bluuebunny/crossref_metadata_2025 huggingface dataset:
EXPEDITIOUS SYNTHESIS OF RHODANINE DERIVATIVES THROUGH MICROWAVE IRRADIATION
Authors: MITSIK Laila. A. , CEBALLOS Noelia M. , PELÁEZ Walter J.
Published: January 2025

In this study, we present an efficient and environmentally friendly method for the synthesis of 5-benzylidene and 5-cinnamylidene rhodanine derivatives, utilizing microwave irradiation as a green synthetic approach. This methodology provides several advantages, including short reaction times, high atom economy, and minimal environmental impact. The yields of the synthesized compounds ranged from 61% to 92%, with purities exceeding 95%. The use of microwave technology under mild conditions underscores the advantages of this approach for sustainable organic synthesis.
MACROALGAE AS MARINE MICRODEBRIS TRAPS: A CASE STUDY IN THE BAHÍA BLANCA ESTUARY
Authors: Forero-López Ana D. , Fernández Carolina , Malanca Fabio , Rimondino Guido N. , Botté Sandra E.
Published: January 2025

Marine microdebris (MDs), including microplastics (MPs) and antifouling and/or anticorrosive paint particles (APPs) could have significant implications within coastal ecosystems of Argentina, particularly producers primary as macroalgae. In this study, we aimed to determine the abundance of MDs, including MPs APPs, among others, in seven different species of macroalgae (P. morrowii, C. diaphanum, Bangia sp., Gelidium spp., Ulva spp., P. nakamurae, and B. minima) and in surface waters from the Bahia Blanca Estuary (BBE). Fiber MPs were the most common type of MDs found in surface waters. In macroalgae, high concentrations of MPs were observed in brown algae (P. nakamurae), followed by red algae (Gelidium spp. and Bangia sp.). Green algae B. minima exhibited high concentrations of APPs. Additionally, other types of MDs, such as black soot particles, metallic microspheres, and fragments, were also identified in green algae Ulva spp. The most predominant colors of MDs found in macroalgae were black, white, and transparent. The size ranges of the MDs were primarily 0.5–2.5 mm and <0.5 mm in both surface waters and macroalgae samples. This study provided a baseline for MDs abundance, indicating the bioaccumulation of these emerging pollutants in the primary producers of the coastal trophic chain of BBE.
POTENTIAL USE OF THE STERILE FROM IPUEIRA MINE, BAHIA, BRAZIL, AS A CORRECTIVE ACID SOIL
Authors: TEIXEIRA Aline Maria dos Santos , SAMPAIO João Alves , MEDEIROS Marta Eloisa , GARRIDO Francisco Manoel dos Santos
Published: January 2025

The use of corrective soil agriculture has a significant impact on agricultural productivity, whether by correcting acidity or by providing calcium and magnesium. Rocks for crops are a soil remineralization technique that involves adding rock powder to the soil to promote acidity correction and increase fertilization. The beneficial effects of using this technique have expanded the potential use of rocks as corrective soil and added value to alternative input sources. In this context, the objective of this study was to evaluate the waste rock from the Ipueira Mine, Bahia, Brazil, for use in agriculture as a soil amendment for acidic soils, according to the characteristics specified by the regulations of the Ministry of Agriculture and Livestock. The characteristics of the rock were evaluated in terms of reactivity (RE), neutralization power (PN), total neutralization relative power (PRNT), and calcium and magnesium contents as their respective oxides. The analysis of the results showed that the waste rock from the Ipueira Mine meets the limits specified by the current regulations for acidity correctives, with 57% PRNT, and that particles with sizes smaller than 0.30 mm present a reduction in CaO content and an increase in MgO content, compared to the other granulometric fractions. Thus, given the results obtained, it is possible to state that the waste rock from the Ipueira mine has the potential for application in agriculture as a corrective for acidic soils, which makes the study of this rock a contribution to the growth of sustainable agriculture in the country.
COMPUTATIONAL THINKING AND SCIENCE VALUE: PREDICTORS OF SENIOR SCHOOL PHYSICS STUDENTS SCIENCE IDENTITY IN ILORIN, NIGERIA
Authors: YAHAYA Wasiu Olayinka , AKANBI Abdulrazaq Oladimeji , YAHAYA Quadri , YUSUF Abdulquadri Aishat
Published: January 2025

Knowledge acquisition and positive learning outcomes serve as the major output of teaching-learning activities. To be able to think differently, independently, and creatively in nature for the decision-making process constitutes what is called computational thinking. The present study searched the senior school students’ science value as a mediating variable between computational thinking and science identity. Computational thinking as a multi-dimensional construct consists of creativity, algorithmic thinking, cooperativity, critical thinking, and problem-solving skills, as theorized in previous literature. The structural equation model approach was employed to analyze the collected data from 243 physics students. The findings of the study revealed that computational thinking dimensions had a weak and non-significant relationship with students’ science identity (p>.05, t<1.96). The study further revealed that the science value held by students does not significantly mediate the relationship between computational thinking dimensions and science identity. The study concluded that computational thinking does not portray students as being scientists. It is recommended to work on other determinant variables so as to determine the factors responsible for students science identity.
FLASH VACUUM PYROLYSIS (FVP) OF DIMETHYL 5-METHYL-3-PHENYL-1H,3H-PYRROLO[1,2-C]THIAZOLE-6,7-DICARBOXYLATE 2,2-DIOXIDE: KINETIC AND THERMAL PARAMETERS
Authors: VELAZCO MONTAGNA Federico Juan , CAMARGO SOLORZANO Gloria Patricia , SOARES María Isabel , PINHO E MELO Teresa María V. D. , PELÁEZ Walter José
Published: January 2025

