bluuebunny/crossref_metadata_2025 huggingface dataset:
Positron emission tomography tracers for synucleinopathies
Authors: Xiang Jie , Zhang Zhentao , Wu Shengxi , Ye Keqiang
Published: January 2025

Synucleinopathies, such as Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy, are characterized by the aggregation of α-synuclein. Variations in brain distribution allow for differentiation among these diseases and facilitate precise clinical diagnosis. However, distinguishing between synucleinopathies and Parkinsonism with tauopathies poses a challenge, significantly impacting clinical drug development. Therefore, molecular imaging is crucial for synucleinopathies, particularly for clinical diagnosis, assessment of drug efficacy, and disease surveillance. In recent years, advances in molecular imaging have led to rapid development of α-synuclein-specific tracers for positron emission tomography (PET), most of which are still in pre-clinical stages. Interestingly, some of these tracers share similar compound skeletal structures and are currently undergoing optimization for clinical application. Despite this progress, there remain challenges in developing α-synuclein tracers. This review summarizes recent findings on promising PET tracers and discusses representative compounds’ characteristics while offering suggestions for further research orientation.
Performance Evaluation of Torque-Controlled Expansion Anchors with Improved Sleeve and Header Details
Authors: Hur Moo-Won , Chae Kyoung-Hun , Lee Hyun-Ho , Park Tae-Won
Published: January 2025

This study assesses improvements in the head and extension sleeve parts for a post-installed anchor. The sleeve and head details were proposed to enhance the structural performance of the post-installed anchor, and the optimal structural shape was determined through finite element analysis. The analysis results revealed that the anchor's performance was most efficiently improved when the sleeve length was 9.0 mm and the head length was 3.0 mm. In the model with these dimensions applied, the performance improved approximately 1.71 times compared to the existing model, validating the effectiveness of the proposed structural details. The improved pull-out strength test of anchor diameter M12 showed an increase of 1.25 times in normal-strength concrete and 1.28 times in high-strength concrete, with an embedded depth of 50 mm. The improved pull-out strength test of anchor diameter M16 showed that the pull-out strength increased by 1.42 times for normal-strength concrete and about 1.33 times for high-strength concrete. This research proposes a modified equation that reflects changes in the effective embedded depth and diameter. A comparison of the proposed equation with that of European Technical Approval Guideline (ETAG) showed that the correlation coefficient changed from 0.908 to 0.962, and the coefficient of variation changed from 18.9 to 10.4%, meaning that the proposed equation reflected the actual experimental values more accurately.
Asymmetries in factors influencing non-fungible tokens’ (NFTs) returns
Authors: Benedek Botond , Nagy Bálint Zsolt
Published: January 2025

The asymmetries of factors influencing the return of cryptocurrencies have already been well documented; however, in the case of NFTs, only information asymmetries and hedging properties related to asymmetries were studied. Therefore, the present study examines factors affecting NFT returns, from market-related factors (crypto-market index return and stock market index return) to the Amihud illiquidity ratio and Google search trends during different market conditions. The wavelet coherences-based methodology was applied separately during the boom, bust, normal, and turbulent periods identified by structural breakpoints. Based on 14 NFT projects between April 2019 and July 2022, results show two fundamental asymmetries influencing these NFT returns. First, there is an asymmetry in the behavior of the factors in different periods; second, there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.
Experimental and numerical investigation into pile spacing effects on the dynamic response of coastal pile foundation bridges considering current-wave-earthquake forces
Authors: Alsultani Riyadh , Karim Ibtisam R. , Khassaf Saleh I.
Published: January 2025

Pile spacing significantly influences the dynamic response of coastal pile foundation bridges, particularly under complex loading conditions. This study presents a novel methodology for optimizing the distribution of piles within a nine-pile cap and evaluates the effects of varying pile spacings on the dynamic fluid-structure interaction. An experimental investigation was conducted to assess the dynamic response of three scaled bridge specimens with pile spacings of 2.5D, 2.0D, and 1.5D (where D is the pile diameter) under combined current-wave forces during seismic events. The acceleration and displacement responses were measured using the innovative Reality Water Structure Earthquake Interaction Test (RWSEIT) system. The results revealed that reduced pile spacing (1.5D) led to a significant increase in both acceleration and displacement responses, indicating higher vulnerability under dynamic loading. In contrast, wider spacing (2.5D) demonstrated improved stability, with reduced displacement and acceleration, thereby enhancing the overall structural resilience. The analysis also highlighted the critical role of water depth, current velocity, and wave properties in modulating the dynamic response. Complementary 3D numerical models were developed using the Finite Element DIANA Software, and the numerical results closely matched the experimental findings, thereby validating the model’s accuracy. These insights contribute to more sustainable infrastructure design, aligning with global efforts to enhance the resilience and longevity of coastal structures while minimizing environmental impact. These findings provide crucial insights into the fluid-structure interactions of coastal bridges, offering practical guidance for the design and assessment of deepwater pile foundations under seismic and hydrodynamic forces.
Time Series Data Analysis and Modeling of Machine Learning Methods in Limb Function Assessment
Authors: Duan Xueying
Published: January 2025

