bluuebunny/crossref_metadata_2025 huggingface dataset:
Social Media Communication & Information Adoption: An Implication of Luxury Accommodation Consumption Behavior Among Consumers
Authors: Suryo Hadi Wira Prabowo
Published: February 2025

Introduction: Tourism plays a huge role in boosting Indonesia’s economy. Bali stands out as an attractive tourist centre. Bali's tourism industry has experienced tremendous development, especially in the consumption of the luxury accommodation market.
Objectives: Therefore, this research is important to find out what factors influence consumer behaviour in the luxury accommodation market.
Methods: Respondents were exclusively selected and after data collection, a total of 457 responses could be used as the research sample. Data were evaluated using Partial Least Squares Structural Equation Modelling to explore the direct impact and moderator impact. This finding shed light on the fact that argument quality and source credibility have a positive effect on luxury accommodation consumption, UGC (User-Generated Content) and FGC (Firm-Generated Content) moderate the association between source credibility, argument quality, and luxury accommodation consumption.
Results: This finding shed light on the fact that argument quality and source credibility have a positive effect on luxury accommodation consumption, UGC (User-Generated Content) and FGC (Firm-Generated Content) moderate the association between source credibility, argument quality, and luxury accommodation consumption.
Conclusions: The providers of luxury accommodations need to enhance the quality of information or arguments they communicate and improve the credibility of their information sources. It is necessary for luxury accommodation providers to increase the likelihood of users providing information to other users, such as by providing a forum platform discussing their stay experiences, thus influencing other consumers and potential consumers.
Blockchain Integration in Information Systems: Transforming Data Security and Transaction Transparency
Authors: Harish Gurram
Published: February 2025

Blockchain technology has emerged as a robust, decentralized framework that offers enhanced data security, transparency, and immutability. Its integration within information systems addresses longstanding challenges in traditional centralized architectures, including data vulnerabilities, transaction inefficiencies, and the risk of single points of failure. This research paper explores the fundamental principles of blockchain, its core components, and the methodologies for integrating it into modern information systems. We present real-world use cases, demonstrate empirical data on adoption rates, and discuss potential limitations and future prospects. Diagrams, tables, and graphs are used to illustrate conceptual models, adoption trends, and performance evaluations. Our findings suggest that blockchain, when integrated effectively, can significantly transform data security and transaction transparency, paving the way for a more reliable, traceable, and efficient digital ecosystem.
Evaluation of Key Performance Indicators (KPIs) for Enhancing Efficiency, Sustainability, and Operational Optimization in Renewable Energy Management using Artificial Intelligence and Large Language Models
Authors: Athar Javed Ali
Published: February 2025

The integration of Large Language Models (LLMs) within renewable energy systems presents an innovative approach to optimizing energy efficiency, enhancing sustainability, and improving operational performance (Bai, J., Wang, Y., Chen, Y., et. al. 2021). Despite their potential, a clear methodology for evaluating the success of LLM implementations remains underdeveloped. This paper introduces a structured framework for evaluating Key Performance Indicators (KPIs) tailored to LLM applications in the renewable energy sector. The framework systematically addresses the assessment of LLM-driven improvements in energy forecasting accuracy, grid management, predictive maintenance, and resource optimization (Dasgupta, I., Lampinen, A. K., et. al. 2022). Critical KPIs include reductions in energy consumption during LLM training and inference, the accuracy of energy demand predictions, the optimization of renewable energy resource utilization, and the minimization of carbon footprints (Piantadosi, S. 2023). By establishing this framework, the paper provides a robust tool for measuring the impact of LLM technologies on both operational efficiency and sustainability outcomes. The study’s findings offer valuable insights for policymakers, researchers, and industry stakeholders to guide the responsible and effective integration of AI-driven solutions in renewable energy infrastructures.
Transformers (BERT) Based Framework for Web Recommendations Using Sentiment-Enriched Web Data
Authors: Supriya Saxena
Published: February 2025

Web mining and Natural Language Processing (NLP) play a crucial role in modern recommendation systems, enhancing accuracy and personalization by leveraging user-generated content. This research proposes a novel framework integrating Transformers, specifically BERT (Bidirectional Encoder Representations from Transformers), to detect fake reviews and perform sentiment analysis. Traditional recommendation techniques, such as collaborative and content-based filtering, fail to capture nuanced user sentiments, leading to suboptimal results. The proposed model refines web-based recommendations by filtering out fake reviews and extracting sentiment-enriched insights, ensuring more reliable predictions. The system undergoes extensive evaluation using machine learning algorithms, including K-Nearest Neighbors, Multinomial Naïve Bayes, Logistic Regression, Random Forest, AdaBoost, and XGBoost, with BERT demonstrating superior performance. Experimental results highlight an impressive accuracy of 94.34% for fake review detection and 94.78% for sentiment classification, outperforming conventional models. The integration of sentiment-driven web mining enhances recommendation accuracy, mitigates misleading feedback, and improves user trust. This study underscores the potential of Transformer -powered sentiment analysis in refining recommendation systems for e-commerce and other digital platforms.
Application of Machine Learning Algorithmic Models for the Authentication of Albanian Mono-Cultivar Olive Oils
Authors: Ardiana Topi
Published: February 2025