The generation through FVP and reactivity of an azafulvenium derived from thermal SO2 extrusion of dimethyl 5-methyl-3-phenyl-1H,3H-pyrrolo[1,2-c]thiazole-6,7-dicarboxylate 2,2-dioxide (compound 1) is described. Synthesis of 1 is briefly shown (Scheme 1, Sutcliffe et al., 2001). The intermediate azafulvenium was previously shown to be trapped by dipolarophiles, acting either as a 4? or 8? dipole (Pinho e Melo et al., 2004). A pyrolysis mechanism is proposed (Scheme 2). Three products were found, two of which were not previously reported (Scheme 3, 2cis and 3). The characterization by NMR, UV-Vis, and GC-MS of the substrate and products was performed. Molar absorptivities from 1 are informed (Figure 1, Table 1), along with 1H-NMR signals (Figure 2). Conditions of pyrolysis reactions are informed in Tables 2 and 3. These data allowed us to obtain the Arrhenius parameters corresponding to the reaction extrusion of SO2 (Figure 3, Table 4). Besides, thermodynamic parameters from the equilibrium between both styrylpyrroles (2trans and 2cis) obtained as main products are also reported for the very first time (Figure 4, Table 4).
EVALUATION OF OXAZINES AS A POTENTIAL TREATMENT FOR GONORRHEA
Authors: Perlbach Agostina , Quiroga Agustina , Gonzalez Andrés , Quintero Cristián Andrés , Peña Diana Carolina Pinilla , Hualpa Mauricio , Cabrini Micaela , Ceballos Noelia M. , Bianchi Paula Cambuli , Díaz Sofía , Pelaez Walter
Published: January 2025

Sexually transmitted infections pose serious public health challenges, with gonorrhea, caused by Neisseria gonorrhoeae, being a common concern, especially due to increasing antibiotic resistance. This study focused on identifying molecules that inhibit N. gonorrhoeae growth or act as antibiotic adjuvants, with parallel testing in other Gram-negative bacteria. Synthetic organic molecules were evaluated through in-silico structural analysis and validated through in vitro assays. Microdiffusion assays showed that molecules WpD, WpF, Wp55, and Wp56 demonstrated antimicrobial activity against Salmonella. When testing adjuvant effects with antibiotics, molecules WpB, WpD, WpE, WpF, Wp3, Wp8, Wp55, Wp56, WpG40, and T10 showed adjuvant activity in Salmonella, while only H07 and H08 showed adjuvant activity in N. gonorrhoeae strains L and F. These findings suggest these adjuvant molecules could reduce required antibiotic doses in gonorrhea treatment, potentially decreasing bacterial resistance development.
CHARACTERIZATION OF AEROSOL PARTICLES IN ILORIN, NIGERIA: GROUND-BASED MEASUREMENT APPROACH
Authors: OLLAITAN Razaq Abolaji , AYANLADE Ayansina
Published: January 2025

The present study investigated the changes in aerosol optical characteristics and their impact on climate change risks in Ilorin, Nigeria, focusing on the physical characteristics, spatial distribution, and relationships between aerosol properties. The research utilized ground-based measurements from AERONET data archives to analyze aerosol optical depth and angstrom exponent data, along with single scattering albedo and aerosol size distribution parameters for 2019 and 2021. Analysis was conducted using mean, percentages, and correlation analysis to determine seasonal and annual aerosol properties. The findings revealed four distinct types of aerosol particles: urban industrial (29.22%), desert dust (37.08%), biomass burning (10.67%), and urban mix (23.03%). The study demonstrated statistically insignificant annual and seasonal variations (p value>0.05), with low Angstrom exponent values (<1) indicating a predominance of coarse particles over Ilorin. The average annual single scattering albedo at wavelengths 440/675/870/1020 was 0.05/0.09/0.23/0.25. A positive correlation between aerosol properties and particle concentration was observed, highlighting the significant impact of spatiotemporal aerosol changes on climate change risks. These findings suggest that increasing changes in Ilorin's aerosol properties may exacerbate future climate risks, emphasizing the growing contribution of anthropogenic emissions to urban climate challenges. This research contributes to our understanding of aerosol optical characteristics in urban environments and their implications for climate change mitigation strategies.
REVIEW ON GLOBAL WARMING: THE CONSEQUENCES OF EXCESSIVE EMISSIONS
Authors: LLENSA BRICCA Francisco Javier , BRAVO Claudia Florencia
Published: January 2025