In response to the current needs of patients with limb dysfunction, with the goal of safety, real-time, non-invasive, and intelligent rehabilitation assessment, and with limb dysfunction patients as the research object, the author uses intelligent perception technology to obtain rehabilitation data of patients, fully utilizing the advantages of the data itself, and is committed to achieving rehabilitation training and muscle fatigue assessment for limb dysfunction patients. The author developed an assessment model for limb function evaluation using the Dynamic Time Warping K-Nearest Neighbor (DTW-KNN) algorithm and a Long Short-Term Memory (LSTM) neural network-based evaluation model. Based on the experimental findings, it was demonstrated that DTW-KNN effectively categorizes and assesses the rehabilitation motions of upper limbs during elbow flexion under various completion scenarios. Patients have the flexibility to conduct independent and effective upper limb rehabilitation training at home using the upper limb functional rehabilitation system, without any constraints of time or space. By enabling physicians to promptly modify the rehabilitation plan, the system significantly addresses the limitations of conventional upper limb rehabilitation approaches, lowers the medical expenses associated with stroke upper limb rehabilitation, and helps mitigate the shortage of rehabilitation specialists. Utilizing the developed upper limb functional rehabilitation system, the author gathered a set of inertial sensing data on upper limb rehabilitation movements, showcasing prominent temporal features. Consequently, to address this issue, the author proposes the use of Long Short-Term Memory (LSTM) neural network - a recurrent neural network (RNN) with superior temporal data processing capabilities. Based on the multi-dimensional inertial sensing data collected by the upper limb rehabilitation system, the author constructs a recurrent neural network classification model. The model can accurately classify and evaluate different types of upper limb rehabilitation movements under varying completion scenarios. The experimental results indicate that: the overall classification accuracy of DTW-KNN for elbow flexion, elbow flexion and forearm abduction, and shoulder flexion in upper limb rehabilitation movements is 71.8%, 47.9%, and 68.8%, respectively. It was observed that the classification accuracies of LSTM neural network model were 98.2%, 93.3%, and 95.1%, respectively. This marks a notable improvement in the classification accuracy of LSTM neural network model compared to DTW-KNN, with an increase of 26.4%, 45.4%, and 26.3%, respectively. LSTM has a significant advantage over DTW-KNN in terms of classification time, with less classification time.
A Perspective Study on Scalable Computation Model for Skin Cancer Detection: Advancements and Challenges
Authors: Kavitha N. , Prasad N. Sivaram , Sujeeth T. , Naresh Palem , Swathi R. , Sujith A.V.L.N. , Reddy P Dileep Kumar
Published: January 2025

In the realm of scalable computing, the quest for early detection of skin cancer takes on a new dimension, demanding robust and efficient algorithms capable of handling vast amounts of data. This article delves into the burgeoning field of intelligent computing, where scalable solutions are imperative for processing the multitude of skin lesion images generated daily. Leveraging cutting-edge deep learning and machine learning techniques, researchers strive to develop automated systems capable of swiftly analyzing lesion features like symmetry, color, size, and shape.Through a comprehensive literature review, this paper explores the strides made in skin lesion detection, focusing on scalable computing approaches that accommodate the growing volume of medical imaging data. By identifying significant contributions in classification and segmentation methods, the article not only sheds light on the latest advancements but also offers guidance for aspiring researchers navigating the complexities of skin lesion analysis. Ultimately, the fusion of scalable computing and intelligent algorithms holds promise in revolutionizing early detection efforts, potentially saving countless lives by swiftly identifying and treating skin cancer at its onset.
An Investigation into the Evaluation and Optimisation Method of Environmental Art Design based on Image Processing and Computer Vision
Authors: Wang Hui
Published: January 2025

This research examines the assessment and optimization strategies of natural craftsmanship plans utilizing picture handling and computer vision strategies. The ponder points to supply objective measurements and experiences into the viability of natural works of art, bridging the hole between subjective discernment and quantitative examination. Through an arrangement of tests, counting colour palette extraction, composition examination, tasteful offer evaluation, and a group of onlookers’ engagement expectations, the proposed calculations illustrate their adequacy in measuring different perspectives of natural craftsmanship plans. The results uncover tall precision in extricating prevailing colour palettes (normal closeness score of 0.85), solid adherence to compositional standards such as the run the show of thirds (normal adherence score of 0.75), and a tall relationship coefficient of 0.82 between anticipated and human-assigned stylish appraisals. Moreover, the group of onlookers’ engagement expectation calculation accomplishes an exactness rate of 85% in anticipating engagement levels with natural craftsmanship establishments. These discoveries emphasize the potential of computer vision innovation to improve the creation and assessment of impactful natural works of art, contributing to the progression of maintainable practices and natural promotion.
Memory, Channel and Process Utilization for Fuzzy based Congestion Detection and Avoidance Scheme in Flying Ad Hoc and IoT Network
Authors: Sahare Mahendra , Maheshwary Priti
Published: January 2025

