is illustrated utilizing the Tunnel Diode circuit model as a demonstrative case study. In this example, the system’s state responses, sliding surface functions, control input, and estimated parameters are simulated under different operating modes and external disturbances. The results demonstrate that the proposed adaptive sliding mode control strategy ensures faster and better convergence compared to error dynamics without control.
Text-Guided Object Detection Accuracy Enhancement Method Based on Improved YOLO-World
Authors: Ding Qian , Zhang Enzheng , Liu Zhiguo , Yao Xinhai , Pan Gaofeng
Published: December 2024

In intelligent human–robot interaction scenarios, rapidly and accurately searching and recognizing specific targets is essential for enhancing robot operation and navigation capabilities, as well as achieving effective human–robot collaboration. This paper proposes an improved YOLO-World method with an integrated attention mechanism for text-guided object detection, aiming to boost visual detection accuracy. The method incorporates SPD-Conv modules into the YOLOV8 backbone to enhance low-resolution image processing and feature representation for small and medium-sized targets. Additionally, EMA is introduced to improve the visual feature representation guided by the text, and spatial attention focuses the model on image areas related to the text, enhancing its perception of specific target regions described in the text. The improved YOLO-World method with attention mechanism is detailed in the paper. Comparative experiments with four advanced object detection algorithms on COCO and a custom dataset show that the proposed method not only significantly improves object detection accuracy but also exhibits good generalization capabilities in varying scenes. This research offers a reference for high-precision object detection and provides technical solutions for applications requiring accurate object detection, such as human–robot interaction and artificial intelligence robots.
Unveiling the Microbial Symphony of Amasi: A Targeted Metagenomic 16S rRNA, ITS, and Metabolites Insights Using Bovine and Caprine Milk
Authors: Ajibade Betty Olusola , Ajayeoba Titilayo Adenike , Sabiu Saheed , Moiseenko Konstantin V. , Mbona Sizwe Vincent , Cason Errol D. , Fedorova Tatyana V. , Ijabadeniyi Oluwatosin Ademola
Published: December 2024

Amasi, a traditional fermented milk produced in Southern Africa, is associated with several health benefits, such as probiotic activities, immune system modulation, and pharmacological (antimicrobial, antitumor and antioxidant) potential. This study investigated the microbial diversity in Amasi (produced from cow’s and goat’s milk) through targeted metagenomic bacterial 16S rRNA and fungal ITS sequencing, the metabolic functional prediction of Amasi samples using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) and profiled amino acids constituents using Liquid Chromatographic-Mass Spectrophotometry (LC-MS). The results obtained revealed Firmicutes, Bacteroidetes, and Proteobacteria as the most prevalent bacterial phyla, with Lactococcus and Lactobacillus being the most abundant genera. On the other hand, Ascomycota, Basidiomycota, and Mucoromycota were the main fungal phyla, while Aspergillus, Kazachstania, and Debaryomyces spp. dominated the fungal genera. Also, Pseudomonas spp., Bacillus spp., Clostridium spp., Cronobacter spp., Alternaria spp., Diaporthe spp., and Penicillium spp. were the probable pathogenic bacteria and fungi genera found, respectively. Atopobium, Synechococcus, and Parabacteroides were found less often as rare genera. It was found that the amino acid and drug metabolism pathway prediction values in Amasi samples were significantly higher (p < 0.05) than in raw cow and goat milk, according to the inferred analysis (PICRUSt). The amino acid validation revealed glutamine and asparagine values as the most significant (p < 0.05) for Amasi cow milk (ACM) and Amasi goat milk (AGM), respectively. Comparatively, ACM showed more microbial diversity than AGM, though there were relative similarities in their microbiome composition. PICRUSt analysis revealed significant metabolites in the two Amasi samples. Overall, data from this study showed heterogeneity in microbial diversity, abundance distributions, metabolites, and amino acid balance between raw cow/goat milk and Amasi samples.
Research on Fire Suppression Characteristics of Compressed Air Foams in Full-Scale 220 kV Converter Transformer
Authors: Guo Yike , Chen Tao , Zhou Biao , Zhang Peng , Wang Yuwei , Wang Xuyao , Hao Danping
Published: December 2024