If greenhouse gas emissions are not halted, the consequences of global warming will become increasingly severe. By analyzing how these factors can trigger climate tipping points, we assessed the global population's interest in the topic, as well as that of the scientific community, which shows growing interest in finding ways to reverse the situation and address global warming. The importance of reducing emissions and the urgent need for action to mitigate the effects of climate change are highlighted.
GLOBAL WARMING: GLOBAL WARMING AND WORLD POPULATION: A SLOW-COOKED FROG
Authors: TADEO Jordan
Published: January 2025

The analogy of the frog in boiling water reflects how the global population can ignore warnings about global warming. Just like the frog, which slowly adapts to rising temperatures, many people fail to recognize the imminent consequences of climate change. This phenomenon, driven by greenhouse gases, leads to a gradual increase in temperatures, resulting in droughts, floods, and extreme weather events. These changes severely impact human health, increasing respiratory diseases and food insecurity. Furthermore, the impact on ecosystems could be devastating, leading to species extinction and habitat collapse. If we do not act soon, we could face a future where life on Earth becomes unsustainable.
ROSEMARY OIL EXTRACTION THROUGH DIFFERENT METHODS: EXPERIMENTAL AND MATHEMATICAL MODELING
Authors: SURGEK Isaias , DOMINGOS Diogo , NUNES Renata Oliveira , CIPOLATTI Eliane Pereira , MENDES Marisa Fernandes
Published: January 2025

Anxiety and depression are mental disorders that affect many people worldwide, with young people being the most impacted. For pharmacological treatment, anxiolytic and antidepressant medications are used, which trigger different side effects in addition to the risk of dependence. Due to these disadvantages, the adoption of techniques such as aromatherapy has grown to promote relaxation and well-being through essential oils. Among them, rosemary essential oil (Rosmarinus officinalis L.) has anxiolytic and antidepressant activities, as well as antioxidant and anti-inflammatory properties. Thus, rosemary essential oil extraction was performed using different techniques: dynamic maceration, soxhlet extraction (hexane), and supercritical extraction. The material underwent pre-treatment, such as grinding, particle size analysis, and drying. Supercritical extraction was carried out at temperatures from 40 to 60 °C and pressures from 100 to 300 bar, with collections every 15 minutes until saturation to construct an extraction kinetic curve. The analysis of the results obtained showed that Soxhlet 1 presented the highest yield, while supercritical extraction performed under conditions of 60 °C and 300 bar obtained the highest yield. The increase in pressure influenced the yield of supercritical extraction, as well as the temperature, which increased the vapor pressure of the components.
MANDARIN PEEL WASTE TRANSFORMATION: UNLOCKING HIGH-VALUE PRODUCT GENERATION
Authors: TORRES Myriam , MOYANO E. Laura , BAJALES Noelia
Published: January 2025

The depletion of fossil fuels, the global energy crisis, and environmental pollution have prompted the search for more sustainable energy production alternatives. Biomass has emerged as a promising renewable energy source due to its abundance and the diverse methods available to convert it into fuel, including biochemical and thermochemical processes. Pyrolysis has been adopted as one of the biomass energy conversion techniques. It involves the thermal decomposition of organic matter in an oxygen-free environment, resulting in the production of high-value-added products, including biochar, bio-oil, and non-condensable gases. In this study, fast pyrolysis reactions of mandarin peels were studied at different temperatures (300°C-500°C) using vacuum and atmospheric pressures in a nitrogen atmosphere. The maximum oil yield (23 wt.%) was obtained through vacuum pyrolysis waste mandarin peel at 500 °C, producing 2.5 times more than the amount of oil produced from atmospheric pressure pyrolysis at the same temperature. Oxygenated compounds and aromatic derivatives were the principal compounds found in bio-oils under vacuum and atmospheric conditions, respectively. 5-(Hydroxymethyl)furfural (5-HMF) and acid acetic (AcoH) were the main products in vacuum pyrolysis, whereas phenols were predominant compounds in atmospheric pyrolysis. Depending on the pyrolysis pressure conditions, HMF and AcOH concentration was between 11-2 wt.% and 3-15%, respectively, considering the bio-oil mass. Additionally, 1H NMR experiments were carried out as an alternative and simple technique to directly determine the amount of high-value chemicals in bio-oils.
INNOVATIVE TECHNIQUE FOR RAPID DETECTION OF ANALYTES IN COMPLEX MATRICES
Authors: ARIAS Justo Manuel , LAIME Ricardo F. , OLMO Jonathan R. , IRIARTE Ana G. , ALVAREZ Rosa M. S.
Published: January 2025

In this work we present an innovative technique to detect analytes in complex matrices. Thioverbenone was synthesized using verbenone and Curphey’s reagent without further purification, and was detected in the mixture reaction using SERS spectroscopý coupled to thin layer chromatography (TLC). Combining both techniques enables the rapid and efficient detection of components in complex matrices without prior purification, thereby saving time and reagents.
EXPLORING TEACHERS' PERSPECTIVES ON INTEGRATING WASTE MANAGEMENT EDUCATION INTO TEACHER TRAINING PROGRAMMES TO PROMOTE ENVIRONMENTAL SUSTAINABILITY IN NIGERIA
Authors: BELLO Muhinat Bolanle
Published: January 2025