UAVs are flying in the air at different speeds and continuously forwarding the collected information to other UAVs or IoT devices in FANET. UAVs are playing an important role in data collection from places where humans can’t reach them easily. The UAVs are intelligent devices, and these devices have sufficient bandwidth and memory for data forwarding and storing. The role of UAVs is specific, and they have the reflexibility to change the battery and control the data interval to control the congestion in network. The IoT devices with FANET can transfer the valuable data to other IoT devices for verification and matching. The proper utilization of bandwidth, memory, energy and processing capability are able to increase the Quality of Service (QoS) in FANET. In this paper, proposed the Memory, channel and Process utilization for Fuzzy based (MCPFB) for congestion detection and avoidance scheme to improve bandwidth utilization, energy consumption in FANET with the IoT network. primarily aims to identify and prevent network congestion, which is crucial for maintaining the QoS requirements and ensuring reliable communication. Congestion is a phenomenon that arises when the volume of data transmitted across a network exceeds its capacity. These factors can lead to disruptions, reduced efficiency, and potential data loss in communication networks such as Flying Ad Hoc Networks (FANETs). To effectively handle congestion in FANET and provide reliable communication in challenging and dynamic environments, it is crucial to employ efficient resource management, intelligent algorithms, and adaptable protocols. The process of designing fuzzy rules for Flying Ad Hoc Networks (FANET) entails developing a set of guidelines that utilize fuzzy logic to make decisions pertaining to different parts of the network. The MCPFB is better than the previous BARS approach in terms of different performance metrics.
Designing an Intuitive Human-Machine Interface for a Skin Cancer Diagnostic System: An Ensemble Learning Approach
Authors: Akella Prasanna Lakshmi , Kumar R
Published: January 2025

advanced computational models for practical clinical application. This study introduces a refined Ensemble Learning-Based Decision Support System, designed with an emphasis on intuitive HMI for accurate melanocytic and non-melanocytic skin cancer diagnosis. We present ”EffiViT,” a model that synergizes EfficientNet’s robust feature extraction capabilities with the Vision Transformer’s attention-based contextual understanding, tailored through an interface that prioritizes ease of use and interpretability for medical professionals. Through extensive evaluation on the ISIC 2019 benchmark dataset, EffiViT demonstrated a classification accuracy of 99.4%, coupled with superior performance in specificity and area under the ROC curve. The system’s interface design was iteratively refined based on feedback from dermatologists, focusing on clear visualization of diagnostic information, straightforward navigation, and efficient access to model interpretations. Our findings underscore the importance of integrating user-centered design principles in the development of diagnostic tools, highlighting how a well-conceived HMI can enhance the adoption and effectiveness of AI-based systems in clinical settings. The proposed system stands out not only for its diagnostic accuracy but also for its contribution to the realm of HMI, offering insights into designing interfaces that facilitate better decision-making and ultimately improve patient outcomes in the field of dermatology.
Construction of an Agricultural Training Effectiveness Assessment Model Based on Big Data
Authors: Pan Guangshi , Guo Mei
Published: January 2025

This research presents an Agricultural Training Effectiveness Assessment Model (ATEAM) leveraging enormous information analytics methods to assess the adequacy of agrarian preparing programs. By coordinating different information sources counting member socioeconomics, and preparing substance, and relevant components, ATEAM gives an all-encompassing system for evaluating preparing adequacy. Through tests and comparative examinations, ATEAM illustrates prevalent prescient precision, clustering quality, and by and large adequacy assessment compared to conventional strategies and related works. Particularly, ATEAM accomplishes an exactness rate of 87.3%, an Outline Score of 0.72 for clustering, and a Mean Squared Error (MSE) of 0.012 for member fulfilment rating expectation. This model empowers partners to create data-driven choices for program optimization and asset assignment, contributing to feasible rural advancement and upgraded nourishment security. The study underscores the transformative potential of huge information analytics in rural preparation, highlighting the significance of leveraging progressed analytics strategies to address complex challenges and drive positive results.
Secure Digital Data Visual Sharing Schemes in Multi-Owner Public Cloud Environment Applications
Authors: Chekka Ratna Babu , Babu B Ravindra
Published: January 2025