Distinguished for its nutritional benefits and high economic value, olive oil has faced issues with adulteration and fraud. As production increases, the need to identify olive oil by specific cultivars and regions has become more pressing. Analyzing chemical data related to the origin and olive cultivars will facilitate advanced quality control and authenticity practices for Albanian olive oil, enhancing its competitiveness as an organic product. While traditional empirical methods have been relied upon to detect olive oil fraud and evaluate quality, this study pioneers a modern approach using machine learning algorithms to differentiate authentic products from counterfeits. Establishing effective mechanisms and best practices to trace product origins and quality indicators will raise awareness about the risks of adulteration to both consumers' health and the broader food industry. To enhance the precision of origin predictions, data pre-processing steps—especially the normalization process following the isolation of independent features from the target variable—are crucial for distance-based algorithms like kNN, which improve accuracy. Furthermore, performance metrics for all algorithms were evaluated, including k-Nearest Neighbors, Logistic Regression, Support Vector Machines, implementation hyperparameter tuning techniques, and the best-performing model. Applying supervised machine learning methods to categorize Albanian Olive Oils (OO) according to their chemical composition aids in identifying their geographical and cultivar origin. Our results indicate an accuracy of 88.88%, constrained by the limitations of the current dataset; however, we intend to expand the dataset in the future.
Enhancing Privacy and Security in Decentralized Healthcare with Federated Learning and Blockchain
Authors: Priti Shukla
Published: February 2025

The adoption of Industry 4.0 technology in healthcare has led to increased concerns about data privacy, security, and interoperability. Traditional centralized healthcare systems are vulnerable to cybercriminal and unauthorized access; thus there is a need for secure and privacypreserving data sharing architecture. This work proposes an integration of Federated Learning (FL) with Blockchain to deliver improvements in the security, scalability, and privacy of decentralized healthcare. Florida allows hospitals to train their models locally and share them without divulging raw patient data, whereas Blockchain ensures data security, decentralized identity management, and safe access control through smart contracts. Although providing such high-level security requires the usage of advanced techniques such as Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Proof-of-Stake (PoS) consensus, the high protection level of data is definitely on the huge-scale advantages of blockchain technology. The proposed model is in line with Industry 4.0 concepts that support automation, interoperability, and strong data ecosystems in healthcare. The resolve is to reduce security threats ensure regulatory compliance and exchange healthcare data with the highest standards of security. Experimental results show improved security, privacy, and efficiency, which make this solution a scalable and robust alternative for current decentralized healthcare data management in Industry 4.0.
A Novel Neural-Based Design of Graphene and Molybdenum Disulfide Magnetic Tunnel Junction
Authors: Swapnali Makdey
Published: February 2025

Magnetic tunneling junction (MTJ) research is currently growing dramatically due to the progress of spintronic devices. Due to the varying electrode and tunneling behaviors, it is critical to conduct a more accurate analysis of MTJ behavior also MTJ has limited behavior analysis research. To accurately analyze the behavior of MTJ a novel design of Graphene and Molybdenum Disulfide Magnetic Tunnel Junction is proposed. Initially, Graphene–MoS2–Graphene MTJ is designed, in this, graphene acts as the electrode and MoS_2 acts as the tunneling junction, which gains a better efficiency score, high TMR ratio, and current transmission range. Furthermore, to predict the magnetic and electrical properties of the designed MTJ, the intelligent behavior analysis strategy known as the radial basis magnetic behavior framework (RBMBF) is implemented, which makes the computations faster and provides better characterization. To analyze the implemented MTJ, it is developed in the MATLAB environment and trained on the RBMBF. Finally, the calculated metrics are compared to those of standard MTJs, and improvement scores are recorded. The intended model has a better transmission coefficient of 1.7 and a TMR ratio of 586% than the previous models.
Modeling Channel Dynamics in Molecular Communication Systems
Authors: Ashwini Katkar
Published: February 2025