This study investigates the integration of waste management education into teacher training programs in Nigeria, addressing the critical need for environmental sustainability in the face of increasing waste generation. A qualitative approach was employed, utilizing thematic analysis to gather and interpret data from teachers regarding their awareness, understanding, and attitudes toward waste management and sustainability concepts. Findings reveal significant gaps in teachers' knowledge and confidence in teaching waste management, with many relying on external resources due to insufficient institutional support. Teachers expressed a strong desire for enhanced professional development opportunities and collaborative partnerships to improve their teaching practices. The study emphasizes the importance of an integrated curriculum that incorporates waste management across various subjects, alongside experiential learning opportunities. Recommendations include the establishment of robust professional development programs to equip educators with the necessary skills and resources to effectively teach sustainability concepts, ultimately fostering a culture of environmental responsibility among students.
DETECTION OF URL PHISHING BASED ON MACHINE LEARNING TECHNIQUES
Authors: Rashid Mohammed Kareem , Mousa Alaa Abdulshaheed , Hassan Saif Al-Deen H.
Published: January 2025

Phishing URLs continue to pose a significant threat to online security, using sophisticated deception techniques to trick users into revealing sensitive information like passwords and financial data. Our research explores the potential of machine learning, specifically a Multilayer Perceptron (MLP) neural network, to effectively identify and flag these malicious websites. We trained and validated our model using two distinct datasets, focusing on distinguishing legitimate URLs from phishing attempts. To ensure thorough evaluation, we compared the MLP's performance against traditional machine learning approaches, including Decision Tree and Naive Bayes algorithms. Through this research, we aim to strengthen existing cybersecurity measures and provide more reliable protection against increasingly sophisticated phishing attacks. The results demonstrate promising potential for using neural networks in phishing detection, though further refinement may be needed to address emerging attack patterns.
GLOBAL WARMING: MELTING OF THE ROOF OF AFRICA. MOUNT KILIMANJARO AND THE FOOTPRINT OF CLIMATE CHANGE.
Authors: BERTOSSI Gianella , HUERTA DERRAC Aldana , RENAUDO Bianca Lucia
Published: January 2025

This paper will analyze the social relevance generated by global warming, glacier melting in order to see its impact on society through the importance of topics such as Mount Kilimanjaro in Africa, sea level rise and various vector-borne diseases. Through the use of Google, Google Scholar and Google Trends we will demonstrate the importance and attention that society gives to the topic over time, both in its relevance at a scientific level and in everyday life.
EDUCATIONAL MANAGEMENT STRATEGIES TO MITIGATE CLIMATE CHANGE
Authors: ABDULLAHI Nimota Jibola Kadir
Published: January 2025

The study explores educational strategies for climate change mitigation, emphasizing community engagement, infrastructure management, and student involvement in schools. To explore educational strategies for effective climate change mitigation, a quantitative survey will be conducted with school administrators and teachers in Kwara State, analyzed using SmartPLS 3.0 for climate change strategies. Findings highlight key strategies for effective climate change mitigation in education. The study emphasizes the need for integrated strategies in education to foster community engagement and resilience against climate change. Finally, integrated educational strategies are vital for effective climate change mitigation and community resilience. The study underscores the significance of educational institutions in building climate-resilient communities through comprehensive mitigation strategies. These strategies encompass curriculum development, infrastructure adaptation, and community outreach programs. The research methodology employs a systematic approach to data collection and analysis, ensuring robust findings that can inform policy development and implementation. Results indicate that successful climate change mitigation in educational settings requires a multi-faceted approach combining theoretical knowledge with practical applications. The discussion examines the implications of these findings for educational policy and practice, highlighting the potential for schools to serve as catalysts for broader community engagement in climate action. Recommendations include developing targeted training programs for educators, establishing climate action committees within schools, and creating partnerships with environmental organizations. This research contributes to the growing body of knowledge on educational approaches to climate change mitigation, providing practical insights for educators and policymakers working to enhance climate resilience in educational settings.
SOIL FERTILITY ANALYSIS" REFERS TO THE ASSESSMENT OF SOIL'S CAPACITY TO PROVIDE ESSENTIAL NUTRIENTS FOR PLANT GROWTH
Authors: Santos Mariana Gonçalves
Published: January 2025

Understanding soil fertility variability in cultivated areas provides important insights for the rational use of soil amendments and fertilizers. This study aims to evaluate changes in the spatial variability of soil chemical properties resulting from intensive vegetable cultivation over more than 50 years in mountain agroecosystems in Nova Friburgo, Rio de Janeiro. The creation of maps for each attribute allowed visualization of its spatial distribution in the area, which is not possible when using only descriptive statistical analysis. The soil's chemical and physical attributes showed spatial variation, with P, K+, Mg+2, Al+3, base saturation (V%), and silt displaying high variability, while Ca+2, Ca+Mg, H+Al, organic carbon, CEC, sand, and clay showed medium variability. However, pH demonstrated low variability. Two main sources of variation in fertility spatial distribution were identified: one characterized by topography and another associated with agricultural activities, which, through intensive use of agricultural inputs, are contributing to increased soil fertility. Another factor is associated with inadequate soil management and topography, which cause the transport of finer soil particles and their accumulation in lower parts of the terrain, where enrichment in P, K+, Ca+2, Mg+2, and organic carbon levels is observed. The evident acidity in agricultural soils is due to high natural acidity and farmers' infrequent use of liming. The soils showed values considered high or very high for P (88%) and K+ (65%) in the study area. These values are directly associated with excessive use of poultry litter and soluble phosphate and potassium fertilizers during vegetable production.
Effective Strategies for Teaching Arabic Prosody in Oral Communication Skills
Authors: Syukriyah Mohd Suib Nur , Baharudin Harun
Published: January 2025