To study the fire behavior of UHVDC (ultra-high-voltage direct current) converter transformers and the effectiveness of CAFs (compressed air foams) in suppressing fires, a full-scale model of a 220 kV converter transformer fire was constructed. The model mainly considered the oil pool fires and oil spill fires that form after explosions, causing the casing to completely fall out. The hot oil fire tests were conducted on the physical converter transformer. The fire suppression characteristics of the CAF system for converter transformer fires were studied. The temperature and changes in various locations of the fire model were analyzed under different foam supply strengths. The fire in a converter transformer is characterized by intense heat, high temperatures, and strong radiation. The highest temperature can exceed 1000 °C in cases of complete combustion. The fire in the converter transformer involves a dynamic oil spill and a large pool of oil, making it challenging to extinguish. The fire extinguishing performance and cooling effect of CAFs are outstanding. The recommended foam supply strength for the actual project is more than 8 L/(min·m2).
Investigating the Role of Food-Derived Peptides in Hyperuricemia: From Mechanisms of Action to Structural Effects
Authors: Han Yu , Liu Wanlu , Li Kexin , Zhang Mingzhen , Liu Xinqi , Li Lu , Guo Zhao , Li He
Published: December 2024

Hyperuricemia, a disorder of purine metabolism associated with cardiovascular disease, gout, and kidney disease, can be alleviated by food-derived peptides. However, the precise mechanisms remain unclear, hindering their development. This study reviews uric acid-lowering peptides from various sources, focusing on two pathways: inhibiting uric acid production and promoting excretion. Low-molecular-weight peptides (<1000 Da) exhibited superior uric acid-lowering effects. We further explored the relationships between amino acid composition and their target interactions. Peptides rich in cyclic amino acids (tryptophan, phenylalanine, and histidine) and containing small amounts of linear amino acids (leucine, cysteine, and glycine) demonstrated significant potential for lowering uric acid. These findings provide theoretical support for developing novel functional foods for the management of hyperuricemia.
Oil-in-Water Emulsions Made of Pistachio Oil: Physical and Chemical Properties and Stability
Authors: Ismaiel Lama , Rizzo Valeria , Di Mattia Carla , Fanesi Benedetta , Lucci Paolo , D’Alessio Giulia , Pacetti Deborah , Pittia Paola
Published: December 2024

Pistachio nuts are valued for their sensory qualities, nutritional benefits, and health-promoting properties. Pistachio oil has also gained interest for its bioactive compounds, though these are sensitive to processing and environmental stresses. While pistachio-based products are commercially available, little research has addressed the emulsifying properties of crude pistachio oil or its impact on the stability and bioactive profile of oil-in-water (O/W) emulsions. This study evaluated the emulsion-forming abilities of two commercial pistachio oils (PO1, PO2), their physical and oxidative stability during emulsification, and the effects of emulsifier concentration over short-term storage (7 days, 4 °C). O/W emulsions were prepared using 20% (w/w) oil and Tween 20 (0.5% and 1% w/w) in phosphate buffer and homogenized under high pressure. The emulsions were analyzed for particle size, fatty acid profile, antioxidants, and oxidative state. The results revealed differences in fatty acid composition, oxidative stability, and bioactive content between the oils and their emulsions. PO1 showed higher levels of bioactives compared to PO2. Emulsification increased the peroxide value of the oil phase, confirming its pro-oxidant effects. The results of this study demonstrate the potential of pistachio oil to create stable O/W emulsions rich in bioactives, offering new opportunities for healthy emulsified food products.
Dynamic Behavior and Fixed-Time Synchronization Control of Incommensurate Fractional-Order Chaotic System
Authors: Wang Xianchen , Wang Zhen , Dang Shihong
Published: December 2024

In this paper, an incommensurate fractional-order chaotic system is established based on Chua’s system. Combining fractional-order calculus theory and the Adomian algorithm, the dynamic phenomena of the incommensurate system caused by different fractional orders are studied. Meanwhile, the incommensurate system parameters and initial values are used as variables to study the dynamic characteristics of the incommensurate system. It is found that there are rich coexistence bifurcation diagrams and coexistence Lyapunov exponent spectra which are further verified with the phase diagrams. Moreover, a special dynamic phenomenon, such as chaotic degenerate dynamic behavior, is found in the incommensurate system. Secondly, for the feasibility of practical application, the equivalent analog circuit of incommensurate system is realized according to fractional-order time–frequency frequency domain algorithm. Finally, in order to overcome the limitation that the convergence time of the finite-time synchronization control scheme depends on the initial value, a fixed-time synchronization control scheme is proposed in the selection of synchronization control scheme. The rationality of this scheme is proved by theoretical analysis and numerical simulation.
Dubovsky’s Class of Mathematical Models for Describing Economic Cycles with Heredity Effects
Authors: Makarov Danil , Parovik Roman , Rakhmonov Zafar
Published: December 2024