Molecular Communication (MC) is a field that merges concepts from biology, chemistry, and information theory to enable information transfer using molecular signals. This work introduces a mathematical model for MC systems to deepen the understanding of key principles and improve nanoscale system design and optimization. The model addresses essential aspects, including molecular diffusion, advection, degradation, and channel impulse response in bounded environments. It investigates the effects of advection, where fluid flow influences concentration gradients and degradation processes, which impact signal robustness and duration. Both bounded and unbounded environments are analyzed to understand the dynamics of molecular concentration, with a focus on molecular confinement and channel conditions.  Additionally, the study derives a channel impulse response equation essential for evaluating system performance. Simulation results offer insights into molecular concentration behaviors, advancing the understanding of MC systems.
Economic Efficiency of Innovative Development in Production Enterprises Based on the Use of Energy Resource-Saving Systems in the Context of Digitization: An Applied Nonlinear Analysis Perspective
Authors: Olena Druhova
Published: February 2025

The aim of this study is to explore the economic efficiency of innovative development in production enterprises through the implementation of energy resource-saving systems, particularly in the context of digitization. This research employs a mixed-methods approach, utilizing quantitative data analysis through software tools such as SPSS and qualitative interviews with industry experts. Surveys were conducted among production enterprises to gather insights on energy-saving practices and their economic impacts. The study found that enterprises implementing energy resource-saving systems experienced a significant reduction in operational costs, improved productivity, and enhanced sustainability metrics. The data indicated an average cost savings of 15% and a productivity increase of 20% post-implementation. To further analyze these findings, the study incorporates Applied Nonlinear Analysis to model the complex relationships between energy resource-saving systems, operational efficiency, and productivity outcomes. This analytical framework allows for a deeper understanding of how nonlinear interactions among various factors influence economic performance in production settings. The findings suggest that the integration of energy resource-saving systems not only contributes to economic efficiency but also aligns with broader sustainability goals. The study concludes that digitization plays a crucial role in optimizing these systems. This research is beneficial for stakeholders in the fields of industrial engineering, environmental management, and corporate sustainability. This study introduces a comprehensive framework for assessing the economic impacts of energy resource-saving systems in production enterprises, contributing new insights into the intersection of digitization, sustainability, and nonlinear analytical methods.
Al-Driven Predictive Maintenance in Shipyards: Enhancing Project Management Efficiency and Operational Cost Reduction through Statistical, Data-Driven Strategies for MRO Services
Authors: Shri. Vijaya Kumar Chava, Vijaya Geeta Dharmavaram
Published: February 2025

A detailed investigation of shipyard operations was conducted during a laborious internship at Hindustan Shipyard in Visakhapatnam. The research uses cutting-edge methodologies to investigate shipyard operations, providing new perspectives on procedures not before examined in similar industrial settings. The study provides novel equipment maintenance and monitoring approaches by integrating real-time sensor technology, statistical analysis, Deep Learning/Machine Learning algorithms, and cutting-edge data analytics. Using these techniques, maintenance plans can be improved, prospective problems can be predicted, and equipment reliability, cost reduction and operating efficacy can all be increased. These advancements significantly improve project management efficiency by streamlining workflows, shortening turnaround times, and enabling proactive decision-making. The research employs data-driven methodologies to analyze operational parameters, detect anomalies, and predict maintenance needs, reducing emergency repairs and operational disruptions. The findings underscore the critical role of Al in modernizing shipyard Maintenance, Repair, and Overhaul (MRO) services, offering a scalable solution for cost-effective, efficient maritime operations.
Reflecting on 20 Years of Regional Development Planning in Indonesia: A Systematic Literature Review
Authors: Muhammad Faried Wajdy
Published: February 2025

Introduction: Regional development planning in Indonesia, as mandated by several regulations, requires the implementation process to refer to various planning approaches, including technocratic, participatory, political, top-down, and bottom-up. These approaches are guided by principles such as holistic-thematic, integrative, and spatial. However, these complex procedures have hindered the achievement of 20-year planning goals. Challenges such as overlapping administrative procedures, the growing influence of regional legislative bodies, mandatory central government policies, increasing budget allocations for villages, declining public trust in the planning process, unclear authorities and responsibilities, and other regional autonomy issues necessitate a comprehensive reflection and re-evaluation of the regional development planning process.
Objectives: This study aims to comprehensively examine the process and results of regional development planning implementation in various regions in Indonesia, focusing on the effectiveness of the musrenbang (development planning deliberation) forum.
Methods: The research employs a Systematic Literature Review (SLR) by identifying, evaluating, and interpreting research on the implementation of regional development planning in Indonesia. Data collected from reputable journals are analyzed using the Atlas.ti application to map various evidence.
Results: The findings reveal that the regional development planning process in Indonesia through the musrenbang forum has not fully addressed the aspirations and needs of citizens. The government dominates every phase, including proposing and filtering aspirations, which limits effective dialogue and consultation with citizens.
Conclusions: The study concludes that the lack of communication, dialogue, and consultation between the government and citizens undermines the effectiveness of regional development planning. The researcher recommends further studies using the communicative planning method, especially focusing on marginalized groups, to enhance inclusivity and participatory governance.
The Development of Teacher Pedagogy in the Use of Artificial Intelligence for Language Learning of Pupils with Special Educational Needs in the Blind Category
Authors: Nor Lailatul Azilah Hamdzah
Published: February 2025