An emphasis on the elements of prosody was crucial for achieving full proficiency in a spoken language. The research focused on prosody, which included components such as stress, intonation, pauses, and mora. This study aimed to evaluate the proficiency of Arabic teachers at Government Aided Religious Schools (SABK) in Selangor, Malaysia, regarding their understanding and instructional abilities in prosody, particularly in speaking skills. The study sample consisted of 152 Arabic teachers working at SABK in Selangor, Malaysia. The questionnaire was evaluated by four reviewers, and the results indicated a high level of reliability, as evidenced by Cronbach Alpha values of 0.91 for prosody teaching strategies and 0.90 for prosody knowledge. Both descriptive and inferential analyses were performed on the acquired data. The research data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 20. The results of the data analysis showed that both the prosody knowledge (mean: 3.76, S.D.: 0.465) and the prosody teaching strategies (mean: 3.69, S.D.: 0.484) were at a relatively high level. According to Pearson's correlation analysis, there was a significant positive correlation (r=0.530) between the prosody teaching strategy and the prosody knowledge. Simultaneously, knowledge of prosody played an important role in applying prosody strategies effectively, contributing about 28.1 percent (r₂=0.281). Proficiency in Arabic prosody aided teachers in creating effective ways for two-way interaction inside the classroom. Students greatly benefited from this situation in terms of improving their communication skills, as it made class communication more effective. The Arabic language curriculum needed to provide teachers with training in prosody teaching strategies to enhance two-way communication in the classroom.
AI-Enabled Supply Chain Optimization
Authors: Nitin Grover
Published: January 2025

As global supply chains become more intricate, the significance of supply chain risk management has significantly increased. This research delves into the utilization of artificial intelligence (AI) in managing supply chain risks, analyzing cutting-edge advancements, obstacles, and potential areas for further exploration.
Through a comprehensive review of literature sources like Google Scholar, Web of Science, EI, and Scopus, this study investigates how AI methods such as machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms can help mitigate supply chain risks. These AI technologies have demonstrated remarkable efficacy in mitigating various risks, including forecasting, anomaly detection, image recognition, text mining, logistics optimization, and emergency response tactics.
Apart from AI-driven approaches, optimization solvers and algorithms play a critical role in tackling complex supply chain dilemmas. Mathematical programming solvers, including linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP), are commonly used to model and optimize supply chain networks by considering factors like cost, capacity, and demand fluctuations. Fine-tuning solver parameters and strategies to enhance computational efficiency—known as solver tuning—has been crucial in enhancing solution quality and decreasing computation time for large-scale supply chain issues.
Heuristic solvers like genetic algorithms, simulated annealing, and ant colony optimization are frequently employed to resolve conflicts in supply chains. These solvers offer practical solutions to problems where exact methods are computationally impractical, allowing for swift responses to disruptions such as production delays or spikes in demand. Pyramid classification, a hierarchical approach, further refines decision-making by categorizing risks and aligning response strategies based on priority and severity.
Furthermore, the integration of AI technologies and solvers enables advanced conflict resolution techniques, including scenario-based modeling and multi-objective optimization. These approaches enable decision-makers to weigh trade-offs between conflicting objectives like minimizing costs versus maximizing service levels in real-time.
The study underscores that AI technologies and optimization solvers significantly bolster risk management in supply chains. Nonetheless, challenges such as data privacy concerns, security vulnerabilities, technical intricacies, and implementation obstacles pose critical barriers to widespread adoption.
The study offers practical suggestions for businesses and decision-makers while pinpointing key areas for future exploration, such as devising hybrid models that merge heuristic solvers with AI for adaptive and scalable risk management strategies.
Mental Health Through Music Therapy
Authors: Shrutika Deshpande  , Mital Chaudhari  , Chaitanya Musudge  , Sania Mulani  , Namrata Suryawanshi
Published: January 2025

This project, Mental Health Through Music Therapy, focuses on leveraging the therapeutic power of music to support mental well-being while integrating a robust user management system. It offers a secure and interactive platform where users can engage with therapy-specific music tailored to their needs. The system features user registration with age verification, ensuring appropriate access, and securely stores user details in structured files and a CSV database for efficient session tracking and future analysis. Users can explore categorized music folders, read therapeutic text files, and enjoy a seamless music playback experience, fostering a relaxing and personalized environment. Designed with a neon-themed interface, the platform incorporates custom animations and error-handling mechanisms for an engaging and user-friendly experience. By combining the emotional benefits of music therapy with modern technological advancements, this project aims to provide an innovative and practical solution for improving mental health and enhancing emotional well-being
Characterizing Transfer Graphs of Suspicious ERC-20 Tokens
Authors: Josenhans Calvin , Kuehlkamp Andrey , Nabrzyski Jarek
Published: January 2025