The use of cloud computing for storing and processing cyber data via the internet has grown widespread in the field of cyber security. Ensuring the secure sharing of cyber data, including texts, images, audio, and video, over the cloud is paramount. However, cloud computing encounters significant challenges concerning cyber data security, authentication, and privacy. The prevailing approaches to data security encounter challenges such as the generation of intricate keys, intensive computation for large keys, and susceptibility to attacks by intruders. Scalability presents its own set of obstacles in maintaining cyber data privacy and ensuring secure communication. A significant privacy concern arises from the frequent changes in membership and data sharing among multiple owners. This paper introduces a novel approach to secure data sharing within data centres by proposing a scheme that employs Private Key Dynamic Visual Cryptography (PK-DVC), Multiple Key Encryption Visual One-Time Pad (MK-VOTP), and visual steganography for encoding and decoding. MK-VOTP is used for data encryption, facilitating data owners' encryption via utilising their identity with supplementary security features. Subsequently, the encrypted data is kept in the cloud, guaranteeing heightened security protocols. Visual steganography is employed for further authentication purposes. Decrypting the original data requires users who fulfil the encrypted properties. This increases security and reduces critical size, allowing several authenticated owners to share data without conflicts. Combining all shares recreates the original picture. The approach described in the paper sounds innovative and addresses several key challenges in ensuring secure data sharing within data centers, particularly in the context of cloud computing. The proposed scheme main components are Private Key Dynamic Visual Cryptography (PK-DVC), Multiple Key Encryption Visual One-Time Pad (MK-VOTP), and  Visual Steganography. These three components plays important role in further authenticating the encrypted data or providing additional security measures beyond encryption. By combining these techniques, the proposed scheme aims to provide robust security for sharing cyber data in cloud environments. The use of dynamic keys, multiple encryption layers, and steganography can enhance data security, making it challenging for intruders to access or decipher sensitive information.
Peptide Sequence Tag Extraction by Graph Convolution Neural Networks
Authors: Bian XinYe , Xie DongMei , Zhang DI , Xie XiaoYu , Feng Yuyue , Zhou Piyu , He Changjiu , Lv Mingming , Wang Haipeng
Published: January 2025

The peptide sequence tag extraction method plays a vital role in tandem mass spectrometry-based protein identification engines. This approach faces two significant challenges in practical applications: first, the issue of fixed tag lengths, where shorter tags lack sufficient specificity, leading to an excessive recall of non-target peptide sequences, and longer tags experience a reduction in precision as tag length increases, potentially failing to recall target peptide sequences; second, the sensitivity and precision of tag extraction remain relatively low. To address these issues, a variable-length peptide sequence tag extraction algorithm, TagEx, based on graph convolutional networks, is proposed. This method begins by training a de novo peptide sequencing scoring model utilizing graph convolutional networks. It then constructs a spectral peak connection graph from the mass spectrum, employing a depth-first search strategy to extract variable-length peptide sequence tags, with the trained graph convolutional network model scoring amino acid connections during the extraction process.Finally, tags are filtered based on length and scoring to obtain the final candidate peptide sequence tag set. To evaluate TagEx’s performance, it was benchmarked against three representative tag extraction software tools: InsPect, PepNovo+, and DirecTag. The experimental results demonstrate that TagEx exhibits superior sensitivity, coverage, and precision, with improvements of 0.62-2.32, 3.22-11.14, and 3.29-8.31 percentage points, respectively, when retaining the top 100 tags.
Deep Learning-driven Skin Disease Diagnosis: Advancing Precision and Patient-Centered Care
Authors: Mehboob Amna , Bennour Akram , Abid Fazeel , Chodhri Emad , Rasheed Jawad , Alsubai Shtwai , Ghabban Fahad Mahmoud
Published: January 2025

Skin diseases are in the middle of the most prevalent conditions, arising from a myriad of factors including viral infections, bacteria, allergies, and fungal pathogens. Appropriate detection of these conditions is essential for effective treatment and management. Further, Deep learning methods are employed to enable early-stage detection, with a particular emphasis on the pivotal role of feature extraction in the classification process. This research emphasizes the significance of a patient-centered approach, aiming to provide responsible and effective solutions for skin diagnoses. In pursuing more accurate and timely skin condition diagnoses, we turn to deep learning techniques, leveraging the HAM10000 dataset. Initially, we perform different prepossessing techniques on selected datasets to handle class imbalance and a Convolutional Neural Network and fine-tune hyperparameters such as with or without Dropout, CW, FL, and Using Global Average Pooling. Our technique excels in distinguishing diverse skin, Gender, localization, and Cell types with reliable evaluation metrics such as precision, recall, FI Score, and specificity. Our technique not only subsidizes the healthcare field but also underscores the potential of advanced technologies in enhancing early skin disease detection and medical decision-making.
An Intelligent Monitoring System for Sports Mental Health Status based on Big Data
Authors: Han Youliang , Geng Wenguang
Published: January 2025