The integration of artificial intelligence (AI) in education has transformed pedagogical approaches, particularly for students with special educational needs (SEN), including those who are blind. This review article explores the development of teacher pedagogy in employing AI tools for language learning among blind pupils. By examining existing literature, it highlights effective strategies, challenges faced by educators, and potential solutions to enhance language acquisition through AI. The findings underscore the importance of teacher training and the need for adaptive instructional strategies to foster an inclusive learning environment.
Exploring AI's Impact on Student Active Participation: Strategies for Engagement in Virtual Flipped Classrooms
Authors: Siti Noor Aneeis Hashim
Published: February 2025

The integration of Artificial Intelligence (AI) into educational frameworks has transformed traditional pedagogical approaches, particularly within virtual flipped classrooms. This review article examines AI's influence on student active participation by analyzing current literature from 2014 to 2025. It explores AI-driven strategies that enhance engagement, personalized learning, and collaborative interactions in virtual flipped learning environments. The findings suggest that AI technologies, such as intelligent tutoring systems and adaptive learning platforms, significantly contribute to improved student outcomes. However, challenges related to implementation, ethical considerations, and the need for teacher training persist. The article concludes with recommendations for effectively integrating AI to foster active participation in virtual flipped classrooms.
Datafication and Cultural Heritage Collections Data Infrastructures: Critical Perspectives on Documentation, Cataloguing and Data-sharing in Cultural Heritage Institutions
Authors: Belteki Daniel , Rees Arran J. , Sichani Anna-Maria
Published: February 2025

The role of cultural heritage collections within the research ecosystem is rapidly changing. From often-passive primary source or reference point for humanities research, cultural heritage collections are now becoming integral part of large-scale interdisciplinary inquiries using computational-driven methods and tools. This new status for cultural heritage collections, in the ‘collections-as-data’ era, would not be possible without foundational work that was and is still going on ‘behind the scenes’ in cultural heritage institutions through cataloguing, documentation and curation of cultural heritage records. This article assesses the landscape for cultural heritage collections data infrastructure in the UK through an empirical and critical perspective, presenting insights on the infrastructure that cultural heritage organisations use to record and manage their collections, exploring the range of systems being used, the levels of complexity or ease at which collections data can be accessed, and the shape of interactions between software suppliers, cultural heritage organisations, and third-party partners. The paper goes on to include a critical analysis of the findings based on the sector’s approach to ‘3s’, that is standards, skill sets and scale, and how that applies to different cultural heritage organisations throughout the data lifecycle, from data creation, stewardship to sharing and re-using.
Continuous caudal anesthesia in children undergoing surgery: A systematic review of retrospective interventional studies
Authors: Kurniyanta I. Putu , Senapathi Tjokorda Gde Agung , Katarina Ida
Published: February 2025

Currently, surgery in children is optimally managed using a multimodal approach aimed at reducing opioid use. Some studies suggest that epidural anesthesia is the gold standard, but it still has limitations due to its short duration with a single injection. Therefore, the latest approach is continuous caudal anesthesia. However, there are currently no systematic review studies on this approach. This research will focus on the administration method and outcomes of continuous caudal anesthesia in children undergoing surgery. We conducted a literature search to compare the effects of continuous caudal anesthesia in pediatric patients based on the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) from 2014 to 2024. Data extracted from the studies included study type, sample size, type of surgery, body weight, age, intervention, initial dose, maintenance dose, catheter location, rescue anesthesia, induction time, postoperative pain assessment, and complications. The risk of bias was assessed using the cross-sectional/prevalence study quality scale. This study included five research studies with a total sample of 142 patients. Different types of surgery and analgesia were used in these studies. All of the studies showed improvement in pain after surgery. Complications such as difficulty breathing, transient unilateral motor weakness, dural tap, bleeding into the tap, and catheter leakage were more commonly related to anesthesia technique and dosage. The continuous caudal epidural approach can be considered a standard anesthesia technique, particularly in children undergoing specific thoracic and abdominal surgeries.
The association between EMS system characteristics and post-hospital discharge patient outcomes
Authors: Nevin Jon
Published: February 2025

Objective: Research in the field of EMS (emergency medical services) has been primarily limited to individual aspects of EMS systems and patient care. However, there is a need for a broad outcome-based analysis of the impacts of the overall system design. The aim of this study was to explore the associations between various EMS system design characteristics and patient outcomes post-hospital discharge.