Ethereum is currently the second largest blockchain by market capitalization and a popular platform for cryptocurrencies. As it has grown, the high value present and the anonymity afforded by the technology have led Ethereum to become a hotbed for various cybercrimes. This paper seeks to understand how these fraudulent schemes may be characterized and develop methods for detecting them. One key feature introduced by Ethereum is the ability to use programmable smart contracts to execute code on the blockchain. A common use of smart contracts is implementing fungible tokens with the ERC-20 interface. Such tokens can be used to impersonate legitimate tokens and defraud users. By parsing the event logs emitted by these ERC-20 contracts over 20 different periods of 100K blocks, we construct token transfer graphs for each of the available ERC-20 tokens on the blockchain. By analyzing these graphs, we find a set of characteristics by which suspicious contracts are distinguished from legitimate ones. These observations result in a simple model that can identify scam contracts with an average of 88.7% accuracy. This suggests that the mechanism by which fraudulent schemes function strongly correlates with their transfer graphs and that these graphs may be used to improve scam-detection mechanisms, contributing to making Ethereum safer.
Enhancing Educational QA Systems: Integrating Knowledge Graphs and Large Language Models for Context-Aware Learning
Authors: Arslan Ayse
Published: January 2025

This study explores the integration of Knowledge Graphs (KG) and Large Language Models (LLMs) to design a question-answering (QA) system in the field of education. The proposed method involves constructing a KG using LLMs, retrieving contextual prompts from high-quality learning resources, and enhancing these prompts to generate accurate answers to real questions related to major educational concepts. The technical framework outlined in this paper, along with the analysis of results, contributes to the advancement of LLM applications in educational technology. The findings provide a foundation for developing intelligent, context-aware educational systems that leverage structured knowledge to support learning and enhance educational outcomes.
An AI-Powered Mobile Application for Reducing Food Waste through Cost Estimation and user Awareness
Authors: Q Liu John , Boulom Tyler
Published: January 2025

This paper addresses the critical issue of food waste, which contributes to economic losses and environmental harm [1]. We propose a mobile application, Foodnomics, leveraging AI technology to estimate food waste costs and raise awareness among users [2]. The app utilizes image recognition to identify leftover food, estimate portion sizes, and calculate associated costs. Our experiments revealed strong accuracy in classifying common food items but highlighted challenges with less familiar items and regional price discrepancies. The proposed solution builds on existing methodologies by focusing on individual consumer behavior and providing actionable insights. By empowering users to track and reduce their food waste, our project offers a scalable and impactful tool for promoting sustainability and reducing food insecurity [3]. The study concludes with recommendations for further improvements, including expanding the training dataset and incorporating realtime pricing models.
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation
Authors: Kuprashevich Maksim , Alekseenko Grigorii , Tolstykh Irina
Published: January 2025

Multimodal Large Language Models (MLLMs) have recently gained immense popularity. Powerful commercial models like ChatGPT and Gemini, as well as open-source ones such as LLaVA, are essentially general-purpose models and are applied to solve a wide variety of tasks, including those in computer vision. These neural networks possess such strong general knowledge and reasoning abilities that they have proven capable of working even on tasks for which they were not specifically trained. We compared the capabilities of the most powerful MLLMs to date including ShareGPT4V, ChatGPT 4V/4O, and LLaVA Next in the specialized task of age and gender estimation, with the state-of-the-art specialized model MiVOLO384. In our study, we discovered that the fine-tuned open-source ShareGPT4V model is capable of outperforming the specialized model in age and gender estimation tasks. At the same time, the proprietary ChatGPT-4O beats both in the age estimation task but does not perform as confidently in gender recognition. This gives interesting insights about the strengths and weaknesses of the participating models and suggests that with a few tweaks, general-purpose MLLM models can match or even surpass specialized ones in certain fields. Even though these fine-tuned models might require more computing power, they offer big benefits for tasks where computing power is not a limiting factor and where the best accuracy is key, such as data annotation.
A Smart Chrome Extension for Web Content Analysis and Distraction Mitigation using Artificial Intelligence and Machine Learning
Authors: Zhu Zhiyuan , Washburn Garret
Published: January 2025

The temptation to lose oneself in the digital world is more prevalent now in the present state of the internet than ever before. Many internet users often fall into this temptation, voiding their efforts to get any productive work done that they may need to utilize their internet browser. To solve this problem, this paper proposes the ScreenTimeSage Chrome Extension available on the Chrome Web Store [12]. The ScreenTimeSage Chrome Extension seeks to remedy this issue of internet users getting off task but utilizing AI to analyze their specific browsing behavior and analyzing where and how they tend to get off task. Once the extension has analyzed how the user gets off task, it is able to provide customized support and advice on how to help the user stay focused. This extension relies on the Gemini LLM API, as it utilizes prompt engineering and Chrome storage to keep an active record of the user’s browsing habits, and classifying how productive the user is on each web page [13]. This extension has been tested for its content classification as well as its back-end server reliability, which is available in this paper. Both experiments are demonstrations of good results from the extension. The ScreenTimeSage Chrome Extension is ultimately a great option for those seeking a way to combat their inclination to get distracted while using their browser, as it provides customized insights on how they individually get distracted and how they can combat this for themselves.
Towards Polyglot Data Processing in Social Networks using the Hadoop-Spark Ecosystem
Authors: Medeiros Antony Seabra de , Lifschitz Sergio
Published: January 2025