This provides an effective way to break away from traditional inefficient evaluation methods for monitoring and analyzing the psychological status of a large number of school sports athletes, the author proposes an intelligent monitoring system for sports psychological health status based on big data. Applying big data technology to mental health assessment, using real-time monitoring and analysis of athlete unified EEG waves, dividing athlete EEG waves into frequency bands, and conducting mental health analysis. The author validated the effectiveness of the system through simulation experiments, and the results showed that the psychological states of the subjects were not the same during the early and fatigue stages of training. In the early stages of training, the brainwave frequency band was mainly in the Beta and Gamma bands, accounting for 37% and 41%, respectively. Concentration was greater than relaxation, while in the fatigue stage of homework, the brainwave frequency band was mainly in the Delta and Thata bands, accounting for 43% and 45%, respectively, and concentration was less than relaxation. The psychological monitoring system designed by the author can provide a technical foundation for a series of strategies to promote training efficiency while ensuring the mental health of athletes.
The Factory Supply Chain Management Optimization Model based on Digital Twins and Reinforcement Learning
Authors: Zhao Xinbo , Wang Zhihong
Published: January 2025

This paper introduces the "digital twin" to solve the problem of material allocation and real-time scheduling in the warehouse site. This project intends first to establish mathematical modeling based on a digital twin unmanned warehouse and dynamically optimize materials in the unmanned warehouse by combining visual analysis and deep reinforcement learning. Then, a security sharing mechanism of digital twin-edge network data based on blockchain fragmentation is proposed. For twin models with time-varying characteristics, a multi-node adaptive resource optimization method such as multipoint cluster selection, local base station consistent access selection, spectrum and computational consistency is constructed. This is done to maximize blockchain business processing power. A two-layer near-end strategy optimization (PPO) algorithm is proposed to solve the adaptive resource optimization problem. Experiments have proved that this method can significantly improve the overall processing power of the blockchain. In addition, this method is more adaptable than conventional deep reinforcement learning.
The Personalized Learning Paths for Digital Media Technology Education based on Big Data
Authors: Peng Changrong , Li Qi , Zhang Xiaodong , Sha Haiyan
Published: January 2025

The paper intends to study the evolution of domain knowledge by studying the spatial-temporal collaborative model. A joint knowledge network model based on the time-space domain is proposed to represent the knowledge base. The skeletal clustering algorithm analyzes the evolution of knowledge networks over the years. According to the concept of the evolution process of knowledge, the paper makes a connection and path analysis of its evolution track. An empirical study of the digital media field is carried out. The results show that the algorithm proposed in this paper can extract the evolution trajectory of domain knowledge that varies with year. The path of knowledge evolution can show the correlation between research topics, hot topics, core literature, the evolution law of research topics and research methods of multiple disciplines, and the cross-characteristics of multiple disciplines.
Construction of Teacher Learning Evaluation Model based on Deep Learning Data Mining
Authors: Zhou Shiyu , Li Deming
Published: January 2025

In-class teaching assessment, which measures the effectiveness of teachers’ instruction as well as how well students are learning in a classroom setting, is becoming more and more important in monitoring, and advancing the quality of education. As artificial intelligence (AI) advances quickly, the idea of intelligent instruction has gradually gotten better and progressively permeated every facet of educational application. The integration of artificial intelligence (AI) technology into the assessment of in-class instruction has grown into a research hotspot due to the prevalent role that classroom instruction plays in primary and undergraduate education. Modern educational systems aim to improve instruction effectiveness and customize learning opportunities for each student. In this paper, we provide a novel model for evaluating teacher learning that makes use of data mining and deep learning capabilities. The objective of the model is to analyse and interpret the intricate patterns present in educational data to offer a thorough evaluation of teacher effectiveness and student advancement. The model uses convolutional neural networks (CNNs) to mine large datasets, such as student comments, lesson plans, classroom interactions, and performance measures, to find important pedagogical indications that are associated with effective teaching outcomes. The effectiveness of the concept is confirmed in a range of educational contexts, indicating its scalability and flexibility. Its use in practical settings shows a notable increase in the accuracy of teacher assessments, offering a clear path forward for ongoing progress in education.
Energy and Deadline Aware Workflow Scheduling using Adaptive Remora Optimization in Cloud Computing
Authors: Srivastava Vidya , Kumar Rakesh
Published: January 2025