Methods: Data indicators were obtained according to the characteristics of 30 US EMS systems in large metropolitan areas, which account for 41.5% of the US population. Examples of indicators were the density of unit deployment, the use of ALS (advanced life support) first response, and the accreditation status of the dispatch center. Data were compared with local outcome/mortality data from national healthcare databases. Partial least squares structural equation modeling was used to identify relationships between the variables.

Results: Of the nine latent variables studied, representing 29 individual indicators, local Medicare hospital rating scores (-.366 coefficient, p < 0.001, CI: -.485,-.134) and underlying socioeconomic and public health status (-.334, p < 0.001, CI: -.474, -.138) were the primary latent variables that showed practical significance (overall model R
2  = .736). Outcome measures described the relationship between the variables but cannot isolate EMS as the sole causal factor in outcomes.

Conclusions: None of the EMS system-specific characteristics of the variables studied were associated with decreased patient mortality. The only two variables that showed a practically significant outcome were the indicators related to socioeconomic and public health data and the Medicare ratings of local hospitals.
Effect Of Yoga Study On Academic Performance In Yoga And Health Education Among Students’ Teachers
Authors: Ravikumar P. , Kannan Dr. B. , Meenakshi Dr. R.
Published: February 2025

The article focuses on assessing facts of an effect of yoga study on academic performance in yoga and health education among students’ teachers. The current study's total sample consists of (Narang, 2017) 60 students and teachers. Using a random sampling technique, data was collected from students and teachers at a B.Ed. college in Theni District. A questionnaire related to attitude towards yoga education was developed and standardized by the investigators and was used to collect responses from the students. This entire study is depended on Experimental Method. Among this Experimental Method, a “complete experimental simple equivalent group pre-test-post test experiment design” was applied for data assortments. T-test was calculated to find out significance difference between mean score and testing of the hypothesis; with the help of SPSS package. The finding of the present study results points out that there is no significance difference between control group and experimental group student’s teacher in their academic performance in Yoga and Health Education with respect to pretest. In the present study significant differences were found in between control group and experimental group students teacher in their academic performance in Yoga and Health Education with respect to progressive test. The finding of current study results shows that there is significance difference between control group and experimental group students teacher in their academic performance in Yoga and Health Education with respect to post test.
The Category of Case in the First Grammar Books of the Bulgarian Language
Authors: Tilev Encho
Published: February 2025

The subject of research in this article are three grammar books of the Bulgarian language from the Bulgarian revival – “Bolgarska grammatika” by Neofit Rilski, “Slavenobolgarskoe detovodstvo” by Neofit Bozveli and Emanuil Vaskidovich and “Grammatika slaveno-bolgarska” by Hristaki Pavlovich. The purpose of the study is to trace how the case category is described in them. The initial hypothesis is that in all three grammar books there is a strong influence of Russian grammar books from the 18th and 19th centuries. This is proven in the course of the research, and when describing the case, the authors follow the language patterns in Greek, Church Slavonic and Russian. This classification of cases strongly influenced further grammatical works until the end of the 19th century.
Study of Cognitive Strategies for Self-regulated Learning of Students in their Professional-pedagogical Training
Authors: Topolska Evgeniya
Published: February 2025

The ability to self-regulate learning is a condition, a prerequisite for in-depth professional and practical training of students - future teachers. It allows even young specialists to navigate in a new environment, independently study, analyze and apply new information, to be adaptable and competitive. In this sense, the study of the essence and conditions under which this ability is stimulated in the learning environment is a step towards improving the learning process in higher education. The study in this article has a specific practical focus, namely: to study preferred cognitive strategies for self-regulated learning of students – future teachers. The results obtained will be reflected in subsequent planning of the learning environment.
Surface properties and susceptibility to staining of a resin composite after brushing with different whitening toothpastes
Authors: Barros Aline da Silva , Barbosa Carolina Meneghin , Scatolin Renata Siqueira , Vieira Junior Waldemir Francisco , Ferraz Laura Nobre
Published: February 2025