The article is devoted to the study of economic cycles and crises, which are studied within the framework of the theory of N.D. Kondratiev long waves (K-waves). The object of the study is the fractional mathematical models of S. V. Dubovsky, consisting of two nonlinear differential equations of fractional order and describing the dynamics of the efficiency of new technologies and the efficiency of capital productivity, taking into account constant and variable heredity. Fractional mathematical models also take into account the dependence of the rate of accumulation on capital productivity and the influx of external investment and new technological solutions. The effects of heredity lead to a delayed effect of the response of the system in question to the impact. The property of heredity in mathematical models is taken into account using fractional derivatives of constant and variable orders in the sense of Gerasimov–Caputo. The fractional mathematical models of S. V. Dubovsky are further studied numerically using the Adams–Bashforth–Moulton algorithm. Using a numerical algorithm, oscillograms and phase trajectories were constructed for various values and model parameters. It is shown that the fractional mathematical models of S. V. Dubovsky may have limit cycles, which are not always stable.
A Study on the Existence, Uniqueness, and Stability of Fractional Neutral Volterra-Fredholm Integro-Differential Equations with State-Dependent Delay
Authors: Raghavendran Prabakaran , Gunasekar Tharmalingam , Ahmad Junaid , Emam Walid
Published: December 2024

This paper presents an analysis of the existence, uniqueness, and stability of solutions to fractional neutral Volterra-Fredholm integro-differential equations, incorporating Caputo fractional derivatives and semigroup operators with state-dependent delays. By employing Krasnoselskii’s fixed point theorem, conditions under which solutions exist are established. To ensure uniqueness, the Banach Contraction Principle is applied, and the contraction condition is verified. Stability is analyzed using Ulam’s stability concept, emphasizing the resilience of solutions to perturbations and providing insights into their long-term behavior. An example is included, accompanied by graphical analysis that visualizes the solutions and their dynamic properties.
Automatic Voltage Regulator Betterment Based on a New Fuzzy FOPI+FOPD Tuned by TLBO
Authors: Shouran Mokhtar , Alenezi Mohammed
Published: December 2024

This paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with the Fractional Order Proportional Integral Derivative (FOPID) technique and employs cascading control theory to significantly improve reliability and robustness. The unique control architecture, termed Fuzzy Fractional Order Proportional Integral (PI) plus Fractional Order Proportional Derivative (PD) plus Integral (Fuzzy FOPI+FOPD+I), integrates advanced control methodologies to achieve superior performance. To optimize the controller parameters, the Teaching–Learning-Based Optimization (TLBO) algorithm is utilized in conjunction with the Integral Time Absolute Error (ITAE) objective function, ensuring precise tuning for optimal control behavior. The methodology is validated through comparative analyses with controllers reported in prior studies, highlighting substantial improvements in performance metrics. Key findings demonstrate significant reductions in peak overshoot, peak undershoot, and settling time, emphasizing the proposed controller’s effectiveness. Additionally, the robustness of the controller is extensively evaluated under challenging scenarios, including parameter uncertainties and load disturbances. Results confirm its ability to maintain stability and performance across a wide range of conditions, outperforming existing methods. This study presents a notable contribution by introducing an innovative control structure that addresses critical challenges in AVR systems, paving the way for more resilient and efficient power system operations.
Analytical Solutions of Time-Fractional Navier–Stokes Equations Employing Homotopy Perturbation–Laplace Transform Method
Authors: Alqahtani Awatif Muflih , Mihoubi Hamza , Arioua Yacine , Bouderah Brahim
Published: December 2024

The aim of this article is to introduce analytical and approximate techniques to obtain the solution of time-fractional Navier–Stokes equations. This proposed technique consists is coupling the homotopy perturbation method (HPM) and Laplace transform (LT). The time-fractional derivative used is the Caputo–Hadamard fractional derivative (CHFD). The effectiveness of this method is demonstrated and validated through two test problems. The results show that the proposed method is robust, efficient, and easy to implement for both linear and nonlinear problems in science and engineering. Additionally, its computational efficiency requires less computation compared to other schemes.
The Effect of CaCl2 on the Gelling Properties of Pea Protein–Pectin Dispersions
Authors: Zhang Dan , Chen Da , Campanella Osvaldo H.
Published: December 2024