Cloud computing has become a more popular and well-known computing paradigm for delivering services to different organizations. The main benefits of the cloud computing paradigm, including on-demand services, pay-as-per-use policy, rapid elasticity, and so on, make cloud computing a more emerging technology to lead with new methods. Cloud systems have become more challenging than other systems because of their wide range of clients and the variety of services in the system. The cloud data center consists of many physical machines (PM) with virtual machines (VM), load balancers, switches, storage etc. Because of the inappropriate use of resources and inefficient scheduling, these data centers consume a lot of energy. In this paper, a multi-objective optimization model called Adaptive Remora Optimization (AROA) is proposed, which comprises sub-models viz; priority calculation, task clustering, probability definition and task-VM mapping using search mode based on Remora optimization to optimize energy consumption and execution time. CloudSim is used for the implementation of the proposed optimization technique. Through simulation the energy consumption is 0.695kWh and the execution time is 179.14sec. The result obtained by AROA is compared with the existing approaches to prove the efficacy of the proposed approach.Experimental results show that the proposed AROA algorithm outperforms the existing approaches.
Potential benefits of GLP-1 analogues and metformin in patients with osteoarthritis
Authors: Skowronek Anna , Piekarska Martyna , Dąbek Katarzyna , Ochwat Michał , Sudoł Maria , Mierzwa Gabriela , Kajtel Aleksandra , Skowronek Tomasz
Published: January 2025

Introduction and purpose
Osteoarthritis (OA) is the most prevalent chronic joint disorder. It is the primary cause of chronic pain. Known risk factors for OA include obesity and type 2 diabetes mellitus (T2DM). One of the challenges in managing OA is the absence of disease-modifying treatments. Since obesity is an independent risk factor of OA, the use of medications like GLP-1 analogs and metformin may benefit patients with OA. The purpose of our article was to provide an overview of the findings from studies on the use of the mentioned drugs in OA patients.
 
Materials and methods
To write this article, databases such as PubMed and Google Scholar were searched using the following terms: osteoarthritis, glucagon-like peptide-1 (GLP-1), obesity, anti-obesity medications.
 
Description of the state of knowledge
Research on the effects of GLP-1 analogs in OA patients has yielded inconclusive results. Some studies showed that semaglutide provided benefits in reducing knee osteoarthritis symptoms in obese, nondiabetic patients. For those with knee OA and T2DM, GLP-1 receptor agonists lowered body weight and pain intensity. Furthermore, patients treated with GLP-1 receptor agonists have a reduced risk of knee surgery and lower cartilage loss velocity. Patients receiving semaglutide who underwent arthroplasty procedures experienced lower risks of prosthetic infections and hospital readmissions. The use of metformin was associated with reduced rates of joint replacement surgery.
 
Conclusions
GLP-1-based therapies and metformin, due to their pleiotropic effects, may provide advantages by alleviating joint inflammation and facilitating weight loss in OA patients. Additional research is required to validate these outcomes and identify the most effective treatment approaches for managing OA.
Factors Affecting Secondary School Students’ Interest to Learn Mathematics: A Review of Literature
Authors: Habimana Sylvain , Bizimana Benjamin , Maniraho Jean Francois
Published: January 2025

This study delved into the factors affecting secondary school students’ interest to learn Mathematics. The aim was to gather insights that can inform strategies aimed at enhancing students' engagement, enthusiasm, and achievement in Mathematics education. Literature information was downloaded using databases such as Google Scholar, ERIC, Search 4 Life, Scopus, Web of Science, and Academia. Of the 129 studies obtained, 117 articles were retained after removing duplicates and studies that did not meet the themes of the study. Further filtering of studies by removing primary and higher learning school-related studies allowed the retention of 25 relevant pieces of research published between 2000 and 2024. The results from the systematic reviews analysis showed that instructional strategy, instructional materials, the importance of Mathematics, a future career in Mathematics, students’ attitudes towards Mathematics, students’ enjoyment of Mathematics lessons, teachers and parental support, and students’ perception towards Mathematics, are amongst the key factors affecting positively secondary school students’ interest to learn Mathematics.
Hydroponic-Based Wastewater Treatment for Reuse: A Comparative Assessment of System Efficiency and Performance
Authors: Haddad Marwan
Published: January 2025

This study assesses the performance and applicability of various pilot-scale hydroponic systems for the dual purpose of treating domestic wastewater and supporting agricultural food production. A comparative analysis of water quality improvement, based on removal efficiencies for TSS, TDS, BOD, COD, TN, and TP, identified the NFT and vertical gradual flow hydroponic systems as the most effective among the five systems evaluated. These systems demonstrated superior contaminant removal rates while promoting plant growth with targeted nutrient additions and addressing key challenges such as salinity and heavy metal accumulation. Key insights revealed improved crop productivity in specific contexts: faba beans and green peas thrived in NFT setups, while musk melons exhibited resilience to salinity levels up to 3000 ppm, making them suitable for saline regions. Additionally, the ability of certain crops to hyperaccumulate heavy metals offers a phytoremediation pathway, further enhancing the environmental benefits of hydroponic systems. Hydroponic setups were shown to produce high agricultural yields with minimal environmental impact, adaptable to both urban and rural contexts. A comprehensive SWOT analysis underscored the NFT system's significant strengths, including sustainability, cost-effectiveness, and environmental health benefits, while identifying minimal weaknesses and threats. Conversely, container and vertical flow systems demonstrated limited applicability due to higher operational challenges and fewer advantages. These findings support the NFT hydroponic system as a robust, scalable solution for wastewater treatment and agricultural production, with significant potential for continuous research, technological innovation, and broader application across diverse environmental and agricultural scenarios.
Crossing Borders, Opening Minds? VET Tandem Partnerships in a Dutch- German Cross-Border Region
Authors: Wilde Stephanie , Kremer Hugo , Stelzer Jana
Published: January 2025