Objectives: This study investigated the effects of different whitening toothpaste (WT) on the surface properties and staining susceptibility of a resin composite.Methods: Cylindrical samples were prepared with a micro-hybrid resin composite and were randomized into groups according to the toothpaste (n = 12): distilled water (DW), regular toothpaste (RT), WT with silica + pyrophosphate (WT-S/P), WT with pentaphosphate and pyrophosphate (WT-P/P), WT with hydrogen peroxide and pyrophosphate (WT-HP/P) and WT with charcoal and pyrophosphate (WT-Ch/P). The samples were brushed for 825 cycles in an automatic brushing machine, simulating 30 days of brushing. After that, an immersion in coffee (10 mL/sample) was performed for 30 minutes for 30 days. The analyses of color, surface microhardness (SMH), and surface roughness (Ra) were performed at the initial time, after brushing with toothpaste and after immersion in coffee. The ΔL*, Δa*, Δb*, ΔEab, Δand E00 values were calculated comparing after toothpaste with initial time and after coffee with after toothpaste. Data were analyzed using a mixed linear model for repeated measures (SMH), Kruskal-Wallis, Dunn, Friedman, and Nemenyi tests, with α = 0.05.Results: For ΔL*, the WT-Ch/P group had the lowest values and differed from the other groups comparing the after toothpaste with the initial time interval (p < 0.001). The WT-Ch/P group had the lowest SMH values in after-toothpaste time (p < 0.001). In after-toothpaste time and after coffee time, the WT-S/P group had the highest Ra values and differed from the groups except the WT-Ch/P group (p < 0.001).Conclusions: The toothpaste composition affects the surface characteristics and susceptibility to staining of the resin composite. The charcoal-based toothpaste had the worst performance for the color analyses and SMH.
São José do Paricá: território e corpo de resistência na floresta amazônica
Authors: Torres Iraildes Caldas
Published: February 2025

São José do Paricá é uma comunidade tradicional localizada no município de Maués, no Amazonas. Sua formação social e cultural é vinculada à diáspora de grupos de seringueiros que fugiram dos seringais, para se libertarem da opressão e da sevícia do trabalho forçado e explorado sob o jugo do aviamento. Trata-se de uma comunidade formada no contexto das CEBS – Comunidade Eclessiais de Base da Igreja Católica, cujo processo socioeducativo contribuiu para que seus moradores possuam consciência de classe bem definita. Esta pesquisa recebeu o auxílio financeiro da FAPEAM – Fundação de Amparo à Pesquisa do Estado do Amazonas e assumiu o propósito de realizar um inventário sobre esta comunidade que é referência em soberania alimentar com certificação de alimentos orgânicos, além de ser grande produtora de guaraná também com selo de certificação. A metodologia assumiu as orientações das abordagens qualitativas, com inspiração da etnografia. Os resultados mostram que São José do Paricá possui grandes lideranças da luta política em defesa da floresta amazônica, especialmente na localidade do rio Urupadi. Pode-se, concluir, reafirmando o fato de que esta é uma comunidade de resistência política, cujos corpos de embates políticos são aqueles dos próprios moradores.
Prima Donna in the Jungle: Opera, Class, and Race in RKO Pictures’ Hitting a New High (1937)
Authors: Bombola Gina
Published: February 2025

This article interrogates why critics panned RKO's Hitting a New High, an operatic musical starring French soprano Lily Pons in the role of an aspiring prima donna who assumes the guise of an “African bird girl” for a publicity stunt. Evidence shows that the film transgressed audience expectations due to (1) its depiction of a “lowbrow,” raced jungle queen, and (2) a shifting sociopolitical climate heightening perceptions of class boundaries and a Black/white binary.
Nanotechnology-Based Pharmacological Treatment for Brain Tumors
Authors: Xu Jiayou
Published: February 2025

One of the most aggressive forms of cancer, brain tumors, especially glioblastoma multiforme (GBM), have a short survival period and a high death rate. It is estimated that 94,390 new cases of primary brain and other central nervous system (CNS) tumors would occur in the United States alone in 2023. The blood-brain barrier's (BBB) protective role and the aggressive nature of GBM provide significant therapeutic challenges. The inability to accurately target tumor cells without harming nearby healthy tissue, poor drug penetration through the blood-brain barrier, and resistance to conventional therapies are some of the drawbacks of current treatments like radiation, chemotherapy, and surgery. A potential solution to these issues is drug delivery systems based on nanotechnology. Nanoparticles, such as carbon-based nanomaterials, liposomes, and polymeric nanoparticles, enhance drug solubility, target certain cells, and enable controlled release. Because of this, therapeutic medications can more easily cross the blood-brain barrier and build up at the tumor site, reducing systemic toxicity. With an emphasis on the advantages of various nanoparticle systems, such as liposomes, polymeric nanoparticles, stimulus-responsive nanoparticles, and carbon nanotubes, this study explores the application of nanotechnology in the treatment of brain tumors. The study examines the shortcomings of existing treatments for brain cancer and how nanodelivery systems can address these issues, ultimately providing a response to the following research question: How can drug delivery systems based on nanotechnology enhance the effectiveness of treatments for brain tumors?.
High-Entropy Alloy Nanocatalysts for Efficient Water Splitting
Authors: Qin Hanqi
Published: February 2025