This article explores the use of the Hadoop-Spark ecosystem for social media data processing, adopting a polyglot approach with the integration of various computation and storage technologies, such as Hive, HBase and GraphX. We discuss specific tasks involved in processing social network data, such as calculating user influence, counting the most frequent terms in messages and identifying social relationships among users and groups. We conducted a series of empirical performance assessments, focusing on executing selected tasks and measuring their execution time within the Hadoop-Spark cluster. These insights offer a detailed quantitative analysis of the performance efficiency of the ecosystem tools. We conclude by highlighting the potential of the Hadoop-Spark ecosystem tools for advancing research in social networks and related fields.
A Smart Caffeine Level Predicting and Analysis Solution with Sequential Machine Learning Model using Artificial Intelligence and Computer Vision
Authors: Zhang Tianrui , Li Ang
Published: January 2025

Caffeine is the most widely consumed stimulant globally, yet its overconsumption poses significant health risks. Traditional methods for measuring caffeine content, such as weighing coffee, can be impractical in everyday settings. This paper proposes an innovative solution that leverages artificial intelligence (AI) and machine learning, specifically utilizing a Sequential Convolutional Neural Network (CNN), to predict caffeine levels based on image analysis of coffee. The system processes images to determine brightness, correlating this data with caffeine concentration on a defined scale. Challenges such as dataset selection, prediction accuracy variability, and training epoch limitations were addressed through data cleaning and iterative model training. Experiments revealed that the model achieves a high accuracy rate, indicating its potential as a practical tool for consumers aiming to monitor their caffeine intake. This application not only enhances user convenience but also promotes healthier consumption practices by providing a reliable method for estimating caffeine levels visually.
Addressing Big Data Challenges with Soft Computing Approaches
Authors: Jonnalagadda Anil Kumar , Myakala Praveen Kumar
Published: January 2025

The exponential growth of data has outpaced traditional computing systems, necessitating innovative approaches for processing, managing, and extracting actionable insights. In this study, we explore how techniques like fuzzy logic, neural networks, and evolutionary algorithms can help solve some of the biggest problems in Big Data, such as uncertainty, imprecision, and noise in real-world datasets. These methods offer unparalleled adaptability and scalability for diverse applications. We propose a comprehensive framework that integrates hybrid approaches, such as neurofuzzy systems and evolutionary-fuzzy optimization, to enhance clustering, feature selection, and predictive analytics with improved accuracy and interpretability. Extensive experiments on real-world datasets from domains like healthcare and IoT demonstrate significant advancements in processing speed, resource utilization, and analytical efficiency over traditional methods. This study highlights the pivotal role of soft computing in unlocking the true potential of Big Data, enabling innovative solutions and driving meaningful advancements across industries.
Integrating Machine Learning for Diamond Price Prediction and Distinguishing Natural Diamonds from Lab Grown: A Unified Approach
Authors: Ranglani Hardev
Published: January 2025

Accurate pricing and authentication of diamonds are essential for ensuring market transparency and consumer confidence. This study applies machine learning (ML) techniques to predict diamond prices and classify diamonds as lab-grown or natural based on attributes such as cut, color, clarity, and carat weight, etc. A unified framework combining regression for price prediction and classification for origin determination is developed with robust model evaluation. The proposed models achieve a mean absolute error of $ 554.32 in price prediction and an F-1 score of 98.66 % in origin classification. The study also explores the varying linear relationship between the variables in predicting diamond price using local linear regression. These findings provide valuable insights for the gemstone industry, offering a practical and interpretable approach to automated diamond valuation and authentication.
The Principal’s Strategy in Optimizing Learning Communities for the Pedagogical Competency Development of Kindergarten Teachers
Authors: Sa'adah Anis , Hariyati Nunuk , Haq Mohammad Syahidul , Khamidi Amrozi , Nursalim Mochamad
Published: January 2025

The title of this research is the principal's strategy in optimizing the learning community for the development of teacher pedagogical competence in kindergarten. There are two research objects, namely the principal's strategy and the pedagogical competence of teachers in kindergarten. The aim of this research is to describe the principal's strategy in optimizing the learning community to develop teacher pedagogical competence in kindergarten. The type of research is qualitative descriptive research which uses interviews, observation and documentation to collect data. Respondents involved school administrators, principals, teachers and education staff at the two schools. The research results show that the strategies used by school principals in developing teacher pedagogical competence include: forming small teams, setting routine schedules, implementing collaboration, and creating an environment that supports sustainable learning. This strategy has proven to be effective in developing teacher competence in designing, implementing and evaluating learning. However, this strategy is not without obstacles, among the obstacles faced are related to time, facilities, and the school community's resistance to change.
Pathway to Green Consumerism: Decoding the Interplay of Affective and Cognitive Green Brand Factors
Authors: Afifa Khoula Sadia , Haider Syed Waqar , Issa Abbas , Raza Aamir , Altaf Fasiha
Published: January 2025