The effects of CaCl2 addition before (PreCa) or after (PostCa) heating pea protein–pectin dispersions on the formed gel’s rheological and microstructural properties were investigated. Isothermal titration calorimetry (ITC) revealed that CaCl2 bound both pea proteins and pectins through a spontaneous exothermic reaction and pectin exhibited a stronger binding affinity to CaCl2. In PreCa gels, low levels of CaCl2 (5 and 10 mM) increased the gel elasticity (increase in the storage modulus, G′) and their microstructural compactness. However, higher CaCl2 levels (15 and 25 mM) decreased gels’ elasticity, likely due to diminished hydrogen bonds formed in the cooling stage, resulting in gels with larger voids and fewer interconnections between the protein and pectin phases. In PostCa gels, their elasticity increased with the CaCl2 content, a rheological change associated with the formation of denser microstructures. The addition of 25 mM CaCl2 decreased β-sheet and increased α-helix and random coil structures. Hydrogen bonding and electrostatic and hydrophobic interactions contributed to gel formation and stability in both PreCa and PostCa gels, whereas disulfide bonds had negligible effects. This study highlights the role of CaCl2 in modulating pea protein–pectin gels’ properties and microstructures for the development of gel-like foods with diverse textures and mouthfeels.
Peptide YY and Glucagon-like Peptide-1 Secretion in Obesity
Authors: Wilbrink Jennifer , van Avesaat Mark , Stronkhorst Arnold , Troost Freddy , le Roux Carel W , Masclee Ad
Published: December 2024

Objective: The regulation of food intake is disturbed in obesity, possibly resulting from alterations in gut peptide secretion. We hypothesize that obesity is associated with attenuated systemic and tissue concentrations of the gut peptides PYY and GLP-1. Methods: A prospective single-center study in which we included 13 individuals with obesity (BMI 39.5 ± 2.8 kg/m2) and 11 lean individuals as controls (BMI 20.7 ± 1.2 kg/m2) matched for age and gender. We measured: (1) tissue concentrations and mRNA expression of GLP-1 and PYY in ileal and colonic biopsies taken during routine colonoscopy and (2) plasma concentrations of PYY and GLP-1 in response to a meal in the same group. Results: Plasma GLP-1 and PYY responses did not differ between individuals with obesity and lean controls. Neither were tissue concentrations and mRNA expression of both peptides different between both groups. Conclusions: Systemic and local PYY and GLP-1 concentrations in individuals with obesity do not differ from those in lean subjects.
Perspective on Leisure Sports Participation Among Older Adults in a Super-Aging Society: Focusing on Health Concern, Athletic Passion, Leisure Satisfaction, and Intent to Continue Participating in Leisure Sports
Authors: Jun Mun-Gyu , Choi Chulhwan
Published: December 2024

Background/Objectives: Today, society has a growing interest in healthy aging. This study analyzed health concerns, leisure satisfaction, athletic passion, and intention to continue participating in leisure sports of various age groups as well as the elderly, and we empirically analyzed the differences. Methods: This study conducted a survey of 306 adult men and women who regularly participated in leisure sports. Frequency, correlation, and difference analyses were performed using SPSS version 26.0. Results: The main findings indicated that older age groups showed higher levels of health concern, obsessive passion, and intention to participate in leisure sports than younger age groups, whereas younger age groups had higher levels of harmonious passion than older age groups. Conclusions: This study provides results through a comparative analysis that can indirectly enhance the quality of life by presenting practical alternatives to increase older adults’ participation in leisure sports activities amid the global aging trend.
Long-Term Results of Pars Plana Vitrectomy with Internal Limiting Membrane Peeling for Vitreomacular Traction Syndrome: A Prospective Investigation in Central Asian Population
Authors: Zhurgumbayeva Gulnar , Kyrykbayev Dastan , Ruslanuly Kairat , Binder Susanne , Kulmaganbetov Mukhit
Published: December 2024

Background: There is a lack of research on the clinical characteristics of vitreomacular traction (VMT) in the Central Asian population, which evaluates the visual recovery and macular hole closure outcomes of pars plana vitrectomy (PPV) with membrane peel in this population. Methods: This long-term prospective cohort study, conducted at the Kazakh Eye Research Institute from June 2015 to December 2021 with a follow-up period until December 2022, included 1574 patients (1784 eyes) with VMT syndrome. Among the eyes, 724 (40.58%) had VMT, 620 (34.75%) had a lamellar macular hole (LMH), and 440 (24.66%) had a full-thickness macular hole (FTMH). Results: The FTMH group consisted of small (≤250 μm) holes in 14 (3.2%) eyes, medium (250–400 μm) holes in 79 (17.9%) eyes, and large (>400 μm) holes in 347 (78.9%) eyes. Significant improvements in visual acuity, retinal thickness reduction, and macular hole closure were observed in 98.79% of patients with LMH and 81.14% of patients with FTMH at 1.5 months after surgery. PPV with membrane peel resulted in improved clinical outcomes, including enhanced visual acuity and macular structure. Conclusions: These findings support the existing research indicating the efficacy and relative safety of this surgical approach for VMT, despite the potential risks of postoperative complications.