High-entropy alloy (HEA) nanocatalysts have garnered increasing attention as a cutting-edge solution to the challenges of sustainable hydrogen production via water splitting, offering significant improvements over traditional catalysts. These advanced nanocatalysts, composed of five or more principal elements, exhibit exceptional catalytic activity, superior thermal and electrochemical stability, and notably reduced overpotential for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). This review delves into the synthesis methodologies of HEA nanocatalysts, including their structural and electrochemical characterization, while highlighting their superior performance in water electrolysis for green hydrogen production. We also evaluate the scalability, cost-effectiveness, and environmental impact of these nanocatalysts, comparing them to conventional systems. Furthermore, the review discusses the key challenges and emerging research directions, such as optimizing elemental composition and surface properties to enhance catalytic efficiency and address commercialization barriers. Through this comprehensive analysis, we aim to provide insights into the future potential of HEA nanocatalysts in promoting sustainable energy solutions.
The Investigation Related to the Influence of Different Parameters Based on Random Forest for Weather Prediction
Authors: Yu Zhuoxing
Published: February 2025

The weather change is important in life for providing useful information that guides people's daily activities. However, in a day that machine learning is used in many fields, the weather prediction using machine learning still needs to be developed. This article uses random forest, a machine learning method that is used in classification to do the weather prediction. By changing the parameters in different values and using the evaluate metrics, this article compared the performance about random forest in different parameters. There are 4 parameters researched in this article. The first parameter is the number of trees. In this situation, the f1-score, recall and precision by the categories have the same trend with accuracy. However, the evaluate metrics about result of 1 is obviously worse than the metrics about result of 0. The second parameter is the size of data used to train. In this situation, the performance when the splitting data size is 0.1 and 0.15 is better than the other. The third parameter is the way of encoding. In this situation, using the order of the probability of rain is better than with no order or using the probability to represent the location. Fourth is to delete the unimportant features by the sort of important score. In this situation, when 4 features are deleted, the performance is worse than any others.
Application and Optimization Strategies of the Improved ResNet50 Model in Image Aesthetic Scoring
Authors: Wu Peiling
Published: February 2025

This research introduces an image aesthetics evaluation model designed to enhance the accuracy and robustness of image aesthetics scoring by modifying the pre-trained ResNet50 model. The experimental findings indicate that this model surpasses other prevalent convolutional neural network architectures and optimizer combinations. Additionally, this paper examines the optimal configuration of ResNet50 combined with the Adam optimization algorithm with a learning rate set at 0.001. A thorough analysis reveals that the residual block design of ResNet50 is particularly effective in managing deep features, while the Adam optimizer offers substantial benefits in maintaining stable convergence and efficient learning. The outcomes suggest that this model has extensive application potential in various scenarios, including social media platforms, image retrieval and search engines, photography training and automated design, advertising creation and content generation, smart albums and image management, and artistic creation and review. In these applications, the model can substantially improve the precision and performance of image recommendation, retrieval, classification, and evaluation, resulting in a superior user experience.
Materials for Flexible Photovoltaic Textiles: Principles and Applications
Authors: Zhong Guan , Lu Jason
Published: February 2025

This paper discusses the advancements in flexible photovoltaic technologies, with a specific focus on organic solar cells and perovskite solar cells. With the increasing global demand for renewable energy, these technologies present viable alternatives to traditional silicon-based solar panels. The principles of the photovoltaic effect are explored in detail, highlighting the mechanisms of light absorption, exciton generation, and charge separation that occur within these materials. Recent developments in material efficiency, stability, and production methods are reviewed, revealing the potential applications in smart clothing, portable devices, and building-integrated photovoltaics. While challenges remain in terms of performance and long-term durability, ongoing research and innovation promise to enhance the commercial viability of flexible photovoltaic technologies. This paper aims to provide insights into how these advancements can contribute to a more sustainable energy future, addressing both environmental concerns and the growing energy needs of society. Overall, flexible photovoltaic technologies represent an exciting area of research and development in the renewable energy landscape.
Integrating Multi-Sensor Fusion, AI, and 5G Communication for Advancing Autonomous Driving and Overcoming Key Challenges
Authors: Gan Haofeng
Published: February 2025

Autonomous driving technology has evolved significantly, transitioning from early experimental stages to a central pillar of future transportation systems. Initially driven by advancements in computer vision and robotics, autonomous vehicles (AVs) are now on the brink of revolutionizing urban mobility, enhancing road safety, and improving traffic efficiency through the integration of sophisticated AI, multi-sensor fusion, and advanced communication networks. This paper explores the development and key components of autonomous driving, including multi-sensor fusion, intelligent power systems, vehicle control mechanisms, and system integration. It also highlights the transformative potential of 5G-enabled Vehicle-to-Everything (V2X) communication and AI in advancing AV performance and reliability. However, the widespread adoption of AVs faces significant challenges, including regulatory barriers, cybersecurity risks, and ethical considerations, all of which must be addressed to realize the full potential of autonomous vehicles. This paper provides a comprehensive overview of these advancements and challenges, outlining the critical factors that will influence the future trajectory of autonomous driving technology.
High-Temperature Titanium Alloys for Space Shuttle Engines: Development Challenges and Future Prospects
Authors: Sun Jiaqi
Published: February 2025