Purpose: The purpose of this paper is to discuss the potential contribution of cross-border vocational education and training (VET) exchange partnerships within the context of tandem exchanges between VET institutions in the Rhine-Waal Euregio region in the Netherlands and Germany. These tandem exchanges offer learners and teachers in VET the opportunity to develop new perspectives on their training pathways, the labour market and learning approaches in each context respectively. This occurs through the discussion and debate ignited by the exchanges, as well as through visits to relevant VET institutions, companies and other institutions in the border region. 
Approach: The approach taken in the paper uses both documentary analysis of a long-standing cross-border VET network, Ler(n)ende Euregio, as well as an interview-based study. Group interviews were held with teachers and school leadership teams from three tandem partnerships in North-Rhine Westphalia and the Netherlands, namely in the Rhine-Waal border region. The interviews revealed aspects of the motivation for the tandems and the challenges involved. 
Findings: The findings showed that learning opportunities for the students and the teachers are considerable and include: Gaining an insight into their vocational training pathway in the neighbouring country, and thereby reflecting on their own VET experience; developing social, intercultural and linguistic competences. This occurs particularly because of the switch between host and guest modes. Further, such exchanges can play a role in tackling prejudice in both cross-border contexts. Challenges faced by the tandem partnerships include: Maintaining continuity in the participating VET institutions and the teachers responsible for the exchanges and overcoming logistical and bureaucratic hurdles. Greater recognition of cross-border exchanges through certification for learners and dedicated time on the curriculum could be positive steps. 
Conclusion: The paper concludes that tandem partnerships in the Dutch-German border region offer a sustainable and accessible addition to the palette of mobilities available to learners and, significantly, also to teachers, in VET. The potential, particularly for learners who may not otherwise travel abroad during their training, is considerable, and is arguably relevant to other cross-border regions and international contexts.
The Role of Higher Education Through the Eyes of Hungarian Undergraduate Students and Graduates: A Qualitative Exploratory Study
Authors: Kocsis Zsófia , Pusztai Gabriella
Published: January 2025

Context: Most education systems equip graduates with the professional knowledge and cognitive skills needed to enter the world of work. However, there are other competencies also important for young graduates to become successful employees. Due to the growing uncertainty that characterizes the labour market, the demand for non-cognitive skills (communication, collaboration, critical thinking etc.) that support employee development potential and better integration has increased significantly. However, higher education institutions often transmit a culture that is different from that of workplaces. The aim of our study is to explore the role of the examined higher education institution in preparing students for the labour market. Our long-term goal is to support institutional development on the basis of this data. 
Methods: To answer these questions, semi-structured inteviews were conducted. Qualitative research methods were preferred, which offer the possibility of uncovering hidden mechanisms. The selection criterion for participants (n=18) was that they had obtained their diploma within the last three years, while, for the other portion of the interviewees, a selection criterion was that they were full-time students. The participants are students of the same rural higher education institution. To ensure the validity and reliability of the research results, personal triangulation was validated using the intercoding technique, which is suitable for a priori coding. 
Results: The results confirmed that higher education institutions provide a good foundation for entering to the labour market, but the development of broad competencies is less advanced. Frequent reference was made to the dominance of theory at the expense of practical orientation. Additionally, participants emphasized that they did not see relevance of the course material and its future applicability to the labour market, and, in some cases, they mentioned problems relating to the attitudes of instructors and the quality of the training provided for the students. A portion of the undergraduate students who work (in addition to being in school) believe that they acquired the most important skills during student employment and not during their higher education studies. 
Conclusions: One of the challenges for higher education is to prepare students to meet employers' needs by developing students’ competencies. During their years in higher education, students should be equipped with a set of competencies that will ensure their integration into the labour market. Our current research contributes to this goal by mapping the competencies, and highlighting where there is room for improvement which can contribute to graduates' success at work.
Self-Regulated Learning Skills in Vocational Students Based on Their Self-Evaluation
Authors: Sirk Meidi
Published: January 2025