The study aims to measure the impact of green brand affect on green purchase intentions of sustainable electronic products. It investigates the direct and indirect factors influencing consumers' green purchase intentions by applying the theory of planned behavior. Surveys were submitted to non-probabilistic convenience sampling of Pakistani consumers (n=379). The partial least squares method, a structural equation modeling technique, was employed using Smart PLS 4.0. The findings reveal that green brand affect does not directly influence green purchase intention. However, green brand image and green brand attitude positively mediate the relationship between green brand affect and green purchase intention, while green brand trust shows no significant mediating effect. This research offers valuable insights into the impact of green brand affect on customers' purchase intentions for sustainable electronic products, marking the first study of its kind in the context of Pakistan. Future research could extend this model by incorporating perceived price as a moderating variable, particularly in middle-income countries, and adopting a longitudinal approach to explore actual purchase behavior over time.
Pengembangan Media Pembelajaran Berbasis Scratch Elemen Bilangan Fase A Pada Kurikulum Merdeka
Authors: Indahwati Rohmah , Basri Hasan , Aini Septi Dariyatul
Published: January 2025

Pemanfaatan media pembelajaran seperti scratch, geogebra, maupun aplikasi lain sangat penting bagi siswa di sekolah dasar, namun demikian belum ditemukan pengembangan media scratch pada elemen bilangan pada fase A. Penelitian ini bertujuan untuk mengembangkan media pembelajaran berbasis scratch elemen bilangan pada fase A pada kurikulum merdeka. Penelitian ini merupakan penelitian Research and Development (R&D) dengan menggunakan model pengembangan ADDIE. Model pengembangan ini mempunyai 5 tahapan, yaitu: Analysis (Analisis), Design (Perancangan), Develompent (Pengembangan), Implementation (Implementasi), Evaluation (Evaluasi). Kami menggunakan lembar validasi, angket dan lembar observasi untuk mengumpulkan data penelitian. Analisis data dilakukan dengan tahapan reduksi, penyajian dan penarikan kesimpulan. Hasil penelitian ini adalah media scratch elemen bilangan fase A pada kurikulum merdeka dinyatakan valid berdasarkan hasil validasi ahli pembelajaran dan media yang diperoleh sebesar 0,83 dan 0,82 dalam kategori validitas tinggi. Sedangkan angket kepraktisan yang diberikan guru dan siswa diperoleh hasil 92% dan 90% dengan kategori sangat praktis.
Prosedur Pelaksanaan Pelatihan Tentang Keselamatan Kerja Dan Lingkungan Di Telkom Witel Denpasar
Authors: Ananda Putra I Gusti Agung , Ni Luh Utari Liana Sari
Published: January 2025

Field Work Practice (PKL) has been carried out at Telkom Witel Denpasar from June 10 to August 9, 2024. This report discusses the procedures for implementing training on occupational safety and the environment at Telkom Witel Denpasar. Procedures related to occupational safety and the environment have been prepared but have not been realized in detail. With the procedures and understanding directly related to emergency response not yet being realized, training is needed to increase employee awareness and comply with existing regulations. The main objective of this activity is to provide understanding and provide knowledge about emergency response procedures related to occupational safety and the environment. With these problems, surveys and interviews were conducted in the field. Then, there is a method of implementation, namely conducting training related to occupational safety and the environment to increase awareness and practice of occupational safety and environmental management in the company. The training that has been carried out includes the use of personal protective equipment, fire and emergency evacuation simulations, emergency response procedures, handling hazardous materials, and waste management. The results of the implementation of this training show a significant increase in employee awareness, understanding, knowledge and the realization of established procedures which are expected to reduce the number of work accidents, reduce negative impacts on the environment and ensure compliance with applicable regulations.
РОЛЬ ВЫСШЕГО ОБРАЗОВАНИЯ В ЭКОНОМИЧЕСКОМ РАЗВИТИИ: ОБЗОР ФАКТОРОВ И ПОДХОДОВ МОДЕЛИРОВАНИЯ
Authors: Тлебалдинова А.С , Смаилова С.С. , Кумаргажанова С.К.
Published: January 2025

На фоне постоянно растущей роли образования в формировании экономических структурных изменений, особенно инженерного образования, данное исследование направлено на изучение влияния высшего образования на экономический рост. Метод анализа включает обзор научных публикаций последних лет, фокусируясь на влиянии образования на экономический рост и моделировании связи между образованием и структурными изменениями в экономике. В анализе рассматриваются факторы, учитываемые в контексте исследования корреляционных связей между образованием, наукой и структурными сдвигами в экономике. В работе также обсуждаются различные методы моделирования взаимосвязи между образованием и экономическим ростом, включая перспективные подходы на основе методов машинного обучения. В результате представленного комплексного обзора текущего состояния исследований в данной области, выявлены направления для будущих исследований.