Titanium and its alloys play a pivotal role in aerospace engineering due to their exceptional strength-to-weight ratio, corrosion resistance, and ability to perform under extreme thermal conditions. This study explores the development of a titanium alloy capable of withstanding temperatures as high as 1273 K, specifically designed for space shuttle engine structures. Key challenges addressed include enhancing the alloy creep resistance, high-temperature strength, and oxidation resistance, which are essential for its performance in severe aerospace environments. A detailed review of the literature identifies crucial alloying elements such as aluminum (Al), niobium (Nb), silicon (Si), manganese (Mn), boron (B), and tungsten (W), all of which contribute significantly to improving high-temperature stability and mechanical properties. Additional considerations like thermal expansion, phase stability, fatigue resistance, and cost-efficiency were analyzed to ensure a balanced approach in alloy design. The proposed alloy composition offers an optimized balance of strength, ductility, and oxidation resistance, making it suitable for aerospace applications. Future work will involve computational simulations and rigorous empirical testing to further refine the alloy properties and ensure its feasibility for use in real-world space shuttle engines.
Technology-driven Instructional Design: A Study with The SAMR Design Model
Authors: Zhang Wenxiang , Xu Xinhe
Published: February 2025

With the advancement of Education Informatization 2.0 Action Plan, the deep integration of information technology and education and teaching has become an important trend in the field of education. Mobile technology, as a key driving force, is reshaping the education ecology and improving the quality of teaching. The current technology application mostly stays at a shallow level and the potential is not fully utilized. Based on the SAMR design model, this study aims to construct a new model of mobile technology-assisted teaching and empirically analyze its effectiveness and challenges in college English teaching. Through in-depth analysis of the four levels of the SAMR model, namely, substitution, enhancement, modification, and redefinition, the framework of the mobile-assisted teaching model is constructed by clarifying the mobile technology application scenarios and strategies in conjunction with the needs of university English teaching. The empirical study shows that the model significantly improves students' learning participation, independent learning ability and comprehensive quality, and at the same time enhances the fun and interactivity of teaching. However, the use of mobile devices for teaching and learning activities also brought about the problem of student distraction. This study proposes that the teaching model should be further optimized in the future to give full play to its advantages and avoid potential shortcomings, so that the technology can better serve college English teaching and learning, enhance teaching effectiveness and promote effective learning.
Research on Automatic Vegetable Pricing Strategies Based on Multiple Machine Learning Algorithms
Authors: Ji Yanchen , Jiang Yuze , Zhou Yunfei
Published: February 2025

This paper explores methods for optimizing product pricing and replenishment strategies in fresh supermarkets through advanced data analysis techniques. The initial data processing involves merging and cleaning, with a focus on statistical and visual analysis of sales distribution across products and categories. Descriptive statistics are derived using line charts, bar charts, and pie charts based on yearly, quarterly, and monthly data. This article analyzes relationships within the data employing the Kruskal-Wallis test, Spearman correlation coefficient, and Pearson correlation coefficient. Spectral clustering is utilized to group products, revealing patterns in their interrelationships. For pricing strategies, this paper implements a cost-plus pricing model to calculate markup rates, considering sales and fixed costs. This article apply regression algorithms, specifically XGBoost and Random Forest, to predict unit price, purchase price, and sales volume, and assess their performance for optimization. Time series forecasting techniques predict data for the upcoming week, complemented by an optimization function that visualizes this iterative process. Further data processing for optimizing purchase quantities employs regression algorithms, time series models, and genetic optimization algorithms to refine predictions, ultimately identifying strategies to maximize profit.
Applications of Reinforcement Learning on Humanoid Robot Controlling
Authors: Ge Qirui
Published: February 2025

Amidst the swift advancement of artificial intelligence and machine learning technologies, humanoid robots are increasingly demonstrating their potential in mimicking human motions, adapting to intricate environments, and executing a wide array of tasks. Reinforcement Learning (RL), as an advanced learning paradigm that optimizes decision-making processes through environmental interaction, has emerged as a pivotal tool in enhancing the capabilities of humanoid robots. In this paper, the researcher delves into a variety of RL techniques and their implementations, spotlighting key accomplishments and addressing the prevailing challenges alongside envisaging future trajectories. Through an in-depth examination of these applications, our aim is to elucidate the transformative potential of RL in the domain of humanoid robotics, paving the way for more adaptive, intelligent, and autonomous systems. This paper posits that, with the deepening of research and technological progress, RL will catalyze breakthroughs in the humanoid robot sector, propelling smart robots towards greater integration within human society.