Context: The importance of both self-regulated and self-directed learners has been emphasised in lifelong learning. However, in theory, self-regulated learning (SRL) serves as a basis for becoming a self-directed learner. Therefore, SRL (which includes certain skills) needs to be developed in vocational education and training alongside specialist training in order to meet the demands of the labour market, where employees should be capable of continuous learning. Previous studies based on the experiences of vocational teachers, and mainly focusing on adolescent students, have highlighted that many vocational students have low levels of SRL skills. However, there is a lack of research exploring student perspectives on their own SRL skills. It is unclear what factors influence their SRL and what support they need for developing SRL. The aim of this article is to analyse and describe the differences in the SRL skills of vocational students based on their self-evaluation and the factors related to their SRL. 
Methods: The author conducted diverse quantitative analyses of the responses of 244 students to achieve the research aim. First, a factor analysis was performed on the five skill areas of SRL, followed by a K-means cluster analysis based on SRL skills factors. Then SRL clusters of students were analysed according to the students' socio-demographic factors, learning difficulties, and other learning factors using cross-tabulation and variance analysis. 
Findings: From an SRL perspective, the most important factors in distinguishing and characterising the clusters of students were the skills related to plan management and organisation. On this basis, the study identified three clusters, consisting of students with: 1) Difficulties in organisation and behaviour (30%), 2) difficulties in planning and time management (20%), and 3) good and balanced SRL skills (50%). 
Conclusion: Overall, the study found that half of the students exhibited good SRL skills and these students encountered fewer learning difficulties. They also studied more through collaborative learning activities, received sufficient verbal feedback, and showed higher levels of motivation towards their schoolwork. However, the remaining students require additional support to improve their SRL skills.
Realizing Decent Work in a "Sandwich" Position: Assessing VET Trainers' Working Conditions in Times of Multiple Transformations
Authors: Nicklich Manuel , Blank Marco , Pfeiffer Sabine
Published: January 2025

Context: The German vocational training and education (VET) system is designed to cope with change. Within this system, organizational VET trainers (OVETT) are the main persons of reference for apprentices in the organization, in most cases for the whole three-year training period. Whether training can help to realize or maintain decent work also depends on whether the trainers themselves have decent working conditions. 
Approach: The empirical foundation for our analysis of the situation of OVETTs and the challenges they face in transformative times is a recently conducted study. Using a mixed-methods research design, consisting of 28 qualitative interviews and an online survey completed by 1,004 organizational VET trainers, this study analyzes the working conditions of German VET trainers and assesses possible effects on decent work. 
Findings: We show that although there is a willingness on the part of OVETTs to handle change and transformation, they are not sufficiently involved in their companies' strategic decisions. Our data shows that some OVETTs perceive themselves as being in a potentially influential position. Many, however, report that they are only the object of change instead of participating and contributing their competencies and opinions. The data shows the extent to which the work of vocational trainers is affected by stress, conflicting demands and a lack of support. This is particularly true for part-time trainers who, in addition to their normal work, are also responsible for the success of the apprentices and the entire VET system. However, the high intrinsic motivation of trainers enables them to fulfil their tasks and meet the high expectations placed on them. 
Conclusions: A special aspect of this is the "sandwich" position of OVETTs, who face transformative, societal demands, and challenges from both below (i.e., from the apprentices) and above (i.e., from management). At the same time, there is a lack of structural and operational resources. Organizational changes expose them more directly to certain challenges, but do not give them a chance to help shape their own conditions. Although the VET trainers are accustomed to mastering different requirements and interests, in transformative times new challenges arise and intensify the already heavy burden on VET personnel. If these are not addressed and VET work is not organized as decent, this will also jeopardize the quality of the vocational training system in the medium term.
Nathacha Appanah’s Ecologies of Violence
Authors: Walters Elly
Published: January 2025

Abstract: In the words of posthuman feminist Stacy Alaimo, ‘all creatures, as embodied beings, are intermeshed with the dynamic, material world’. In this article I study such intermeshing of embodied beings and material worlds in the work of the Mauritian French writer Nathacha Appanah (b. 1973). With a focus on Rien ne t’appartient (2021), I interrogate the ways in which ecological metaphor is used to conceptualize the protagonist’s experience of sexual abuse and forced abortion. I follow the vegetal and elemental imaginaries employed to depict these sexual and reproductive violences, which form part of Appanah’s ‘ecologies of violence’ in the novel.
Accidental Death of Satire: Dario Fo’s La Tragedia di Aldo Moro and the End of a Theatrical Aesthetics
Authors: Angelucci Malcolm , Kolsky Stephen
Published: January 2025

Abstract: This article problematizes the current periodization of Dario Fo’s and Franca Rame’s theatrical career. Through an analysis of the unfinished Tragedia di Aldo Moro , it argues that at the end of the 1970s Fo’s formula of political theatre as a ribaltamento , an overturning of tragedy to produce satire, fails to grasp the increasingly complex political situation, thus losing its influence and audience, well before the end of the mass revolutionary movement in the early 1980s. Fo’s theatre ‘dies’ in 1978, only to be rescued by a shift in style and themes in the monologues for and with Franca Rame.